From da7fc3945fd3470d087e301164a47efc85eda306 Mon Sep 17 00:00:00 2001 From: verena <9377970+vpchung@users.noreply.github.com> Date: Sat, 21 Oct 2023 03:14:55 +0000 Subject: [PATCH 01/14] add `operation` column to `challenge` table --- .../src/main/resources/db/challenges.csv | 560 +++++++++--------- .../db/migration/V1.0.0__create_tables.sql | 1 + 2 files changed, 281 insertions(+), 280 deletions(-) diff --git a/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv b/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv index 6ad642feea..225f327c1e 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv +++ b/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv @@ -1,280 +1,280 @@ -"id","slug","name","headline","description","avatar_url","website_url","status","difficulty","platform","doi","start_date","end_date","created_at","updated_at" -"1","network-topology-and-parameter-inference","Network Topology and Parameter Inference","","Participants are asked to develop and/or apply optimization methods, including the selection of the most informative experiments, to accurately estimate parameters and predict outcomes of perturbations in Systems Biology models.","","https://www.synapse.org/#!Synapse:syn2821735","completed","intermediate","1","","2012-06-01","2012-10-01","2023-06-23 00:00:00","2023-10-19 00:10:08" -"2","breast-cancer-prognosis","Breast Cancer Prognosis","","The goal of the breast cancer prognosis Challenge is to assess the accuracy of computational models designed to predict breast cancer survival, based on clinical information about the patient's tumor as well as genome-wide molecular profiling data including gene expression and copy number profiles.","","https://www.synapse.org/#!Synapse:syn2813426","completed","intermediate","1","","2012-07-12","2012-10-15","2023-06-23 00:00:00","2023-10-17 23:00:12" -"3","phil-bowen-als-prediction-prize4life","Phil Bowen ALS Prediction Prize4Life","","Amyotrophic Lateral Sclerosis (ALS)-also known as Lou Gehrig's disease (in the US) or Motor Neurone disease (outside the US)-is a fatal neurological disease causing death of the nerve cells in the brain and spinal cord which control voluntary muscle movements. This leaves patients struggling with a progressive loss of motor function while leaving cognitive functions intact. Symptoms usually do not manifest until the age of 50 but can start earlier. At any given time, approximately five out of every 100,000 people worldwide suffer from ALS, though there would be a higher prevalence if the disease did not progress so rapidly, leading to the death of the patient. There are no known risk factors for developing ALS other than having a family member who has a hereditary form of the disease, which accounts for about 5-10% of ALS patients. There is also no known cure for ALS. The only FDA-approved drug for the disease is Riluzole, which has been shown to prolong the life span of someone w...","","https://www.synapse.org/#!Synapse:syn2826267","completed","intermediate","1","","2012-06-01","2012-10-01","2023-06-23 00:00:00","2023-10-14 05:38:09" -"4","drug-sensitivity-and-drug-synergy-prediction","Drug Sensitivity and Drug Synergy Prediction","","Development of new cancer therapeutics currently requires a long and protracted process of experimentation and testing. Human cancer cell lines represent a good model to help identify associations between molecular subtypes, pathways, and drug response. In recent years there have been several efforts to generate genomic profiles of collections of cell lines and to determine their response to panels of candidate therapeutic compounds. These data provide the basis for the development of in silico models of sensitivity based either on the unperturbed genetic potential of a cancer cell, or by using perturbation data to incorporate knowledge of actual cell response. Making predictions from either of these data profiles will be beneficial in identifying single and combinatorial chemotherapeutic response in patients. To that end, the present challenge seeks computational methods, derived from the molecular profiling of cell lines both in a static state and in response to perturbation of ...","","https://www.synapse.org/#!Synapse:syn2785778","completed","intermediate","1","","2012-06-01","2012-10-01","2023-06-23 00:00:00","2023-10-19 00:11:48" -"5","niehs-ncats-unc-toxicogenetics","NIEHS-NCATS-UNC Toxicogenetics","","This challenge is designed to build predictive models of cytotoxicity as mediated by exposure to environmental toxicants and drugs. To approach this question, we will provide a dataset containing cytotoxicity estimates as measured in lymphoblastoid cell lines derived from 884 individuals following in vitro exposure to 156 chemical compounds. In subchallenge 1, participants will be asked to model interindividual variability in cytotoxicity based on genomic profiles in order to predict cytotoxicity in unknown individuals. In subchallenge 2, participants will be asked to predict population-level parameters of cytotoxicity across chemicals based on structural attributes of compounds in order to predict median cytotoxicity and mean variance in toxicity for unknown compounds.","","https://www.synapse.org/#!Synapse:syn1761567","completed","intermediate","1","","2013-06-10","2013-09-15","2023-06-23 00:00:00","2023-10-14 05:38:13" -"6","whole-cell-parameter-estimation","Whole-Cell Parameter Estimation","","The goal of this challenge is to explore and compare innovative approaches to parameter estimation of large, heterogeneous computational models. Participants are encouraged to develop and/or apply optimization methods, including the selection of the most informative experiments. The organizers encourage participants to form teams to collaboratively solve the challenge.","","https://www.synapse.org/#!Synapse:syn1876068","completed","intermediate","1","","2013-06-10","2013-09-23","2023-06-23 00:00:00","2023-10-14 05:38:13" -"7","hpn-dream-breast-cancer-network-inference","HPN-DREAM Breast Cancer Network Inference","","The overall goal of the Heritage-DREAM breast cancer network inference challenge is to quickly and effectively advance our ability to infer causal signaling networks and predict protein phosphorylation dynamics in cancer. We provide extensive training data from experiments on four breast cancer cell lines stimulated with various ligands. The data comprise protein abundance time-courses under inhibitor perturbations.","","https://www.synapse.org/#!Synapse:syn1720047","completed","intermediate","1","","2013-06-10","2013-09-16","2023-06-23 00:00:00","2023-10-14 05:38:14" -"8","rheumatoid-arthritis-responder","Rheumatoid Arthritis Responder","","The goal of this project is to use a crowd-based competition framework to develop a validated molecular predictor of anti-TNF response in RA. There is an increasing need for predictors of response to therapy in inflammatory disease driven by the observation that most clinically defined diseases show variable response and the growing availability of alternative therapies. Anti-TNF drugs in Rheumatoid Arthritis represent a prototypical example of this opportunity. A number of studies have tried, over the past decade, to develop a robust predictor of response. We believe the time is right to try a different approach to developing such a biomarker with a crowd-sourced collaborative competition. This is based on DREAM and Sage Bionetworks' experience with running competitions and the availability of new unpublished large-scale data relating to RA treatment response.THIS CHALLENGE RAN FROM FEBRUARY TO OCTOBER 2014 AND IS NOW CLOSED.","","https://www.synapse.org/#!Synapse:syn1734172","completed","intermediate","1","","2014-02-10","2014-06-04","2023-06-23 00:00:00","2023-10-14 05:38:14" -"9","icgc-tcga-dream-mutation-calling","ICGC-TCGA DREAM Mutation Calling","","The ICGC-TCGA DREAM Genomic Mutation Calling Challenge (herein, The Challenge) is an international effort to improve standard methods for identifying cancer-associated mutations and rearrangements in whole-genome sequencing (WGS) data. Leaders of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cancer genomics projects are joining with Sage Bionetworks and IBM-DREAM to initiate this innovative open crowd-sourced Challenge [1-3].","","https://www.synapse.org/#!Synapse:syn312572","completed","intermediate","1","","2013-12-14","2016-04-22","2023-06-23 00:00:00","2023-10-14 05:38:15" -"10","acute-myeloid-leukemia-outcome-prediction","Acute Myeloid Leukemia Outcome Prediction","","The AML Outcome Prediction Challenge provides a unique opportunity to access and interpret a rich dataset for AML patients that includes clinical covariates, select gene mutation status and proteomic data. Capitalizing on a unique AML reverse phase protein array (RPPA) dataset obtained at M.D. Anderson Cancer Center that captures 271 measurements for each patient, participants of the DREAM 9 Challenge will help uncover what drives AML. Outcomes of this Challenge have the potential to be used immediately to tailor therapies for newly diagnosed leukemia patients and to accelerate the development of new drugs for leukemia.","","https://www.synapse.org/#!Synapse:syn2455683","completed","intermediate","1","","2014-06-02","2014-09-15","2023-06-23 00:00:00","2023-10-14 05:38:16" -"11","broad-dream-gene-essentiality-prediction","Broad-DREAM Gene Essentiality Prediction","","The goal of this project is to use a crowd-based competition to develop predictive models that can infer gene dependency scores in cancer cells (genes that are essential to cancer cell viability when suppressed) using features of those cell lines. An additional goal is to find a small set of biomarkers (gene expression, copy number, and mutation features) that can best predict a single gene or set of genes.","","https://www.synapse.org/#!Synapse:syn2384331","completed","intermediate","1","","2014-06-02","2014-09-29","2023-06-23 00:00:00","2023-10-14 05:38:16" -"12","alzheimers-disease-big-data","Alzheimer's Disease Big Data","","The goal of the Alzheimer's Disease Big Data DREAM Challenge #1 (AD#1) was to apply an open science approach to rapidly identify accurate predictive AD biomarkers that can be used by the scientific, industrial and regulatory communities to improve AD diagnosis and treatment. AD#1 will be the first in a series of AD Data Challenges to leverage genetics and brain imaging in combination with cognitive assessments, biomarkers and demographic information from cohorts ranging from cognitively normal to mild cognitively impaired to individuals with AD.","","https://www.synapse.org/#!Synapse:syn2290704","completed","intermediate","1","","2014-06-02","2014-10-17","2023-06-23 00:00:00","2023-10-14 05:38:17" -"13","olfaction-prediction","Olfaction Prediction","","The goal of the DREAM Olfaction Prediction Challenge is to find models that can predict how a molecule smells from its physical and chemical features. A model that allows us to predict a smell from a molecule will provide fundamental insights into how odor chemicals are transformed into a smell percept in the brain. Further, being able to predict how a chemical smells will greatly accelerate the design of new molecules to be used as fragrances. Currently, fragrance chemists synthesize many molecules to obtain a new ingredient, but most of these will not have the desired qualities.","","https://www.synapse.org/#!Synapse:syn2811262","completed","intermediate","1","","2015-01-15","2015-05-01","2023-06-23 00:00:00","2023-10-14 05:38:17" -"14","prostate-cancer","Prostate Cancer","","This challenge will attempt to improve the prediction of survival and toxicity of docetaxel treatment in patients with metastatic castration-resistant prostate cancer (mCRPC). The primary benefit of this Challenge will be to establish new quantitative benchmarks for prognostic modeling in mCRPC, with a potential impact for clinical decision making and ultimately understanding the mechanism of disease progression. Participating teams will be asked to submit predictive models based on clinical variables from the comparator arms of four phase III clinical trials with over 2,000 mCRPC patients treated with first-line docetaxel. The comparator arm of a clinical trial represents the patients that receive a treatment that is considered to be effective. This arm of the clinical trial is used to evaluate the effectiveness of the new therapy being tested.","","https://www.synapse.org/#!Synapse:syn2813558","completed","intermediate","1","","2015-03-16","2015-07-27","2023-06-23 00:00:00","2023-10-14 05:38:18" -"15","als-stratification-prize4life","ALS Stratification Prize4Life","","As illustrated by the overview figure below, (a) Challenge Data includes data from ALS clinical trials and ALS registries. ALS clinical trials consist of patients from clinical trials available open access on the PRO-ACT database and patients from 6 clinical trials not yet added into the database. Data from ALS registries was collected from patients in national ALS registries. (b) Data is divided into three subsets-training data provided to solvers in full, leaderboard, and validation data that is available only to the organizers and is reserved for the scoring of the challenge. (c) The goal of this challenge is then to predict the Clinical Targets, i.e. the disease progression as ALSFRS slope as well as survival. (d) For Building the Models, participants create two algorithms-one that selects features and one that predicts outcomes. To perform predictions, data from a given patient is fed into the selector . The selector selects 6 features and a cluster/model ID (3), e.g. from a...","","https://www.synapse.org/#!Synapse:syn2873386","completed","intermediate","1","","2015-06-22","2015-10-04","2023-06-23 00:00:00","2023-10-14 05:38:19" -"16","astrazeneca-sanger-drug-combination-prediction","AstraZeneca-Sanger Drug Combination Prediction","","To accelerate the understanding of drug synergy, AstraZeneca has partnered with the European Bioinformatic Institute, the Sanger Institute, Sage Bionetworks, and the distributed DREAM community to launch the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. This Challenge is designed to explore fundamental traits that underlie effective combination treatments and synergistic drug behavior using baseline genomic data, i.e. data collected pretreatment. As the basis of the Challenge, AstraZeneca is releasing ~11.5k experimentally tested drug combinations measuring cell viability over 118 drugs and 85 cancer cell lines (primarily colon, lung, and breast), and monotherapy drug response data for each drug and cell line. Moreover, in coordination with the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Sanger Institute, genomic data including gene expression, mutations (whole exome), copy-number alterations, and methylation data will be released into the publ...","","https://www.synapse.org/#!Synapse:syn4231880","completed","intermediate","1","","2015-09-03","2016-03-14","2023-06-23 00:00:00","2023-10-14 05:38:19" -"17","smc-dna-meta","SMC-DNA Meta","","The goal of this Challenge is to identify the most accurate meta-pipeline for somatic mutation detection, and establish the state-of-the-art. The algorithms in this Challenge must use as input mutations predicted by one or more variant callers and output mutation calls associated with cancer. An additional goal is to highlight the complementarity of the calling algorithms and help understand their individual advantages/deficiencies.","","https://www.synapse.org/#!Synapse:syn4588939","completed","intermediate","1","","2015-08-17","2016-04-10","2023-06-23 00:00:00","2023-10-14 05:38:20" -"18","smc-het","SMC-Het","","The ICGC-TCGA DREAM Somatic Mutation Calling-Tumour Heterogeneity Challenge (SMC-Het) is an international effort to improve standard methods for subclonal reconstruction-to quantify and genotype each individual cell population present within a tumor. Leaders of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cancer genomics projects are joining with Sage Bionetworks and IBM-DREAM to initiate this innovative open crowd-sourced Challenge [1-3].","","https://www.synapse.org/#!Synapse:syn2813581","completed","intermediate","1","","2015-11-16","2016-06-30","2023-06-23 00:00:00","2023-10-14 05:38:21" -"19","respiratory-viral","Respiratory Viral","","Respiratory viruses are highly infectious and cause acute illness in millions of people every year. However, there is wide variation in the physiologic response to exposure at the individual level. Some people that are exposed to virus are able to completely avoid infection. Others contract virus but are able to fight it off without exhibiting any symptoms of illness such as coughing, sneezing, sore throat or fever. It is not well understood what characteristics may protect individuals from respiratory viral infection. These individual responses are likely influenced by multiple processes including both the basal state of the human host upon exposure and the dynamics of host immune response in the early hours immediately following exposure. Many of these processes play out in the peripheral blood through activation and recruitment of circulating immune cells. Global gene expression patterns measured in peripheral blood at the time of symptom onset-several days after viral exposure...","","https://www.synapse.org/#!Synapse:syn5647810","completed","intermediate","1","","2016-05-16","2016-09-28","2023-06-23 00:00:00","2023-10-14 05:38:21" -"20","disease-module-identification","Disease Module Identification","","The Disease Module Identification DREAM Challenge is an open community effort to systematically assess module identification methods on a panel of state-of-the-art genomic networks and leverage the “wisdom of crowds” to discover novel modules and pathways underlying complex diseases.","","https://www.synapse.org/#!Synapse:syn6156761","completed","intermediate","1","https://doi.org/10.1038/s41592-019-0509-5","2016-06-24","2016-10-01","2023-06-23 00:00:00","2023-10-16 21:17:48" -"21","encode","ENCODE","","Transcription factors (TFs) are regulatory proteins that bind specific DNA sequence patterns (motifs) in the genome and affect transcription rates of target genes. Binding sites of TFs differ across cell types and experimental conditions. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an experimental method that is commonly used to obtain the genome-wide binding profile of a TF of interest in a specific cell type/condition. However, profiling the binding landscape of every TF in every cell type/condition is infeasible due to constraints on cost, material and effort. Hence, accurate computational prediction of in vivo TF binding sites is critical to complement experimental results.","","https://www.synapse.org/#!Synapse:syn6131484","completed","intermediate","1","","2016-07-07","2017-01-11","2023-06-23 00:00:00","2023-10-14 05:38:26" -"22","idea","Idea","","The DREAM Idea Challenge is designed to collaboratively shape and enable the solution of a question fundamental to improving human health. In the process, all proposals and their evaluation will be made publicly available for the explicit purpose of connecting modelers and experimentalists who want to address the same question. This Wall of Models will enable new collaborations, and help turn every good modeling idea into a success story. It will further serve as a basis for new DREAM challenges.","","https://www.synapse.org/#!Synapse:syn5659209","completed","advanced","1","","2016-06-15","2017-04-30","2023-06-23 00:00:00","2023-10-14 05:38:26" -"23","smc-rna","SMC-RNA","","The ICGC-TCGA DREAM Somatic Mutation Calling-RNA Challenge (SMC-RNA) is an international effort to improve standard methods for identifying cancer-associated rearrangements in RNA sequencing (RNA-seq) data. Leaders of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cancer genomics projects are joining with Sage Bionetworks and IBM-DREAM to initiate this innovative open crowd-sourced Challenge [1-3].","","https://www.synapse.org/#!Synapse:syn2813589","completed","intermediate","1","","2016-06-29","2017-05-02","2023-06-23 00:00:00","2023-10-14 05:38:29" -"24","digital-mammography","Digital Mammography","","The Digital Mammography DREAM Challenge will attempt to improve the predictive accuracy of digital mammography for the early detection of breast cancer. The primary benefit of this Challenge will be to establish new quantitative tools-machine learning, deep learning or other-that can help decrease the recall rate of screening mammography, with a potential impact on shifting the balance of routine breast cancer screening towards more benefit and less harm. Participating teams will be asked to submit predictive models based on over 640,000 de-identified digital mammography images from over 86000 subjects, with corresponding clinical variables.","","https://www.synapse.org/#!Synapse:syn4224222","completed","advanced","1","https://doi.org/10.1001/jamanetworkopen.2020.0265","2016-11-18","2017-05-16","2023-06-23 00:00:00","2023-10-14 05:38:29" -"25","multiple-myeloma","Multiple Myeloma","","Multiple myeloma (MM) is a cancer of the plasma cells in the bone marrow, with about 25,000 newly diagnosed patients per year in the United States alone. The disease's clinical course depends on a complex interplay of clinical traits and molecular characteristics of the plasma cells.1 Since risk-adapted therapy is becoming standard of care, there is an urgent need for a precise risk stratification model to assist in therapeutic decision-making and research. While progress has been made, there remains a significant opportunity to improve patient stratification to optimize treatment and to develop new therapies for high-risk patients. A DREAM Challenge represents a chance not only to integrate available data and analytical approaches to tackle this important problem, but also provides the ability to benchmark potential methods to identify those with the greatest potential to yield patient care benefits in the future.","","https://www.synapse.org/#!Synapse:syn6187098","completed","intermediate","1","","2017-06-30","2017-11-08","2023-06-23 00:00:00","2023-10-14 05:38:31" -"26","ga4gh-dream-workflow-execution","GA4GH-DREAM Workflow Execution","","The highly distributed and disparate nature of genomic and clinical data generated around the world presents an enormous challenge for those scientists who wish to integrate and analyze these data. The sheer volume of data often exceeds the capacity for storage at any one site and prohibits the efficient transfer between sites. To address this challenge, researchers must bring their computation to the data. Numerous groups are now developing technologies and best practice methodologies for running portable and reproducible genomic analysis pipelines as well as tools and APIs for discovering genomic analysis resources. Software development, deployment, and sharing efforts in these groups commonly rely on the use of modular workflow pipelines and virtualization based on Docker containers and related tools.","","https://www.synapse.org/#!Synapse:syn8507133","completed","intermediate","1","","2017-07-21","2017-12-31","2023-06-23 00:00:00","2023-10-14 05:38:31" -"27","parkinsons-disease-digital-biomarker","Parkinson's Disease Digital Biomarker","","The Parkinson's Disease Digital Biomarker DREAM Challenge is a first of it's kind challenge, designed to benchmark methods for the processing of sensor data for development of digital signatures reflective of Parkinson's Disease. Participants will be provided with raw sensor (accelerometer, gyroscope, and magnetometer) time series data recorded during the performance of pre-specified motor tasks, and will be asked to extract data features which are predictive of PD pathology. In contrast to traditional DREAM challenges, this one will focus on feature extraction rather than predictive modeling, and submissions will be evaluated based on their ability to predict disease phenotype using an array of standard machine learning algorithms.","","https://www.synapse.org/#!Synapse:syn8717496","completed","intermediate","1","","2017-07-06","2017-11-10","2023-06-23 00:00:00","2023-10-14 05:38:32" -"28","nci-cptac-proteogenomics","NCI-CPTAC Proteogenomics","","Cancer is driven by aberrations in the genome [1,2], and these alterations manifest themselves largely in the changes in the structure and abundance of proteins, the main functional gene products. Hence, characterization and analyses of alterations in the proteome has the promise to shed light into cancer development and may improve development of both biomarkers and therapeutics. Measuring the proteome is very challenging, but recent rapid technology developments in mass spectrometry are enabling deep proteomics analysis [3]. Multiple initiatives have been launched to take advantage of this development to characterize the proteome of tumours, such as the Clinical Proteomic Tumor Analysis Consortium (CPTAC). These efforts hold the promise to revolutionize cancer research, but this will only be possible if the community develops computational tools powerful enough to extract the most information from the proteome, and to understand the association between genome, transcriptome and ...","","https://www.synapse.org/#!Synapse:syn8228304","completed","intermediate","1","","2017-06-26","2017-11-20","2023-06-23 00:00:00","2023-10-14 05:38:33" -"29","multi-targeting-drug","Multi-Targeting Drug","","The objective of this challenge is to incentivize development of methods for predicting compounds that bind to multiple targets. In particular, methods that are generalizable to multiple prediction problems are sought. To achieve this, participants will be asked to predict 2 separate compounds, each having specific targets to which they should bind, and a list of anti-targets to avoid. Participants should use the same methods to produce answers for questions 1 and 2.","","https://www.synapse.org/#!Synapse:syn8404040","completed","intermediate","1","","2017-10-05","2018-02-26","2023-06-23 00:00:00","2023-10-14 05:38:33" -"30","single-cell-transcriptomics","Single Cell Transcriptomics","","In this Challenge on Single-Cell Transcriptomics, participants will reconstruct the location of single cells in the Drosophila embryo using single-cell transcriptomic data. Data will be made available in late August and participating challenge teams can work on the data and submit their results previous to the DREAM Conference. The best performers will be announced at the DREAM conference on Dec 8.","","https://www.synapse.org/#!Synapse:syn15665609","completed","intermediate","1","","2018-09-04","2018-11-21","2023-06-23 00:00:00","2023-10-14 05:38:34" -"31","idg-drug-kinase-binding","IDG Drug-Kinase Binding","","This IDG-DREAM Drug-Kinase Binding Prediction Challenge seeks to evaluate the power of statistical and machine learning models as a systematic and cost-effective means for catalyzing compound-target interaction mapping efforts by prioritizing most potent interactions for further experimental evaluation. The Challenge will focus on kinase inhibitors, due to their clinical importance [2], and will be implemented in a screening-based, pre-competitive drug discovery project in collaboration with theIlluminating the Druggable Genome (IDG) Kinase-focused Data and Resource Generation Center, consortium, with the aim to establish kinome-wide target profiles of small-molecule agents, with the goal of extending the druggability of the human kinome space.","","https://www.synapse.org/#!Synapse:syn15667962","completed","intermediate","1","","2018-10-01","2019-04-18","2023-06-23 00:00:00","2023-10-14 05:38:35" -"32","malaria","Malaria","","The Malaria DREAM Challenge is open to anyone interested in contributing to the development of computational models that address important problems in advancing the fight against malaria. The overall goal of the first Malaria DREAM Challenge is to predict Artemisinin (Art) drug resistance level of a test set of malaria parasites using their in vitro transcription data and a training set consisting of published in vivo and unpublished in vitrotranscriptomes. The in vivodataset consists of ~1000 transcription samples from various geographic locations covering a wide range of life cycles and resistance levels, with other accompanying data such as patient age, geographic location, Art combination therapy used, etc [Mok et al (2015) Science]. The in vitro transcription dataset consists of 55 isolates, with transcription collected at two timepoints (6 and 24 hours post-invasion), in the absence or presence of an Art perturbation, for two biological replicates using a custom microarray a...","","https://www.synapse.org/#!Synapse:syn16924919","completed","intermediate","1","","2019-04-30","2019-08-15","2023-06-23 00:00:00","2023-10-14 05:38:35" -"33","preterm-birth-prediction-transcriptomics","Preterm Birth Prediction - Transcriptomics","","A basic need in pregnancy care is to establish gestational age, and inaccurate estimates may lead to unnecessary interventions and sub-optimal patient management. Current approaches to establish gestational age rely on patient's recollection of her last menstrual period and/or ultrasound, with the latter being not only costly but also less accurate if not performed during the first trimester of pregnancy. Therefore development of an inexpensive and accurate molecular clock of pregnancy would be of benefit to patients and health care systems. Participants in sub-challenge 1 (Prediction of gestational age) will be given whole blood gene topic_3170 collected from pregnant women to develop prediction models for the gestational age at blood draw. Another challenge in obstetrics, in both low and high-income countries, is identification and treatment of women at risk of developing the ‘great obstetrical syndromes‘. Of these, preterm birth (PTB), defined as giving birth prior to completio...","","https://www.synapse.org/#!Synapse:syn18380862","completed","good_for_beginners","1","","2019-05-04","2019-12-05","2023-06-23 00:00:00","2023-10-14 05:38:36" -"34","single-cell-signaling-in-breast-cancer","Single-Cell Signaling in Breast Cancer","","Signaling underlines nearly every cellular event. Individual cells, even if genetically identical, respond to perturbation in different ways. This underscores the relevance of cellular heterogeneity, in particular in how cells respond to drugs. This is of high relevance since the fact that a subset of cells do not respond (or only weakly) to drugs can render this drug an ineffective treatment. In spite of its relevance to many diseases, comprehensive studies on the heterogeneous signaling in single cells are still lacking. We have generated the, to our knowledge, currently largest single cell signaling dataset on a panel of 67 well-characterized breast cancer cell lines by mass cytometry (3'015 conditions, ~80 mio single cells, 38 markers; Bandura et al. 2009; Bendall et al., 2011; Bodenmiller et al., 2012; Lun et al., 2017; Lun et al., 2019). These cell lines are, among others, also characterized at the genomic, transcriptomic, and proteomic level (Marcotte et al., 2016). We ask ...","","https://www.synapse.org/#!Synapse:syn20366914","completed","intermediate","1","","2018-08-20","2019-11-15","2023-06-23 00:00:00","2023-10-14 05:38:37" -"35","ehr-dream-challenge-patient-mortality-prediction","EHR DREAM Challenge - Patient Mortality Prediction","","The recent advent of new CRISPR-based molecular tools allows the reconstruction of cell lineages based on the phylogenetical analysis of DNA mutations induced by CRISPR during development and promises to solve the lineage of complex model organisms at single-cell resolution (see image from McKenna et al Science 2016). To date, however, no lineage reconstruction algorithms have been rigorously examined for their performance/robustness across diverse molecular tools, datasets, and number of cells/size of lineage trees. It also remains unclear whether new Machine-Learning algorithms that go beyond the classical ones developed for reconstructing phylogenetic trees, could consistently reconstruct cell lineages to a high degree of accuracy. The challenge-a partnership between The Allen Institute and DREAM-will comprise 3 subchallenges that consist of reconstructing cell lineage trees of different sizes and nature. In subchallenge 1, participants will be given experimental molecular data...","","https://www.synapse.org/#!Synapse:syn18405991","completed","intermediate","1","https://doi.org/10.1093/jamia/ocad159","2019-09-09","2020-01-23","2023-06-23 00:00:00","2023-10-19 00:12:33" -"36","allen-institute-cell-lineage-reconstruction","Allen Institute Cell Lineage Reconstruction","","The recent advent of new CRISPR-based molecular tools allows the reconstruction of cell lineages based on the phylogenetical analysis of DNA mutations induced by CRISPR during development and promises to solve the lineage of complex model organisms at single-cell resolution. To date, however, no lineage reconstruction algorithms have been rigorously examined for their performance/robustness across diverse molecular tools, datasets, and number of cells/size of lineage trees. It also remains unclear whether new Machine-Learning algorithms that go beyond the classical ones developed for reconstructing phylogenetic trees, could consistently reconstruct cell lineages to a high degree of accuracy. The challenge-a partnership between The Allen Institute and DREAM-will comprise 3 subchallenges that consist of reconstructing cell lineage trees of different sizes and nature. In subchallenge 1, participants will be given experimental molecular data to reconstruct in vitro cell lineages of l...","","https://www.synapse.org/#!Synapse:syn20692755","completed","intermediate","1","","2019-10-15","2020-02-06","2023-06-23 00:00:00","2023-10-19 00:12:46" -"37","tumor-deconvolution","Tumor Deconvolution","","The extent of stromal and immune cell infiltration within solid tumors has prognostic and predictive significance. Unfortunately, expression profiling of tumors has, until very recently, largely been undertaken using bulk techniques (e.g., microarray and RNA-seq). Unlike single-cell methods (e.g., single-cell RNA-seq, FACS, mass cytometry, or immunohistochemistry), bulk approaches average expression across all cells (cancer, stromal, and immune) within the sample and, hence, do not directly quantitate tumor infiltration. This information can be recovered by computational tumor deconvolution methods, which would thus allow interrogation of immune subpopulations across the large collection of public bulk topic_3170sets. The goal of this Challenge is to evaluate the ability of computational methods to deconvolve bulk topic_3170, reflecting a mixture of cell types, into individual immune components. Methods will be assessed based on in vitro and in silico admixtures specifically gener...","","https://www.synapse.org/#!Synapse:syn15589870","completed","intermediate","1","","2019-06-26","2020-04-30","2023-06-23 00:00:00","2023-10-14 05:38:39" -"38","ctd2-pancancer-drug-activity","CTD2 Pancancer Drug Activity","","Over the last two years, the Columbia CTD2 Center developed PANACEA (Pancancer Analysis of Chemical Entity Activity), a comprehensive repertoire of dose response curves and molecular profiles representative of cellular responses to drug perturbations. PANACEA covers a broad spectrum of cellular contexts representative of poor outcome malignancies, including rare ones such as GIST sarcoma and gastroenteropancreatic neuroendocrine tumors (GEP-NETs). PANACEA is uniquely suited to support DREAM Challenges related to the elucidation of drug mechanism of action (MOA), drug sensitivity, and drug synergy. The goal of the CTD2 Pancancer Drug Activity DREAM Challenge is to foster the development and benchmarking of algorithms to predict targets of chemotherapeutic compounds from post-treatment transcriptional data.","","https://www.synapse.org/#!Synapse:syn20968331","completed","good_for_beginners","1","","2019-12-02","2020-02-13","2023-06-23 00:00:00","2023-10-20 23:11:10" -"39","ctd2-beataml","CTD2 BeatAML","","In the era of precision medicine, AML patients have few therapeutic options, with “7 + 3” induction chemotherapy having been the standard for decades (Bertoli et al. 2017). While several agents targeting the myeloid marker CD33 or alterations in FLT3 or IDH2 have demonstrated efficacy in patients (Wei and Tiong 2017), responses are uncertain in some populations (Castaigne et al. 2012) and relapse remains prevalent (Stone et al. 2017). These drugs highlight both the promise of targeted therapies in AML and the urgent need for additional treatment options that are tailored to more refined patient subpopulations in order to achieve durable responses. The BeatAML initiative was launched as a comprehensive study of the relationship between molecular alterations and ex-vivo drug sensitivity in patients with AML. One of the primary goals of this multi-center study was to develop a discovery cohort that could yield new drug target hypotheses and predictive biomarkers of therapeutic respon...","","https://www.synapse.org/#!Synapse:syn20940518","completed","good_for_beginners","1","","2019-12-19","2020-04-28","2023-06-23 00:00:00","2023-10-14 05:38:42" -"40","metadata-automation","Metadata Automation","","The Cancer Research Data Commons (CRDC) will collate data across diverse groups of cancer researchers, each collecting biomedical data in different formats. This means the data must be retrospectively harmonized and transformed to enable this data to be submitted. In addition, to be findable by the broader scientific community, coherent information (metadata) is necessary about the data fields and values. Coherent metadata annotation of the data fields and their values can enable computational data transformation, query, and analysis. Creation of this type of descriptive metadata can require biomedical expertise to determine the best annotations and thus is a time-consuming and manual task which is both an obstacle and a bottleneck in data sharing and submissions. Goal-Using structured biomedical data files, challenge participants will develop tools to semi-automate annotation of metadata fields and values, using available research data annotations (e.g. caDSR CDEs) as well as es...","","https://www.synapse.org/#!Synapse:syn18065891","completed","intermediate","1","","2020-01-14","2020-06-02","2023-06-23 00:00:00","2023-10-14 05:38:42" -"41","automated-scoring-of-radiographic-joint-damage","Automated Scoring of Radiographic Joint Damage","","The purpose of the RA2-DREAM Challenge is to develop an automated method to quickly and accurately quantify the degree of joint damage associated with rheumatoid arthritis (RA). Based on radiographs of the hands and feet, a novel, automated scoring method could be applied broadly for patient care and research. We challenge participants to develop algorithms to automatically assess joint space narrowing and erosions using a large set of existing radiographs with damage scores generated by visual assessment of images by trained readers using standard protocols. The end result will be a generalizable, publicly available, automated method to generate accurate, reproducible and unbiased RA damage scores to replace the current tedious, expensive, and non-scalable method of scoring by human visual inspection.","","https://www.synapse.org/#!Synapse:syn20545111","completed","intermediate","1","","2019-11-04","2020-05-21","2023-06-23 00:00:00","2023-10-18 00:38:55" -"42","beat-pd","BEAT-PD","","Recent advances in mobile health have demonstrated great potential to leverage sensor-based technologies for quantitative, remote monitoring of health and disease-particularly for diseases affecting motor function such as Parkinson's disease. Such approaches have been rolled out using research-grade wearable sensors and, increasingly, through the use of smartphones and consumer wearables, such as smart watches and fitness trackers. These devices not only provide the ability to measure much more detailed disease phenotypes but also provide the ability to follow patients longitudinally with much higher frequency than is possible through clinical exams. However, the conversion of sensor-based data streams into digital biomarkers is complex and no methodological standards have yet evolved to guide this process. Parkinson's disease (PD) is a neurodegenerative disease that primarily affects the motor system but also exhibits other symptoms. Typical motor symptoms of the disease include...","","https://www.synapse.org/#!Synapse:syn20825169","completed","intermediate","1","","2020-01-13","2020-05-13","2023-06-23 00:00:00","2023-10-14 05:38:45" -"43","ctd2-pancancer-chemosensitivity","CTD2 Pancancer Chemosensitivity","","Over the last two years, the Columbia CTD2 Center developed PANACEA (Pancancer Analysis of Chemical Entity Activity), a comprehensive repertoire of dose response curves and molecular profiles representative of cellular responses to drug perturbations. PANACEA covers a broad spectrum of cellular contexts representative of poor outcome malignancies, including rare ones such as GIST sarcoma and gastroenteropancreatic neuroendocrine tumors (GEP-NETs). PANACEA is uniquely suited to support DREAM Challenges related to the elucidation of drug mechanism of action (MOA), drug sensitivity, and drug synergy. The goal of this Challenge is to foster development and benchmarking of algorithms to predict the sensitivity, as measured by the area under the dose-response curve, of a cell line to a compound based on the baseline transcriptional profiles of the cell line. The drug perturbational RNAseq profiles of 11 cell lines for 30 chosen compounds will be provided to challenge participants, with...","","https://www.synapse.org/#!Synapse:syn21763589","completed","good_for_beginners","1","","2020-04-28","2020-07-27","2023-06-23 00:00:00","2023-10-14 05:38:45" -"44","ehr-dream-challenge-covid-19","EHR DREAM Challenge-COVID-19","","The rapid rise of COVID-19 has challenged healthcare globally. The underlying risks and outcomes of infection are still incompletely characterized even as the world surpasses 4 million infections. Due to the importance and emergent need for better understanding of the condition and the development of patient specific clinical risk scores and early warning tools, we have developed a platform to support testing analytic and machine learning hypotheses on clinical data without data sharing as a platform to rapidly discover and implement approaches for care. We have previously applied this approach in the successful EHR DREAM Challenge focusing on Patient Mortality Prediction with UW Medicine. We have the goal of incorporating machine learning and predictive algorithms into clinical care and COVID-19 is an important and highly urgent challenge. In our first iteration, we will facilitate understanding risk factors that lead to a positive test utilizing electronic health recorded dat...","","https://www.synapse.org/#!Synapse:syn21849255","completed","intermediate","1","https://doi.org/10.1001/jamanetworkopen.2021.24946","2020-04-30","2021-07-01","2023-06-23 00:00:00","2023-10-14 05:38:46" -"45","anti-pd1-response-prediction","Anti-PD1 Response Prediction","","While durable responses and prolonged survival have been demonstrated in some lung cancer patients treated with immuno-oncology (I-O) anti-PD-1 therapy, there remains a need to improve the ability to predict which patients are more likely to receive benefit from treatment with I-O. The goal of this challenge is to leverage clinical and biomarker data to develop predictive models of response to I-O therapy in lung cancer and ultimately gain insights that may facilitate potential novel monotherapies or combinations with I-O.","","https://www.synapse.org/#!Synapse:syn18404605","completed","intermediate","1","","2020-11-17","2021-02-25","2023-06-23 00:00:00","2023-10-14 05:38:47" -"46","brats-2021-challenge","BraTS 2021 Challenge","","Glioblastoma, and diffuse astrocytic glioma with molecular features of glioblastoma (WHO Grade 4 astrocytoma), are the most common and aggressive malignant primary tumor of the central nervous system in adults, with extreme intrinsic heterogeneity in appearance, shape, and histology. Glioblastoma patients have very poor prognosis, and the current standard of care treatment comprises surgery, followed by radiotherapy and chemotherapy. The International Brain Tumor Segmentation (BraTS) Challenges —which have been running since 2012— assess state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans.","","https://www.synapse.org/#!Synapse:syn25829067","completed","advanced","1","","2021-07-07","2021-10-15","2023-06-23 00:00:00","2023-10-14 05:38:48" -"47","cancer-data-registry-nlp","Cancer Data Registry NLP","","A critical bottleneck in translational and clinical research is access to large volumes of high-quality clinical data. While structured data exist in medical EHR systems, a large portion of patient information including patient status, treatments, and outcomes is contained in unstructured text fields. Research in Natural Language Processing (NLP) aims to unlock this hidden and often inaccessible information. However, numerous challenges exist in developing and evaluating NLP methods, much of it centered on having “gold-standard” metrics for evaluation, and access to data that may contain personal health information (PHI). This DREAM Challenge will focus on the development and evaluation of of NLP algorithms that can improve clinical trial matching and recruitment.","","https://www.synapse.org/#!Synapse:syn18361217","upcoming","intermediate","1","","\N","\N","2023-06-23 00:00:00","2023-10-14 05:38:49" -"48","barda-community-challenge-pediatric-covid-19-data-challenge","BARDA Community Challenge - Pediatric COVID-19 Data Challenge","","While most children with COVID-19 are asymptomatic or have mild symptoms, healthcare providers have difficulty determining which among their pediatric patients will progress to moderate or severe COVID-19 early in the progression. Some of these patients develop multisystem inflammatory syndrome in children (MIS-C), a life-threatening inflammation of organs and tissues. Methods to distinguish children at risk for severe COVID-19 complications, including conditions such as MIS-C, are needed for earlier interventions to improve pediatric patient outcomes. Multiple HHS divisions are coming together for a data challenge competition that will leverage de-identified electronic health record data to develop, train and validate computational models that can predict severe COVID-19 complications in children, equipping healthcare providers with the information and tools they need to identify pediatric patients at risk.","","https://www.synapse.org/#!Synapse:syn25875374/wiki/611225","completed","intermediate","1","","2021-08-19","2021-12-17","2023-06-23 00:00:00","2023-10-14 05:38:50" -"49","brats-continuous-evaluation","BraTS Continuous Evaluation","","Brain tumors are among the deadliest types of cancer. Specifically, glioblastoma, and diffuse astrocytic glioma with molecular features of glioblastoma (WHO Grade 4 astrocytoma), are the most common and aggressive malignant primary tumor of the central nervous system in adults, with extreme intrinsic heterogeneity in appearance, shape, and histology, with a median survival of approximately 15 months. Brain tumors in general are challenging to diagnose, hard to treat and inherently resistant to conventional therapy because of the challenges in delivering drugs to the brain, as well as the inherent high heterogeneity of these tumors in their radiographic, morphologic, and molecular landscapes. Years of extensive research to improve diagnosis, characterization, and treatment have decreased mortality rates in the U.S by 7% over the past 30 years. Although modest, these research innovations have not translated to improvements in survival for adults and children in low-and middle-income...","","https://www.synapse.org/brats_ce","completed","advanced","1","","2022-01-01","\N","2023-06-23 00:00:00","2023-10-14 05:38:51" -"50","fets-2022","FeTS 2022","","FeTS 2022 focuses on benchmarking methods for federated learning (FL), and particularly i) weight aggregation methods for federated training, and ii) algorithmic generalizability on out-of-sample data based on federated evaluation. In line with its last instance (FeTS 2021-the 1st FL challenge ever organized), FeTS 2022 targets the task of brain tumor segmentation and builds upon i) the centralized dataset of >8,000 clinically-acquired multi-institutional MRI scans (from the RSNA-ASNR-MICCAI BraTS 2021 challenge) with their real-world partitioning, and ii) the collaborative network of remote independent institutions included in a real-world federation. Participants are welcome to compete in either of the two challenge tasks- Task 1 (“Federated Training”) seeks effective weight aggregation methods for the creation of a consensus model given a pre-defined segmentation algorithm for training, while also (optionally) accounting for network outages. Task 2 (“Federated Evaluation”) see...","","https://www.synapse.org/#!Synapse:syn28546456/wiki/617093","completed","advanced","1","","2022-04-08","2022-08-15","2023-06-23 00:00:00","2023-10-18 00:36:14" -"51","random-promotor","Random Promotor","","Decoding how gene expression is regulated is critical to understanding disease. Regulatory DNA is decoded by the cell in a process termed “cis-regulatory logic”, where proteins called Transcription Factors (TFs) bind to specific DNA sequences within the genome and work together to produce as output a level of gene expression for downstream adjacent genes. This process is exceedingly complex to model as a large number of parameters is needed to fully describe the process (see Rationale, de Boer et al. 2020; Zeitingler J. 2020). Understanding the cis-regulatory logic of the human genome is an important goal and would provide insight into the origins of many diseases. However, learning models from human data is challenging due to limitations in the diversity of sequences present within the human genome (e.g. extensive repetitive DNA), the vast number of cell types that differ in how they interpret regulatory DNA, limited reporter assay data, and substantial technical biases present ...","","https://www.synapse.org/#!Synapse:syn28469146/wiki/617075","completed","intermediate","1","","2022-05-02","2022-08-07","2023-06-23 00:00:00","2023-10-14 05:38:53" -"52","preterm-birth-prediction-microbiome","Preterm Birth Prediction - Microbiome","","Globally, about 11% of infants every year are born preterm, defined as birth prior to 37 weeks of gestation, totaling nearly 15 million births.(5) In addition to the emotional and financial toll on families, preterm births have higher rates of neonatal death, nearly 1 million deaths each year, and long-term health consequences for some children. Infants born preterm are at risk for a variety of adverse outcomes, such as respiratory illnesses, cerebral palsy, infections, and blindness, with infants born very preterm (i.e., before 32 weeks) at increased risk of these conditions.(6) The ability to accurately predict which women are at a higher risk for preterm birth would help healthcare providers to treat in a timely manner those at higher risk of delivering preterm. Currently available treatments for pregnant women at risk of preterm delivery include corticosteroids for fetal maturation and magnesium sulfate provided prior to 32 weeks to prevent cerebral palsy.(7) There are several...","","https://www.synapse.org/#!Synapse:syn26133770/wiki/612541","completed","advanced","1","","2022-07-19","2022-09-16","2023-06-23 00:00:00","2023-10-14 05:38:54" -"53","finrisk","FINRISK - Heart Failure and Microbiome","","Cardiovascular diseases are the leading cause of death both in men and women worldwide. Heart failure (HF) is the most common form of heart disease, characterised by the heart's inability to pump a sufficient supply of blood to meet the needs of the body. The lifetime risk of developing HF is roughly 20%, yet, it remains difficult to diagnose due to its and a lack of agreement of diagnostic criteria. As the diagnosis of HF is dependent on ascertainment of clinical histories and appropriate screening of symptomatic individuals, identifying those at risk of HF is essential. This DREAM challenge focuses on the prediction of HF using a combination of gut microbiome and clinical variables. This challenge is designed to predict incident risk for heart failure in a large human population study of Finnish adults, FINRISK 2002 (Borodulin et al., 2018). The FINRISK study has been conducted in Finland to investigate the risk factors for cardiovascular disease every 5 years since 1972. A rand...","","https://www.synapse.org/#!Synapse:syn27130803/wiki/616705","completed","advanced","1","","2022-09-20","2023-01-30","2023-06-23 00:00:00","2023-10-16 21:19:55" -"54","scrna-seq-and-scatac-seq-data-analysis","scRNA-seq and scATAC-seq Data Analysis","","Understanding transcriptional regulation at individual cell resolution is fundamental to understanding complex biological systems such as tissues and organs. Emerging high-throughput sequencing technologies now allow for transcript quantification and chromatin accessibility at the single cell level. These technologies present unique challenges due to inherent data sparsity. Proper signal correction is key to accurate gene expression quantification via scRNA-seq, which propagates into downstream analyses such as differential gene expression analysis and cell-type identification. In the even more sparse scATAC-seq data, the correct identification of informative features is key to assessing cell heterogeneity at the chromatin level. The aims of this challenge will be two-fold- 1) To evaluate computational methods for signal correction and peak identification in scRNA-seq and scATAC-seq, respectively; 2) To assess the impact of these methods on downstream analysis","","https://www.synapse.org/#!Synapse:syn26720920/wiki/615338","completed","advanced","1","","2022-11-29","2023-02-08","2023-06-23 00:00:00","2023-10-14 05:38:56" -"55","cough-diagnostic-algorithm-for-tuberculosis","COugh Diagnostic Algorithm for Tuberculosis","","Tuberculosis (TB), a communicable disease caused by Mycobacterium tuberculosis, is a major cause of ill health and one of the leading causes of death worldwide. Until the COVID-19 pandemic, TB was the leading cause of death from a single infectious agent, ranking even above HIV/AIDS. In 2020, an estimated 9.9 million people fell ill with TB and 1.3 million died of TB worldwide. However, approximately 40% of people with TB were not diagnosed or reported to public health authorities because of challenges in accessing health facilities or failure to be tested or treated when they do. The development of low-cost, non-invasive digital screening tools may improve some of the gaps in diagnosis. As cough is a common symptom of TB, it has the potential to be used as a biomarker for diagnosis of disease. Several previous studies have demonstrated the potential for cough sounds to be used to screen for TB[1-3], though these were typically done in small samples or limited settings. Further de...","","https://www.synapse.org/#!Synapse:syn31472953/wiki/617828","completed","advanced","1","","2022-10-16","2023-02-13","2023-06-23 00:00:00","2023-10-14 05:38:57" -"56","nih-long-covid-computational-challenge","NIH Long COVID Computational Challenge","","The overall prevalence of post-acute sequelae of SARS-CoV-2 (PASC) is currently unknown, but there is growing evidence that more than half of COVID-19 survivors experience at least one symptom of PASC/Long COVID at six months after recovery of the acute illness. Reports also reflect an underlying heterogeneity of symptoms, multi-organ involvement, and persistence of PASC/Long COVID in some patients. Research is ongoing to understand prevalence, duration, and clinical outcomes of PASC/Long COVID. Symptoms of fatigue, cognitive impairment, shortness of breath, and cardiac damage, among others, have been observed in patients who had only mild initial disease. The breadth and complexity of data created in today's health care encounters require advanced analytics to extract meaning from longitudinal data on symptoms, laboratory results, images, functional tests, genomics, mobile health/wearable devices, written notes, electronic health records (EHR), and other relevant data types. Adva...","","https://www.synapse.org/#!Synapse:syn33576900/wiki/618451","completed","intermediate","1","","2022-08-25","2022-12-15","2023-06-23 00:00:00","2023-10-18 00:39:03" -"57","bridge2ai","Bridge2AI","What makes a good color palette?","What makes a good color palette?","","","upcoming","good_for_beginners","1","","\N","\N","2023-06-23 00:00:00","2023-10-14 05:38:58" -"58","rare-x-open-data-science","RARE-X Open Data Science","","The Xcelerate RARE-A Rare Disease Open Science Data Challenge is bringing together researchers and data scientists in a collaborative and competitive environment to make the best use of patient-provided data to solve big unknowns in healthcare. The Challenge will launch to researchers in late May 2023, focused on rare pediatric neurodevelopmental diseases.","","https://www.synapse.org/#!Synapse:syn51198355/wiki/621435","completed","intermediate","1","","2023-05-17","2023-08-16","2023-06-23 00:00:00","2023-10-14 05:38:59" -"59","cagi5-regulation-saturation","CAGI5: Regulation saturation","","17,500 single nucleotide variants (SNVs) in 5 human disease associated enhancers (including IRF4, IRF6, MYC, SORT1) and 9 promoters (including TERT, LDLR, F9, HBG1) were assessed in a saturation mutagenesis massively parallel reporter assay. Promoters were cloned into a plasmid upstream of a tagged reporter construct, and reporter expression was measured relative to the plasmid DNA to determine the impact of promoter variants. Enhancers were placed upstream of a minimal promoter and assayed similarly. The challenge is to predict the functional effects of these variants in the regulatory regions as measured from the reporter expression.","","https://genomeinterpretation.org/CAGI5-regulation-saturation.html","completed","intermediate","2","","2018-01-04","2018-05-03","2023-06-23 00:00:00","2023-10-18 15:36:06" -"60","cagi5-calm1","CAGI5: CALM1","","Calmodulin is a calcium-sensing protein that modulates the activity of a large number of proteins in the cell. It is involved in many cellular processes, and is especially important for neuron and muscle cell function. Variants that affect calmodulin function have been found to be causally associated with cardiac arrhythmias. A large library of calmodulin missense variants was assessed with respect to their effects on protein function using a high-throughput yeast complementation assay. The challenge is to predict the functional effects of these calmodulin variants on competitive growth in a high-throughput yeast complementation assay.","","https://genomeinterpretation.org/CAGI5-calm1.html","completed","intermediate","2","","2017-10-21","2017-12-20","2023-06-23 00:00:00","2023-10-18 15:35:49" -"61","cagi5-pcm1","CAGI5: PCM1","","The PCM1 (Pericentriolar Material 1) gene is a component of centriolar satellites occurring around centrosomes in vertebrate cells. Several studies have implicated PCM1 variants as a risk factor for schizophrenia. Ventricular enlargement is one of the most consistent abnormal structural brain findings in schizophrenia Therefore 38 transgenic human PCM1 missense mutations implicated in schizophrenia were assayed in a zebrafish model to determine their impact on the posterior ventricle area. The challenge is to predict whether variants implicated in schizophrenia impact zebrafish ventricular area.","","https://genomeinterpretation.org/CAGI5-pcm1.html","completed","intermediate","2","","2017-11-09","2018-04-19","2023-06-23 00:00:00","2023-10-18 15:35:49" -"62","cagi5-frataxin","CAGI5: Frataxin","","Fraxatin is a highly-conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Humans with a frataxin deficiency have the cardio-and neurodegenerative disorder Friedreich's ataxia. A library of eight missense variants was assessed by near and far-UV circular dichroism and intrinsic fluorescence spectra to determine thermodynamic stability at different concentration of denaturant. These were used to calculate a ΔΔGH20 value, the difference in unfolding free energy (ΔGH20) between the mutant and wild-type proteins for each variant. The challenge is to predict ΔΔGH20 for each frataxin variant.","","https://genomeinterpretation.org/CAGI5-frataxin.html","completed","intermediate","2","","2017-11-30","2018-04-18","2023-06-23 00:00:00","2023-10-18 15:35:50" -"63","cagi5-tpmt","CAGI5: TPMT and p10","","The gene p10 encodes for PTEN (Phosphatase and TEnsin Homolog), an important secondary messenger molecule promoting cell growth and survival through signaling cascades including those controlled by AKT and mTOR. Thiopurine S-methyl transferase (TPMT) is a key enzyme involved in the metabolism of thiopurine drugs and functions by catalyzing the S-methylation of aromatic and heterocyclic sulfhydryl groups. A library of thousands of PTEN and TPMT mutations was assessed to measure the stability of the variant protein using a multiplexed variant stability profiling (VSP) assay, which detects the presence of EGFP fused to the mutated PTEN and TPMT protein respectively. The stability of the variant protein dictates the abundance of the fusion protein and thus the EGFP level of the cell. The challenge is to predict the effect of each variant on TPMT and/or PTEN protein stability.","","https://genomeinterpretation.org/CAGI5-tpmt.html","completed","intermediate","2","","2017-11-30","2017-12-01","2023-06-23 00:00:00","2023-10-14 05:39:03" -"64","cagi5-annotate-all-missense","CAGI5: Annotate all nonsynonymous variants","","dbNSFP describes 810,848,49 possible protein-altering variants in the human genome. The challenge is to predict the functional effect of every such variant. For the vast majority of these missense variants, the functional impact is not currently known, but experimental and clinical evidence are accruing rapidly. Rather than drawing upon a single discrete dataset as typical with CAGI, predictions will be assessed by comparing with experimental or clinical annotations made available after the prediction submission date, on an ongoing basis. if predictors assent, predictions will also incorporated into dbNSFP.","","https://genomeinterpretation.org/CAGI5-annotate-all-missense.html","completed","intermediate","2","","2017-11-30","2018-05-09","2023-06-23 00:00:00","2023-10-14 05:39:04" -"65","cagi5-gaa","CAGI5: GAA","","Acid alpha-glucosidase (GAA) is a lysosomal alpha-glucosidase. Some mutations in GAA cause a rare disorder, Pompe disease, (Glycogen Storage Disease II). Rare GAA missense variants found in a human population sample have been assayed for enzymatic activity in transfected cell lysates. The assessment of this challenge will include evaluations that recognize novelty of approach. The challenge is to predict the fractional enzyme activity of each mutant protein compared to the wild-type enzyme.","","https://genomeinterpretation.org/CAGI5-gaa.html","completed","intermediate","2","","2017-11-09","2018-04-25","2023-06-23 00:00:00","2023-10-14 05:39:04" -"66","cagi5-chek2","CAGI5: CHEK2","","Variants in the CHEK2 gene are associated with breast cancer. This challenge includes CHEK2 gene variants from approximately 1200 Latino breast cancer cases and 1200 ethnically matched controls. This challenge is to estimate the probability of each gene variant occurring in an individual from the cancer affected cohort.","","https://genomeinterpretation.org/CAGI5-chek2.html","completed","intermediate","2","","2017-12-20","2018-04-24","2023-06-23 00:00:00","2023-10-14 05:39:07" -"67","cagi5-enigma","CAGI5: ENIGMA","","Breast cancer is the most prevalent cancer among women worldwide. The association between germline mutations in the BRCA1 and BRCA2 genes and the development of cancer has been well established. The most common high-risk mutations associated with breast cancer are those in the autosomal dominant breast cancer genes 1 and 2 (BRCA1 and BRCA2). Mutations in these genes are found in 1-3% of breast cancer cases. The challenge is to predict which variants are associated with increased risk for breast cancer.","","https://genomeinterpretation.org/CAGI5-enigma.html","completed","intermediate","2","","2017-12-20","2018-05-01","2023-06-23 00:00:00","2023-10-14 05:39:08" -"68","cagi5-mapsy","CAGI5: MaPSy","","The Massively Parallel Splicing Assay (MaPSy) approach was used to screen 797 reported exonic disease mutations using a mini-gene system, assaying both in vivo via transfection in tissue culture, and in vitro via incubation in cell nuclear extract. The challenge is to predict the degree to which a given variant causes changes in splicing.","","https://genomeinterpretation.org/CAGI5-mapsy.html","completed","intermediate","2","","2017-11-29","2018-05-07","2023-06-23 00:00:00","2023-10-14 05:39:08" -"69","cagi5-vex-seq","CAGI5: Vex-seq","","A barcoding approach called Variant exon sequencing (Vex-seq) was applied to assess effect of 2,059 natural single nucleotide variants and short indels on splicing of a globin mini-gene construct transfected into HepG2 cells. This is reported as ΔΨ (delta PSI, or Percent Spliced In), between the variant Ψand the reference Ψ. The challenge is to predict ΔΨ for each variant.","","https://genomeinterpretation.org/CAGI5-vex-seq.html","completed","intermediate","2","","2017-12-14","2018-05-02","2023-06-23 00:00:00","2023-10-16 17:51:58" -"70","cagi5-sickkids5","CAGI5: SickKids clinical genomes","","This challenge involves 30 children with suspected genetic disorders who were referred for clinical genome sequencing. Predictors are given the 30 genome sequences, and are also provided with the phenotypic descriptions as shared with the diagnostic laboratory. The challenge is to predict what class of disease is associated with each genome, and which genome corresponds to which clinical description. Predictors may additionally identify the diagnostic variant(s) underlying the predictions, and identify predictive secondary variants conferring high risk of other diseases whose phenotypes are not reported in the clinical descriptions.","","https://genomeinterpretation.org/CAGI5-sickkids5.html","completed","intermediate","2","","2017-12-22","2018-04-26","2023-06-23 00:00:00","2023-10-14 05:39:10" -"71","cagi5-intellectual-disability","CAGI5: ID Panel","","The challenge presented here is to use computational methods to predict a patient's clinical phenotype and the causal variant(s) based on analysis of their gene panel sequence data. Sequence data for 74 genes associated with intellectual disability (ID) and/or Autism spectrum disorders (ASD) from a cohort of 150 patients with a range of neurodevelopmental presentations (ID, autism, epilepsy, etc..) have been made available for this challenge. For each patient, predictors must report the causative variants and which of seven phenotypes are present.","","https://genomeinterpretation.org/CAGI5-intellectual-disability.html","completed","intermediate","2","","2017-12-22","2018-04-30","2023-06-23 00:00:00","2023-10-18 15:28:06" -"72","cagi5-clotting-disease","CAGI5: Clotting disease exomes","","African Americans have a higher incidence of developing venous thromboembolisms (VTE), which includes deep vein thrombosis (DVT) and pulmonary embolism (PE), than people of European ancestry. Participants are provided with exome data and clinical covariates for a cohort of African Americans who have been prescribed Warfarin either because they had experienced a VTE event or had been diagnosed with atrial fibrillation (which predisposes to clotting). The challenge is to distinguish between these conditions. At present, in contrast to European ancestry, there are no genetic methods for anticipating which African Americans are most at risk of a venous thromboembolism, and the results of this challenge may contribute to the development of such tools.","","https://genomeinterpretation.org/CAGI5-clotting-disease.html","completed","intermediate","2","","2017-11-23","2018-04-28","2023-06-23 00:00:00","2023-10-18 15:30:55" -"73","cagi6-sickkids","CAGI6: SickKids clinical genomes and transcriptomes","The SickKids Genome Clinic is providing clinical phenotypic information in t...","This challenge involves data from 79 children who were referred to The Hospital for Sick Children's (SickKids) Genome Clinic for genome sequencing because of suspected but undiagnosed genetic disorders. Research subjects are consented for sharing of their sequence data and phenotype information with researchers working to understand the molecular causes of rare disease. When a candidate disease variant believed to be related to the phenotype is identified, the variant is adjudicated and confirmed in a clinical setting. In this challenge, transcriptomic and phenotype data from a subset of the “solved” (diagnosed) and “unsolved” SickKids patients will be provided, along with corresponding genomic sequence data. The challenge is to use a transcriptome-driven approach to identify the gene(s) and molecular mechanisms underlying the phenotypic descriptions in each case. For the unsolved cases, prioritized variants from the participating teams will be examined to see if additional diagno...","","https://genomeinterpretation.org/CAGI6-sickkids.html","completed","intermediate","1","","2021-08-04","2021-12-31","2023-06-23 00:00:00","2023-10-18 20:53:36" -"74","cagi6-cam","CAGI6: CaM","","Calmodulin (CaM) is a ubiquitous calcium (Ca2+) sensor protein interacting with more than 200 molecular partners, thereby regulating a variety of biological processes. Missense point mutations in the genes encoding CaM have been associated with ventricular tachycardia and sudden cardiac death. A library encompassing up to 17 point mutations was assessed by far-UV circular dichroism (CD) by measuring melting temperature (Tm) and percentage of unfolding (%unfold) upon thermal denaturation at pH and salt concentration that mimic the physiological conditions. The challenge is to predict: the Tm and %unfold values for isolated CaM variants under Ca2+-saturating conditions (Ca2+-CaM) and in the Ca2+-free (apo) state; whether the point mutation stabilizes or destabilizes the protein (based on Tm and %unfold).","","https://genomeinterpretation.org/CAGI6-cam.html","completed","intermediate","1","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-18 15:32:37" -"75","cami-ii","CAMI II","","CAMI II offers several challenges-an assembly, a genome binning, a taxonomic binning and a taxonomic profiling challenge, on several multi-sample data sets from different environments, including long and short read data. This includes a marine data set and a high-strain diversity data set, with a third data set to follow later. A pathogen detection challenge on a clinical sample is also provided.","","https://www.microbiome-cosi.org/cami/cami/cami2","completed","intermediate","3","","2019-01-14","2021-01-31","2023-06-23 00:00:00","2023-10-17 23:15:00" -"76","camda18-metasub-forensics","CAMDA18-MetaSUB Forensics","","The MetaSUB International Consortium is building a longitudinal metagenomic map of mass-transit systems and other public spaces across the globe. The consortium maintains a strategic partnership with CAMDA and this year provides data from global City Sampling Days for the first-ever multi-city forensic analyses.","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#metasub_forensics_challenge","completed","intermediate","7","","\N","\N","2023-06-23 00:00:00","2023-10-17 23:15:08" -"77","camda18-cmap-drug-safety","CAMDA18-CMap Drug Safety","","Attrition in drug discovery and development due to safety / toxicity issues remains a significant concern, and there are strong efforts to identify and mitigate risk as early as possible. Drug-induced liver injury (DILI) is one of the primary problems in drug development and regulatory clearance due to the poor performance of existing preclinical models. There is a pressing need to evaluate alternative methods for predicting DILI, with great hopes being placed in modern approaches from statistics and machine learning applied to genome scale profiling data. A critical question thus is if we can better integrate, understand, and exploit information from cell-based screens like the Broad Institute Connectivity Map (CMap, Science 313, Nature Reviews Cancer 7). This CAMDA challenge focuses on understanding or predicting drug induced liver injury in humans from cell-based screens, specifically the CMap gene expression responses of two different cancer cell lines (MCF7 and PC3) to 276 d...","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#cmap_drug_safety_challenge","completed","intermediate","7","","\N","\N","2023-06-23 00:00:00","2023-10-14 05:39:18" -"78","camda18-cancer-data-integration","CAMDA18-Cancer Data Integration","","Examine the power of data integration in a real-world clinical settings. Many approaches work well on some data-sets yet not on others. We here challenge you to demonstrate a unified single approach to data-integration that matches or outperforms the current state of the art on two different diseases, breast cancer and neuroblastoma. Breast cancer affects about 3 million women every year (McGuire et al, Cancers 7), and this number is growing fast, especially in developed countries. Can you improve on the large Metabric study (Curtis et al., Nature 486, and Dream Challenge, Margolin et al, Sci Transl Med 5)? The cohort is biologically heterogeneous with all five distinct PAM50 breast cancer subtypes represented. Matched profiles for microarray and copy number data as well as clinical information (survival times, multiple prognostic markers, therapy data) are available for about 2,000 patients. Neuroblastoma is the most common extracranial solid tumor in children. The base study com...","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#cancer_data_integration_challenge","completed","intermediate","7","","\N","\N","2023-06-23 00:00:00","2023-10-14 05:39:18" -"79","cafa-4","CAFA 4","","The goal of the Critical Assessment of Functional Annotation(CAFA) challenge is to evaluate automated protein function prediction algorithms in the task of predicting Gene Ontology and Human Phenotype Ontology terms for a given set of protein sequences. For the GO-based predictions, the evaluation will be carried out for the Molecular Function Ontology, Biological Process Ontology and Cellular Component Ontology. Participants develop protein function prediction algorithms using training protein sequence data and submit their predictions on target protein sequence data.","","https://www.biofunctionprediction.org/cafa/","completed","intermediate","1","","2019-10-21","2020-02-12","2023-06-23 00:00:00","2023-10-14 05:39:20" -"80","casp13","CASP13","","CASP (Critical Assessment of Structure Prediction) is a community wide experiment to determine and advance the state of the art in modeling protein structure from amino acid sequence. Every two years, participants are invited to submit models for a set of proteins for which the experimental structures are not yet public. Independent assessors then compare the models with experiment. Assessments and results are published in a special issue of the journal PROTEINS. In the most recent CASP round, CASP12, nearly 100 groups from around the world submitted more than 50,000 models on 82 modeling targets","","https://predictioncenter.org/casp13/index.cgi","completed","intermediate","14","","2018-04-18","2018-08-20","2023-06-23 00:00:00","2023-10-17 22:52:29" -"81","casp14","CASP14","","CASP (Critical Assessment of Structure Prediction) is a community wide experiment to determine and advance the state of the art in modeling protein structure from amino acid sequence. Every two years, participants are invited to submit models for a set of proteins for which the experimental structures are not yet public. Independent assessors then compare the models with experiment. Assessments and results are published in a special issue of the journal PROTEINS. In the most recent CASP round, CASP14, nearly 100 groups from around the world submitted more than 67,000 models on 90 modeling targets.","","https://predictioncenter.org/casp14/index.cgi","completed","intermediate","14","","2020-05-04","2020-09-07","2023-06-23 00:00:00","2023-10-17 22:47:26" -"82","cfsan-pathogen-detection","CFSAN Pathogen Detection","","In the U.S. alone, one in six individuals, an estimated 48 million people, fall prey to foodborne illness, resulting in 128,000 hospitalizations and 3,000 deaths per year. Economic burdens are estimated cumulatively at $152 billion dollars annually, including $39 billion due to contamination of fresh and processed produce. One longstanding problem is the ability to rapidly identify the food-source associated with the outbreak being investigated. The faster an outbreak is identified and the increased certainty that a given source (e.g., papayas from Mexico) and patients are linked, the faster the outbreak can be stopped, limiting morbidity and mortality. In the last few years, the application of next-generation sequencing (NGS) technology for whole genome sequencing (WGS) of foodborne pathogens has revolutionized food pathogen outbreak surveillance. WGS of foodborne pathogens enables high-resolution identification of pathogens isolated from food or environmental samples. These pat...","","https://precision.fda.gov/challenges/2","completed","intermediate","6","","2018-02-15","2018-04-26","2023-06-23 00:00:00","2023-10-14 05:39:23" -"83","cdrh-biothreat","CDRH Biothreat","","Many infectious diseases have similar signs and symptoms, making it challenging for healthcare providers to identify the disease-causing agent. Clinical samples are often tested by multiple test methods to help reveal the microbe that is causing the infectious disease. The results of these test methods can help healthcare professionals determine the best treatment for patients. Today, High-Throughput Sequencing (HTS) or Next Generation Sequencing (NGS) technology has the capability, as a single test, to accomplish what might have required several different tests in the past. NGS technology may allow the diagnosis of infections without prior knowledge of disease(s) cause. NGS technology can potentially reveal the presence of all microorganisms in a patient sample. Using infectious disease NGS (ID-NGS) technology, each microbial pathogen may be identified by its unique genomic fingerprint. The vision of ID-NGS technology is to further improve patient care by delivering diagnostics ...","","https://precision.fda.gov/challenges/3","completed","intermediate","6","","2018-08-03","2018-10-18","2023-06-23 00:00:00","2023-10-14 05:39:24" -"84","multi-omics-enabled-sample-mislabeling-correction","Multi-omics Enabled Sample Mislabeling Correction","","In biomedical research, sample mislabeling (accidental swapping of patient samples) or data mislabeling (accidental swapping of patient omics data) has been a long-standing problem that contributes to irreproducible results and invalid conclusions. These problems are particularly prevalent in large scale multi-omics studies, in which multiple different omics experiments are carried out at different time periods and/or in different labs. Human errors could arise during sample transferring, sample tracking, large-scale data generation, and data sharing/management. Thus, there is a pressing need to identify and correct sample and data mislabeling events to ensure the right data for the right patient. Simultaneous use of multiple types of omics platforms to characterize a large set of biological samples, as utilized in The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects, has been demonstrated as a powerful approach to understanding the ...","","https://precision.fda.gov/challenges/4","completed","intermediate","6","https://doi.org/10.1038/s41591-018-0180-x","2018-09-24","2018-12-19","2023-06-23 00:00:00","2023-10-14 05:39:25" -"85","biocompute-object-app-a-thon","BioCompute Object App-a-thon","","Like scientific laboratory experiments, bioinformatics analysis results and interpretation are faced with reproducibility challenges due to the variability in multiple computational parameters, including input format, prerequisites, platform dependencies, and more. Even small changes in these computational parameters may have a large impact on the results and carry big implications for their scientific validity. Because there are currently no standardized schemas for reporting computational scientific workflows and parameters together with their results, the ways in which these workflows are communicated is highly variable, incomplete, and difficult or impossible to reproduce. The US Food and Drug Administration (FDA) High Performance Virtual Environment (HIVE) group and George Washington University (GW) have partnered to establish a framework for community-based standards development and harmonization of high-throughput sequencing (HTS) computations and data formats based arou...","","https://precision.fda.gov/challenges/7/","completed","intermediate","6","https://doi.org/10.1101/2020.11.02.365528","2019-05-14","2019-10-18","2023-06-23 00:00:00","2023-10-14 05:39:25" -"86","brain-cancer-predictive-modeling-and-biomarker-discovery","Brain Cancer Predictive Modeling and Biomarker Discovery","","An estimated 86,970 new cases of primary brain and other central nervous system tumors are expected to be diagnosed in the US in 2019. Brain tumors comprise a particularly deadly subset of all cancers due to limited treatment options and the high cost of care. Only a few prognostic and predictive markers have been successfully implemented in the clinic so far for gliomas, the most common malignant brain tumor type. These markers include MGMT promoter methylation in high-grade astrocytomas, co-deletion of 1p/19q in oligodendrogliomas, and mutations in IDH1 or IDH2 genes (Staedtke et al. 2016). There remains significant potential for identifying new clinical biomarkers in gliomas. Clinical investigators at Georgetown University are seeking to advance precision medicine techniques for the prognosis and treatment of brain tumors through the identification of novel multi-omics biomarkers. In support of this goal, precisionFDA and the Georgetown Lombardi Comprehensive Cancer Center and ...","","https://precision.fda.gov/challenges/8/","completed","advanced","6","","2019-11-01","2020-02-14","2023-06-23 00:00:00","2023-10-14 05:39:25" -"87","gaining-new-insights-by-detecting-adverse-event-anomalies","Gaining New Insights by Detecting Adverse Event Anomalies","","The Food and Drug Administration (FDA) calls on the public to develop computational algorithms for automatic detection of adverse event anomalies using publicly available data.","","https://precision.fda.gov/challenges/9/","completed","intermediate","6","","2020-01-17","2020-05-18","2023-06-23 00:00:00","2023-10-14 05:39:27" -"88","calling-variants-in-difficult-to-map-regions","Calling Variants in Difficult-to-Map Regions","","This challenge calls on the public to assess variant calling pipeline performance on a common frame of reference, with a focus on benchmarking in difficult-to-map regions, segmental duplications, and the Major Histocompatibility Complex (MHC).","","https://precision.fda.gov/challenges/10/","completed","intermediate","6","https://doi.org/10.1016/j.xgen.2022.100129","2020-05-01","2020-06-15","2023-06-23 00:00:00","2023-10-14 05:39:28" -"89","vha-innovation-ecosystem-and-covid-19-risk-factor-modeling","VHA Innovation Ecosystem and COVID-19 Risk Factor Modeling","","The novel coronavirus disease 2019 (COVID-19) is a respiratory disease caused by a new type of coronavirus, known as “severe acute respiratory syndrome coronavirus 2,” or SARS-CoV-2. On March 11, 2020, the World Health Organization (WHO) declared the outbreak a global pandemic. As of Monday, June 1, the Johns Hopkins University COVID-19 dashboard reports over 6.21 million total confirmed cases worldwide, including over 1.79 million cases in the United States. Although most people have mild to moderate symptoms, the disease can cause severe medical complications leading to death in some people. The Centers for Disease Control and Prevention (CDC) have identified several groups at elevated risk for severe illness, including people 65 years and older, individuals living in nursing homes or long term care facilities, and those with serious underlying medical conditions, such as severe obesity, diabetes, chronic lung disease or moderate to severe asthma, chronic kidney or liver disease...","","https://precision.fda.gov/challenges/11/","completed","intermediate","6","","2020-06-02","2020-07-03","2023-06-23 00:00:00","2023-10-14 05:39:28" -"90","covid-19-precision-immunology-app-a-thon","COVID-19 Precision Immunology App-a-thon","","The novel coronavirus disease 2019 (COVID-19), a respiratory disease caused by a new type of coronavirus, known as “severe acute respiratory syndrome coronavirus 2” or SARS-CoV-2, was declared a global pandemic by the World Health Organization on March 11, 2020. To date, the Johns Hopkins University COVID-19 dashboard reports over 62 million confirmed cases worldwide, with a wide range of disease severity from asymptomatic to deaths (over 1.46 million). To effectively combat the widespread transmission of COVID-19 infection and save lives especially of those vulnerable individuals, it is imperative to better understand its pathophysiology to enable effective diagnosis, prognosis and treatment strategies using rapidly shared data.","","https://precision.fda.gov/challenges/12/","completed","intermediate","6","","2020-11-30","2021-01-29","2023-06-23 00:00:00","2023-10-14 05:39:29" -"91","smarter-food-safety-low-cost-tech-enabled-traceability","Smarter Food Safety Low Cost Tech-Enabled Traceability","","The motivation is tapping into new technologies and integrating data streams will help to advance the widespread, consistent implementation of traceability systems across the food industry. However, the affordability of such technologies, particularly for smaller companies, can be a barrier to implementing tech-enabled traceability systems. FDA's New Era of Smarter Food Safety initiative strives to work with stakeholders to explore low-cost or no-cost options so that our approaches are inclusive of and viable for human and animal food operations of all sizes. Democratizing the benefits of digitizing data will allow the entire food system to move more rapidly towards digital traceability systems. The primary goal is to encourage stakeholders, including technology providers, public health advocates, entrepreneurs, and innovators from all disciplines and around the world, to develop traceability hardware, software, or data analytics platforms that are low-cost or no-cost to the en...","","https://precision.fda.gov/challenges/13","completed","intermediate","6","","2021-06-01","2021-07-30","2023-06-23 00:00:00","2023-10-17 23:05:49" -"92","tumor-mutational-burden-tmb-challenge-phase-1","Tumor Mutational Burden (TMB) Challenge Phase 1","","Tumor mutational burden (TMB) is generally defined as the number of mutations detected in a patient's tumor sample per megabase of DNA sequenced. However different algorithms use different methods for calculating TMB. Mutations in genes in tumor cells may lead to the creation of neoantigens, which have the potential to activate an immune system response against the tumor, and the likelihood of an immune system response may increase with the number of mutations. Thus, TMB is a biomarker for some immunotherapy drugs, called immune checkpoint inhibitors, such as those that target the PD-1 and PD-L1 pathways (Chan et al., 2019). An outstanding problem is the lack of standardization for TMB calculation and reporting between different assays. To address this problem, the Friends of Cancer Research convened a working group of industry and regulatory stakeholders to develop guidance and tools for TMB harmonization. Results from the first phase of this effort were presented at AACR 2020 (...","","https://precision.fda.gov/challenges/17","completed","advanced","6","","2021-06-21","2021-09-13","2023-06-23 00:00:00","2023-10-14 05:39:32" -"93","kits21","Kidney and Kidney Tumor Segmentation","","The 2021 Kidney and Kidney Tumor Segmentation challenge (abbreviated KiTS21) is a competition in which teams compete to develop the best system for automatic semantic segmentation of renal tumors and surrounding anatomy. Kidney cancer is one of the most common malignancies in adults around the world, and its incidence is thought to be increasing [1]. Fortunately, most kidney tumors are discovered early while they're still localized and operable. However, there are important questions concerning management of localized kidney tumors that remain unanswered [2], and metastatic renal cancer remains almost uniformly fatal [3]. Kidney tumors are notorious for their conspicuous appearance in computed tomography (CT) imaging, and this has enabled important work by radiologists and surgeons to study the relationship between tumor size, shape, and appearance and its prospects for treatment [4,5,6]. It's laborious work, however, and it relies on assessments that are often subjective and impr...","","https://kits21.kits-challenge.org/","completed","advanced","5","","2021-08-23","2021-09-17","2023-06-23 00:00:00","2023-10-16 18:30:07" -"94","realnoisemri","Real Noise MRI","","In recent years, there is a growing focus on the application of fast magnetic resonance imaging (MRI) based on prior knowledge. In the 1980s and 2000s the community used either purely mathematical models such as the partial Fourier transform or solutions derived through advanced engineering such as parallel imaging to speed up MRI acquisition. Since the mid-2000's, compressed sensing and artificial intelligence have been employed to speed up MRI acquisition. These newer methods rely on under sampling the data acquired in Fourier (aka k-) space and then interpolating or augmenting k-space data based on training data content. One of the underlying problems for the development of fast imaging techniques, that just as in e.g. [1], it is common to use a fully sampled image as ground truth and then under sample it in k-space in order to simulate under sampled data. The problem with this approach is that in cases were the under sampled data is corrupted, through e.g. motion, this under s...","","https://realnoisemri.grand-challenge.org/","completed","intermediate","5","","2021-09-21","2021-12-06","2023-06-23 00:00:00","2023-10-14 05:39:33" -"95","deep-generative-model-challenge-for-da-in-surgery","Deep Generative Model Challenge for DA in Surgery","","Mitral regurgitation (MR) is the second most frequent indication for valve surgery in Europe and may occur for organic or functional causes [1]. Mitral valve repair, although considerably more difficult, is prefered over mitral valve replacement, since the native tissue of the valve is preserved. It is a complex on-pump heart surgery, often conducted only by a handful of surgeons in high-volume centers. Minimally invasive procedures, which are performed with endoscopic video recordings, became more and more popular in recent years. However, data availability and data privacy concerns are still an issue for the development of automatic scene analysis algorithms. The AdaptOR challenge aims to address these issues by formulating a domain adaptation problem from simulation to surgery. We provide a smaller number of datasets from real surgeries, and a larger number of annotated recordings of training and planning sessions from a physical mitral valve simulator. The goal is to reduce th...","","https://adaptor2021.github.io/","completed","intermediate","1","","2021-04-01","2021-07-16","2023-06-23 00:00:00","2023-10-14 05:39:34" -"96","aimdatathon","AIM Datathon 2020","Join the AI in Medicine ( AIM ) Datathon 2020","Join the AI in Medicine ( AIM ) Datathon 2020","","https://www.kaggle.com/competitions/aimdatathon","completed","intermediate","8","","2020-11-09","2020-11-22","2023-06-23 00:00:00","2023-10-16 17:55:00" -"97","opc-recurrence","Oropharynx Cancer (OPC) Radiomics Challenge :: Local Recurrence Prediction","Determine from CT data whether a tumor will be controlled by definitive radi...","Determine from CT data whether a tumor will be controlled by definitive radiation therapy.","","https://www.kaggle.com/competitions/opc-recurrence","completed","intermediate","8","","2016-07-26","2016-09-12","2023-06-23 00:00:00","2023-10-16 18:10:11" -"98","oropharynx-radiomics-hpv","Oropharynx Cancer (OPC) Radiomics Challenge :: Human Papilloma Virus (HPV) Status Prediction","Predict from CT data the HPV phenotype of oropharynx tumors; compare to grou...","Predict from CT data the HPV phenotype of oropharynx tumors; compare to ground-truth results previously obtained by p16 or HPV testing.","","https://www.kaggle.com/competitions/oropharynx-radiomics-hpv","completed","intermediate","8","","2016-07-26","2016-09-12","2023-06-23 00:00:00","2023-10-16 18:10:15" -"99","data-science-bowl-2017","Data Science Bowl 2017","Can you improve lung cancer detection?","Can you improve lung cancer detection?","","https://www.kaggle.com/competitions/data-science-bowl-2017","completed","intermediate","8","","2017-01-12","2017-04-12","2023-06-23 00:00:00","2023-10-14 05:39:38" -"100","predict-impact-of-air-quality-on-death-rates","Predict impact of air quality on mortality rates","Predict CVD and cancer caused mortality rates in England using air quality d...","Predict CVD and cancer caused mortality rates in England using air quality data available from Copernicus Atmosphere Monitoring Service","","https://www.kaggle.com/competitions/predict-impact-of-air-quality-on-death-rates","completed","intermediate","8","","2017-02-13","2017-05-05","2023-06-23 00:00:00","2023-10-14 05:39:38" -"101","intel-mobileodt-cervical-cancer-screening","Intel & MobileODT Cervical Cancer Screening","Which cancer treatment will be most effective?","Which cancer treatment will be most effective?","","https://www.kaggle.com/competitions/intel-mobileodt-cervical-cancer-screening","completed","intermediate","8","","2017-03-15","2017-06-21","2023-06-23 00:00:00","2023-10-14 05:39:39" -"102","msk-redefining-cancer-treatment","Personalized Medicine-Redefining Cancer Treatment","Predict the effect of Genetic Variants to enable Personalized Medicine","Predict the effect of Genetic Variants to enable Personalized Medicine","","https://www.kaggle.com/competitions/msk-redefining-cancer-treatment","completed","intermediate","8","","2017-06-26","2017-10-02","2023-06-23 00:00:00","2023-10-14 05:39:40" -"103","mubravo","Predicting Cancer Diagnosis","Bravo's machine learning competition!","Bravo's machine learning competition!","","https://www.kaggle.com/competitions/mubravo","completed","intermediate","8","","2018-07-31","2018-08-13","2023-06-23 00:00:00","2023-10-14 05:39:41" -"104","histopathologic-cancer-detection","Histopathologic Cancer Detection","Identify metastatic tissue in histopathologic scans of lymph node sections","Identify metastatic tissue in histopathologic scans of lymph node sections","","https://www.kaggle.com/competitions/histopathologic-cancer-detection","completed","intermediate","8","","2018-11-16","2019-03-30","2023-06-23 00:00:00","2023-10-14 05:39:41" -"105","tjml1920-decision-trees","TJML 2019-20 Breast Cancer Detection Competition","Use a decision tree to identify malignant breast cancer tumors","Use a decision tree to identify malignant breast cancer tumors","","https://www.kaggle.com/competitions/tjml1920-decision-trees","completed","intermediate","8","","2019-09-22","2019-10-16","2023-06-23 00:00:00","2023-10-14 05:39:42" -"106","prostate-cancer-grade-assessment","Prostate cANcer graDe Assessment (PANDA) Challenge","Prostate cancer diagnosis using the Gleason grading system","Prostate cancer diagnosis using the Gleason grading system","","https://www.kaggle.com/competitions/prostate-cancer-grade-assessment","completed","intermediate","8","","2020-04-21","2020-07-22","2023-06-23 00:00:00","2023-10-14 05:39:43" -"107","breast-cancer","Breast Cancer","Use cell nuclei categories to predict breast cancer tumor.","Use cell nuclei categories to predict breast cancer tumor.","","https://www.kaggle.com/competitions/breast-cancer","completed","intermediate","8","","2020-08-12","2020-08-13","2023-06-23 00:00:00","2023-10-14 05:39:43" -"108","breast-cancer-detection","Breast Cancer Detection","breast cancer detection","breast cancer detection","","https://www.kaggle.com/competitions/breast-cancer-detection","completed","intermediate","8","","2020-09-25","2020-12-31","2023-06-23 00:00:00","2023-10-14 05:39:44" -"109","hrpred","Prediction of High Risk Patients","Classification of high and low risk cancer patients","Classification of high and low risk cancer patients","","https://www.kaggle.com/competitions/hrpred","completed","intermediate","8","","2020-11-25","2020-12-05","2023-06-23 00:00:00","2023-10-14 05:39:44" -"110","ml4moleng-cancer","MIT ML4MolEng-Predicting Cancer Progression","MIT 3.100, 10.402, 20.301 In class ML competition (Spring 2021)","MIT 3.100, 10.402, 20.301 In class ML competition (Spring 2021)","","https://www.kaggle.com/competitions/ml4moleng-cancer","completed","intermediate","8","","2021-05-06","2021-05-21","2023-06-23 00:00:00","2023-10-14 05:39:45" -"111","uw-madison-gi-tract-image-segmentation","UW-Madison GI Tract Image Segmentation","Track healthy organs in medical scans to improve cancer treatment","Track healthy organs in medical scans to improve cancer treatment","","https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation","completed","intermediate","8","","2022-04-14","2022-07-14","2023-06-23 00:00:00","2023-10-14 05:39:46" -"112","rsna-miccai-brain-tumor-radiogenomic-classification","RSNA-MICCAI Brain Tumor Radiogenomic Classification","Predict the status of a genetic biomarker important for brain cancer treatment","The Brain Tumor Segmentation (BraTS) challenge celebrates its 10th anniversary, and this year is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society. The RSNA-ASNR-MICCAI BraTS 2021 challenge utilizes multi-institutional pre-operative baseline multi-parametric magnetic resonance imaging (mpMRI) scans, and focuses on the evaluation of state-of-the-art methods for (Task 1) the segmentation of intrinsically heterogeneous brain glioblastoma sub-regions in mpMRI scans. Furthemore, this BraTS 2021 challenge also focuses on the evaluation of (Task 2) classification methods to predict the MGMT promoter methylation status. Participants are free to choose whether they want to focus only on one or both tasks.","","https://www.kaggle.com/competitions/rsna-miccai-brain-tumor-radiogenomic-classification","completed","advanced","8","","2021-07-13","2021-10-15","2023-06-23 00:00:00","2023-10-14 05:39:46" -"113","breastcancer","Breast Cancer - Beginners ML","Beginners hands-on experience with ML basics","Beginners hands-on experience with ML basics","","https://www.kaggle.com/competitions/breastcancer","completed","intermediate","8","","2021-12-21","2022-02-12","2023-06-23 00:00:00","2023-10-18 21:18:15" -"114","ml-olympiad-health-and-education","ML Olympiad -Let's Fight lung cancer","Use your ML expertise to help us step another step toward defeating cancer [...","Use your ML expertise to help us step another step toward defeating cancer [ Starts on the 14th February ]","","https://www.kaggle.com/competitions/ml-olympiad-health-and-education","completed","intermediate","8","","2022-01-31","2022-03-19","2023-06-23 00:00:00","2023-10-14 05:39:48" -"115","cs98-22-dl-task1","CS98X-22-DL-Task1","This competition is related to Task 1 in coursework-breast cancer classification","This competition is related to Task 1 in coursework-breast cancer classification","","https://www.kaggle.com/competitions/CS98-22-DL-Task1","completed","intermediate","8","","2022-02-28","2022-04-11","2023-06-23 00:00:00","2023-10-14 05:39:48" -"116","parasitedetection-iiitb2019","Parasite detection","detect if cell image has parasite or is uninfected","detect if cell image has parasite or is uninfected","","https://www.kaggle.com/competitions/parasitedetection-iiitb2019","completed","intermediate","8","","2019-10-13","2019-11-25","2023-06-23 00:00:00","2023-10-14 05:39:49" -"117","hpa-single-cell-image-classification","Human Protein Atlas -Single Cell Classification","Find individual human cell differences in microscope images","Find individual human cell differences in microscope images","","https://www.kaggle.com/competitions/hpa-single-cell-image-classification","completed","intermediate","8","","2021-01-26","2021-05-11","2023-06-23 00:00:00","2023-10-14 05:39:50" -"118","stem-cell-predcition","Stem Cell Predcition","Classify stem and non-stem cells using RNA-seq data","Classify stem and non-stem cells using RNA-seq data","","https://www.kaggle.com/competitions/stem-cell-predcition","completed","intermediate","8","","2021-04-01","2021-07-01","2023-06-23 00:00:00","2023-10-14 05:39:50" -"119","sartorius-cell-instance-segmentation","Sartorius - Cell Instance Segmentation","Detect single neuronal cells in microscopy images","In this competition, you’ll detect and delineate distinct objects of interest in biological images depicting neuronal cell types commonly used in the study of neurological disorders. More specifically, you'll use phase contrast microscopy images to train and test your model for instance segmentation of neuronal cells. Successful models will do this with a high level of accuracy. If successful, you'll help further research in neurobiology thanks to the collection of robust quantitative data. Researchers may be able to use this to more easily measure the effects of disease and treatment conditions on neuronal cells. As a result, new drugs could be discovered to treat the millions of people with these leading causes of death and disability.","","https://www.kaggle.com/competitions/sartorius-cell-instance-segmentation","completed","intermediate","8","","2021-10-14","2021-12-30","2023-06-23 00:00:00","2023-10-16 18:05:17" -"120","pvelad","Photovoltaic cell anomaly detection","Hosted by Hebei University of Technology (AIHebut research group) and Beihan...","Hosted by Hebei University of Technology (AIHebut research group) and Beihang University (NAVE research group)","","https://www.kaggle.com/competitions/pvelad","completed","intermediate","8","","2022-03-15","2022-07-30","2023-06-23 00:00:00","2023-10-14 05:39:51" -"121","blood-mnist","Blood-MNIST","Classifying blood cell types using Weights and Biases","Classifying blood cell types using Weights and Biases","","https://www.kaggle.com/competitions/blood-mnist","completed","intermediate","8","","2022-03-19","2022-03-19","2023-06-23 00:00:00","2023-10-14 05:39:52" -"122","insilicomolhack","MolHack","Apply deep learning to speedup drug validation","Apply deep learning to speedup drug validation","","https://www.kaggle.com/competitions/insilicomolhack","completed","intermediate","8","","2018-04-02","2018-05-25","2023-06-23 00:00:00","2023-10-14 05:39:53" -"123","codata2019challenge","Cell Response Classification","From recorded timeseries of many cells in a well, predict which drug treatme...","From recorded timeseries of many cells in a well, predict which drug treatment has been applied","","https://www.kaggle.com/competitions/codata2019challenge","completed","intermediate","8","","2019-04-08","2019-05-07","2023-06-23 00:00:00","2023-10-14 05:39:53" -"124","drug-solubility-challenge","Drug solubility challenge","Solubility is vital to achieve desired concentration of drug for anticipated...","Solubility is vital to achieve desired concentration of drug for anticipated pharmacological response.","","https://www.kaggle.com/competitions/drug-solubility-challenge","completed","intermediate","8","","2019-05-18","2019-10-18","2023-06-23 00:00:00","2023-10-14 05:39:54" -"125","kinase-inhibition-challenge","Kinase inhibition challenge","Protein kinases have become a major class of drug targets, accumulating a hu...","Protein kinases have become a major class of drug targets, accumulating a huge amount of data","","https://www.kaggle.com/competitions/kinase-inhibition-challenge","completed","intermediate","8","","2019-05-20","2019-12-28","2023-06-23 00:00:00","2023-10-14 05:39:54" -"126","ai-drug-discovery","AI Drug Discovery Workshop and Coding Challenge","Developing Fundamental AI Programming Skills for Drug Discovery","Developing Fundamental AI Programming Skills for Drug Discovery","","https://www.kaggle.com/competitions/ai-drug-discovery","completed","intermediate","8","","2021-11-12","2021-12-31","2023-06-23 00:00:00","2023-10-14 05:39:56" -"127","protein-compound-affinity","Structure-free protein-ligand affinity prediction - Task 1 Fitting","Developing new AI models for drug discovery, main portal (Task-1 fitting)","Developing new AI models for drug discovery, main portal (Task-1 fitting)","","https://www.kaggle.com/competitions/protein-compound-affinity","completed","intermediate","8","","2021-12-06","2021-12-31","2023-06-23 00:00:00","2023-10-16 18:13:18" -"128","cisc873-dm-f21-a5","CISC873-DM-F21-A5","Anti-Cancer Drug Activity Prediction","Anti-Cancer Drug Activity Prediction","","https://www.kaggle.com/competitions/cisc873-dm-f21-a5","completed","intermediate","8","","2021-11-26","2021-12-10","2023-06-23 00:00:00","2023-10-14 05:39:56" -"129","pro-lig-aff-task2-mse","Structure-free protein-ligand affinity prediction - Task 2 Fitting","Developing new AI models for drug discovery (Task-2 fitting)","Developing new AI models for drug discovery (Task-2 fitting)","","https://www.kaggle.com/competitions/pro-lig-aff-task2-mse","completed","intermediate","8","","2021-12-08","2021-12-31","2023-06-23 00:00:00","2023-10-16 18:13:22" -"130","pro-lig-aff-task1-pearsonr","Structure-free protein-ligand affinity prediction - Task 1 Ranking","Developing new AI models for drug discovery (Task-1 ranking)","Developing new AI models for drug discovery (Task-1 ranking)","","https://www.kaggle.com/competitions/pro-lig-aff-task1-pearsonr","completed","intermediate","8","","2021-12-08","2021-12-31","2023-06-23 00:00:00","2023-10-16 18:13:26" -"131","pro-lig-aff-task2-pearsonr","Structure-free protein-ligand affinity prediction - Task 2 Ranking","Developing new AI models for drug discovery (Task-2 ranking)","Developing new AI models for drug discovery (Task-2 ranking)","","https://www.kaggle.com/competitions/pro-lig-aff-task2-pearsonr","completed","intermediate","8","","2021-12-08","2021-12-31","2023-06-23 00:00:00","2023-10-16 18:13:28" -"132","pro-lig-aff-task3-spearmanr","Structure-free protein-ligand affinity prediction - Task 3 Ranking","Developing new AI models for drug discovery (Task-3 ranking)","Developing new AI models for drug discovery (Task-3 ranking)","","https://www.kaggle.com/competitions/pro-lig-aff-task3-spearmanr","completed","intermediate","8","","2021-12-08","2021-12-31","2023-06-23 00:00:00","2023-10-16 18:13:32" -"133","hhp","Heritage Health Prize","Identify patients who will be admitted to a hospital within the next year us...","Identify patients who will be admitted to a hospital within the next year using historical claims data. (Enter by 06-59-59 UTC Oct 4 2012)","","https://www.kaggle.com/competitions/hhp","completed","intermediate","8","","2011-04-04","2013-04-04","2023-06-23 00:00:00","2023-10-14 05:40:00" -"134","pf2012","Practice Fusion Analyze This! 2012 - Prediction Challenge","Start digging into electronic health records and submit your ideas for the m...","Start digging into electronic health records and submit your ideas for the most promising, impactful or interesting predictive modeling competitions","","https://www.kaggle.com/competitions/pf2012","completed","intermediate","8","","2012-06-07","2012-06-30","2023-06-23 00:00:00","2023-10-16 18:14:24" -"135","pf2012-at","Practice Fusion Analyze This! 2012 - Open Challenge","Start digging into electronic health records and submit your creative, insig...","Start digging into electronic health records and submit your creative, insightful, and visually striking analyses.","","https://www.kaggle.com/competitions/pf2012-at","completed","intermediate","8","","2012-06-07","2012-09-10","2023-06-23 00:00:00","2023-10-16 18:14:26" -"136","seizure-detection","UPenn and Mayo Clinic's Seizure Detection Challenge","Detect seizures in intracranial EEG recordings","Detect seizures in intracranial EEG recordings","","https://www.kaggle.com/competitions/seizure-detection","completed","intermediate","8","","2014-05-19","2014-08-19","2023-06-23 00:00:00","2023-10-14 05:40:02" -"137","seizure-prediction","American Epilepsy Society Seizure Prediction Challenge","Predict seizures in intracranial EEG recordings","Predict seizures in intracranial EEG recordings","","https://www.kaggle.com/competitions/seizure-prediction","completed","intermediate","8","","2014-08-25","2014-11-17","2023-06-23 00:00:00","2023-10-14 05:40:03" -"138","deephealth-1","Deep Health - alcohol","Find Correlations and patterns with Medical data","Find Correlations and patterns with Medical data","","https://www.kaggle.com/competitions/deephealth-1","completed","intermediate","8","","2017-02-13","2017-02-19","2023-06-23 00:00:00","2023-10-16 18:14:48" -"139","deep-health-3","Deep Health - Diabetes 2","This competition is for those attending the Deep Health Hackathon. Predic...","This competition is for those attending the Deep Health Hackathon. Predict the next occurrence of diabetes","","https://www.kaggle.com/competitions/deep-health-3","completed","intermediate","8","","2017-02-15","2017-02-19","2023-06-23 00:00:00","2023-10-16 18:14:50" -"140","d012554-2021","D012554 - 2021","Classify the health of a fetus using CTG data","Classify the health of a fetus using CTG data","","https://www.kaggle.com/competitions/d012554-2021","completed","intermediate","8","","2021-04-11","2021-05-09","2023-06-23 00:00:00","2023-10-16 18:15:04" -"141","idao-2022-bootcamp-insomnia","IDAO 2022. ML Bootcamp - Insomnia","Predict sleep disorder on given human health data","Predict sleep disorder on given human health data","","https://www.kaggle.com/competitions/idao-2022-bootcamp-insomnia","completed","intermediate","8","","2021-12-04","2021-12-05","2023-06-23 00:00:00","2023-10-16 18:15:12" -"142","tweet-mental-health-classification","Tweet Mental Health Classification","Build Models to classify tweets to determine mental health","Build Models to classify tweets to determine mental health","","https://www.kaggle.com/competitions/tweet-mental-health-classification","completed","intermediate","8","","2021-12-27","2022-01-31","2023-06-23 00:00:00","2023-10-14 05:40:07" -"143","ml-olympiad-good-health-and-well-being","ML Olympiad - GOOD HEALTH AND WELL BEING","Use your ML expertise to classify if a patient has heart disease or not","Use your ML expertise to classify if a patient has heart disease or not","","https://www.kaggle.com/competitions/ml-olympiad-good-health-and-well-being","completed","intermediate","8","","2022-02-03","2022-03-01","2023-06-23 00:00:00","2023-10-16 18:15:20" -"144","rsna-breast-cancer-detection","RSNA Screening Mammography Breast Cancer Detection","Find breast cancers in screening mammograms","Find breast cancers in screening mammograms","","https://www.kaggle.com/competitions/rsna-breast-cancer-detection","completed","intermediate","8","","2022-11-28","2023-02-27","2023-06-23 00:00:00","2023-10-14 05:40:12" -"145","biocreative-vii-text-mining-drug-and-chemical-protein-interactions-drugprot","BioCreative VII: Text mining drug and chemical-protein interactions (DrugProt)","","With the rapid accumulation of biomedical literature, it is getting increasingly challenging to exploit efficiently drug-related information described in the scientific literature. One of the most relevant aspects of drugs and chemical compounds are their relationships with certain biomedical entities, in particular genes and proteins. The aim of the DrugProt track (similar to the previous CHEMPROT task of BioCreative VI) is to promote the development and evaluation of systems that are able to automatically detect in relations between chemical compounds/drug and genes/proteins. There are a range of different types of drug-gene/protein interactions, and their systematic extraction and characterization is essential to analyze, predict and explore key biomedical properties underlying high impact biomedical applications. These application scenarios include use cases related to drug discovery, drug repurposing, drug design, metabolic engineering, modeling drug response, pharmacogenet...","","https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/","completed","intermediate","7","","2021-06-15","2021-09-22","2023-06-23 00:00:00","2023-10-16 18:15:36" -"146","extended-literature-ai-for-drug-induced-liver-injury","Extended Literature AI for Drug Induced Liver Injury","","Unexpected Drug-Induced Liver Injury (DILI) still is one of the main killers of promising novel drug candidates. It is a clinically significant disease that can lead to severe outcomes such as acute liver failure and even death. It remains one of the primary liabilities in drug development and regulatory clearance due to the limited performance of mandated preclinical models even today. The free text of scientific publications is still the main medium carrying DILI results from clinical practice or experimental studies. The textual data still has to be analysed manually. This process, however, is tedious and prone to human mistakes or omissions, as results are very rarely available in a standardized form or organized form. There is thus great hope that modern techniques from machine learning or natural language processing could provide powerful tools to better process and derive the underlying knowledge within free form texts. The pressing need to faster process potential drug can...","","http://camda2022.bioinf.jku.at/contest_dataset#extended_literature_ai_for_drug_induced_liver_injury","completed","intermediate","7","","\N","2022-05-20","2023-06-23 00:00:00","2023-10-14 05:40:14" -"147","anti-microbial-resistance-forensics","Anti-Microbial Resistance Forensics","","Bacteriophages, being the re-occuring mystery in the history of science are believed to be they key for understanding of microbial evolution and the transfer of AMR genes. Recent studies show that there is a significant correlation between occurence of Phages and AMR genes, indicating that they are indeed taking part in the spread of them. While taking part in AMR dissemination the phages are also considered as the potential alternative to antibiotics. In such contradictory world there is a huge potential as well as urgent need for precise classification, description and analysis of capabilities. Due to pandemic of SARS-CoV-2, advance in phylogenetic algorithms and k-mer based methods have been extremely rapid and those improvements are witing to be adapted to different branches of life sciences.","","http://camda2022.bioinf.jku.at/contest_dataset#anti-microbial_resistance_forensics","completed","intermediate","7","","\N","2022-05-20","2023-06-23 00:00:00","2023-10-14 05:40:14" -"148","disease-maps-to-modelling-covid-19","Disease Maps to Modelling COVID-19","Use the COVID-19 disease map to suggest drugs candidate for repurposing, tha...","The Disease Maps to modeling COVID-19 Challenge provides highly detailed expert-curated molecular mechanistic maps for COVID-19. Combine them with available omic data to expand the current biological knowledge on COVID-19 mechanism of infection and downstream consequences. The main topic for this year's challenge is drug repurposing with the possibility of Real World Data based validation of the most promising candidates suggested.","","http://camda2022.bioinf.jku.at/contest_dataset#disease_maps_to_modelling_covid-19","completed","intermediate","7","","\N","2022-05-20","2023-06-23 00:00:00","2023-10-14 05:40:15" -"149","crowdsourced-evaluation-of-inchi-based-tautomer-identification","Crowdsourced Evaluation of InChI-based Tautomer Identification","Calling on scientists from industry, government, and academia dealing with c...","This challenge focuses on the International Chemical Identifier (InChI), which was developed and is maintained under the auspices of the International Union of Pure and Applied Chemistry (IUPAC) and the InChI Trust. The InChI Trust, the IUPAC Working Group on Tautomers, and the U.S. Food and Drug Administration (FDA) call on the scientific community dealing with chemical repositories/data sets and analytics of compounds to test the recently modified InChI algorithm, which was designed for advanced recognition of tautomers. Participants will evaluate this algorithm against real chemical samples in this Crowdsourced Evaluation of InChI-based Tautomer Identification.","","https://precision.fda.gov/challenges/29","completed","intermediate","6","","2022-11-01","2023-03-01","2023-06-23 00:00:00","2023-10-14 05:40:15" -"150","nctr-indel-calling-from-oncopanel-sequencing-challenge-phase-2","NCTR Indel Calling from Oncopanel Sequencing Challenge Phase 2","In Phase 2, participants who completed in Phase 1 of the challenge have the ...","The high value of clinically actionable information obtained by oncopanel sequencing makes it a crucial tool for precision oncology[1,2]. With the surge in availability of oncopanels, it is critical to ensure that they have been thoroughly tested and are properly used. FDA has initiated the Sequencing Quality Control phase II (SEQC2) project[3] to develop standard analysis protocols and quality control metrics for fit-for-purpose use of Next Generation Sequencing (NGS) data including oncopanel sequencing to inform regulatory science research and precision medicine. The Oncopanel Sequencing Working Group of FDA-led SEQC2 has developed a reference sample[4] suitable for benchmarking oncopanels and comprehensively assessed the analytical performance of several oncopanels[1,2]. The genomic deoxyribonucleic acid (gDNA) reference sample was derived from ten Universal Human Reference RNA (UHRR, Agilent Technologies, Inc) cell-lines and made publicly available by Agilent. Substantial gen...","","https://precision.fda.gov/challenges/22","completed","intermediate","6","","2022-07-11","2022-07-26","2023-06-23 00:00:00","2023-10-17 23:18:17" -"151","nctr-indel-calling-from-oncopanel-sequencing-data-challenge-phase-1","NCTR Indel Calling from Oncopanel Sequencing Data Challenge Phase 1","Genetic variation involving indels (insertions and deletions) in the cancer ...","The high value of clinically actionable information obtained by oncopanel sequencing makes it a crucial tool for precision oncology[1,2]. With the surge in availability of oncopanels, it is critical to ensure that they have been thoroughly tested and are properly used. FDA has initiated the Sequencing Quality Control phase II (SEQC2) project[3] to develop standard analysis protocols and quality control metrics for fit-for-purpose use of Next Generation Sequencing (NGS) data including oncopanel sequencing to inform regulatory science research and precision medicine. The Oncopanel Sequencing Working Group of FDA-led SEQC2 has developed a reference sample[4] suitable for benchmarking oncopanels and comprehensively assessed the analytical performance of several oncopanels[1,2]. The genomic deoxyribonucleic acid (gDNA) reference sample was derived from ten Universal Human Reference RNA (UHRR, Agilent Technologies, Inc) cell-lines and made publicly available by Agilent. Substantial gen...","","https://precision.fda.gov/challenges/21","completed","intermediate","6","","2022-05-02","2022-07-08","2023-06-23 00:00:00","2023-10-17 23:18:21" -"152","vha-innovation-ecosystem-and-precisionfda-covid-19-risk-factor-modeling-challenge-phase-2","VHA Innovation Ecosystem and precisionFDA COVID-19 Risk Factor Modeling Challenge Phase 2","The focus of Phase 2 was to validate the top performing models on two additi...","The novel coronavirus disease 2019 (COVID-19) is a respiratory disease caused by a new type of coronavirus, known as “severe acute respiratory syndrome coronavirus 2,” or SARS-CoV-2. On March 11, 2020, the World Health Organization (WHO) declared the outbreak a global pandemic. As of January 22nd, 2022, the Johns Hopkins University COVID-19 dashboard reports over 338 million total confirmed cases worldwide. Although most people have mild to moderate symptoms, the disease can cause severe medical complications leading to death in some people. The Centers for Disease Control and Prevention (CDC) have identified several risk factors for severe COVID-19 illness, including people 65 years and older, individuals living in nursing homes or long-term care facilities, and those with serious underlying medical conditions. The Veteran population has a higher prevalence of several of the known risk factors for severe COVID-19 illness, such as advanced age, heart disease, and diabetes. Identif...","","https://precision.fda.gov/challenges/20","completed","intermediate","6","","2021-04-14","2022-01-28","2023-06-23 00:00:00","2023-10-14 05:40:19" -"153","tumor-mutational-burden-tmb-challenge-phase-2","Tumor Mutational Burden (TMB) Challenge Phase 2","The goal of the Friends of Cancer Research and precisionFDA Tumor Mutational...","Tumor mutational burden (TMB) is generally defined as the number of mutations detected in a patient's tumor sample per megabase of DNA sequenced. However different algorithms use different methods for calculating TMB. Mutations in genes in tumor cells may lead to the creation of neoantigens, which have the potential to activate an immune system response against the tumor, and the likelihood of an immune system response may increase with the number of mutations. Thus, TMB is a biomarker for some immunotherapy drugs, called immune checkpoint inhibitors, such as those that target the PD-1 and PD-L1 pathways (Chan et al., 2019). An outstanding problem is the lack of standardization for TMB calculation and reporting between different assays. To address this problem, the Friends of Cancer Research convened a working group of industry and regulatory stakeholders to develop guidance and tools for TMB harmonization. Results from the first phase of this effort were presented at AACR 2020 (s...","","https://precision.fda.gov/challenges/18","completed","intermediate","6","","2021-07-19","2021-09-12","2023-06-23 00:00:00","2023-10-14 05:40:20" -"154","predicting-gene-expression-using-millions-of-random-promoter-sequences","Predicting Gene Expression Using Millions of Random Promoter Sequences","","Decoding how gene expression is regulated is critical to understanding disease. Regulatory DNA is decoded by the cell in a process termed “cis-regulatory logic”, where proteins called Transcription Factors (TFs) bind to specific DNA sequences within the genome and work together to produce as output a level of gene expression for downstream adjacent genes. This process is exceedingly complex to model as a large number of parameters is needed to fully describe the process (see Rationale, de Boer et al. 2020; Zeitingler J. 2020). Understanding the cis-regulatory logic of the human genome is an important goal and would provide insight into the origins of many diseases. However, learning models from human data is challenging due to limitations in the diversity of sequences present within the human genome (e.g. extensive repetitive DNA), the vast number of cell types that differ in how they interpret regulatory DNA, limited reporter assay data, and substantial technical biases present i...","","https://www.synapse.org/#!Synapse:syn28469146/wiki/617075","completed","intermediate","1","","2022-06-15","2022-08-07","2023-06-23 00:00:00","2023-10-14 05:40:21" -"155","brats-2023","BraTS 2023","","The International Brain Tumor Segmentation (BraTS) challenge. BraTS, since 2012, has focused on the generation of a benchmarking environment and dataset for the delineation of adult brain gliomas. The focus of this year’s challenge remains the generation of a common benchmarking environment, but its dataset is substantially expanded to ~4,500 cases towards addressing additional i) populations (e.g., sub-Saharan Africa patients), ii) tumors (e.g., meningioma), iii) clinical concerns (e.g., missing data), and iv) technical considerations (e.g., augmentations). Specifically, the focus of BraTS 2023 is to identify the current state-of-the-art algorithms for addressing (Task 1) the same adult glioma population as in the RSNA-ANSR-MICCAI BraTS challenge, as well as (Task 2) the underserved sub-Saharan African brain glioma patient population, (Task 3) intracranial meningioma, (Task 4) brain metastasis, (Task 5) pediatric brain tumor patients, (Task 6) global & local missing data, (Task 7...","","https://www.synapse.org/brats","active","advanced","1","","2023-06-01","2023-08-25","2023-06-23 00:00:00","2023-10-14 05:40:21" -"156","cagi7","CAGI7","The seventh round of CAGI","There have been six editions of CAGI experiments, held between 2010 and 2022. The seventh round of CAGI is planned to take place over the Summer of 2024.","","https://genomeinterpretation.org/challenges.html","upcoming","intermediate","1","","\N","\N","2023-08-04 21:47:38","2023-10-14 05:40:32" -"157","casp15","CASP15","Establish the state-of-art in modeling proteins and protein complexes","CASP14 (2020) saw an enormous jump in the accuracy of single protein and domain models such that many are competitive with experiment. That advance is largely the result of the successful application of deep learning methods, particularly by the AlphaFold and, since that CASP, RosettaFold. As a consequence, computed protein structures are becoming much more widely used in a broadening range of applications. CASP has responded to this new landscape with a revised set of modeling categories. Some old categories have been dropped (refinement, contact prediction, and aspects of model accuracy estimation) and new ones have been added (RNA structures, protein ligand complexes, protein ensembles, and accuracy estimation for protein complexes). We are also strengthening our interactions with our partners CAPRI and CAMEO. We hope that these changes will maximize the insight that CASP15 provides, particularly in new applications of deep learning.","","https://predictioncenter.org/casp15/index.cgi","completed","intermediate","14","","2022-04-18","\N","2023-08-04 21:52:12","2023-09-28 23:09:59" -"158","synthrad2023","SynthRAD2023","Synthesizing computed tomography for radiotherapy","This challenge aims to provide the first platform offering public data evaluation metrics to compare the latest developments in sCT generation methods. The accepted challenge design approved by MICCAI can be found at https://doi.org/10.5281/zenodo.7746019. A type 2 challenge will be run, where the participant needs to submit their algorithm packaged in a docker both for validation and test.","","https://synthrad2023.grand-challenge.org/","active","intermediate","5","","2023-04-01","2023-08-22","2023-08-04 21:54:31","2023-09-28 23:12:01" -"159","synthetic-data-for-instrument-segmentation-in-surgery-syn-iss","Synthetic Data for Instrument Segmentation in Surgery (Syn-ISS)","","A common limitation noted by the surgical data science community is the size of datasets and the resources needed to generate training data at scale for building reliable and high-performing machine learning models. Beyond unsupervised and self-supervised approaches another solution within the broader machine learning community has been a growing volume of literature in the use of synthetic data (simulation) for training algorithms than can be applied to real world data. Synthetic data has multiple benefits like free groundtruth at large scale, possibility to collect larger sample of rare events, include anatomical variations, etc. A first step towards proving the validity of using synthetic data for real world applications is to demonstrate the feasibility within the simulation world itself. Our proposed challenge is to train machine learning methods for instrument segmentation using synthetic datasets and test their performance on synthetic datasets. That is, the challenge parti...","","https://www.synapse.org/#!Synapse:syn50908388/wiki/620516","active","intermediate","1","","2023-07-19","2023-09-07","2023-08-04 23:49:44","2023-10-10 19:52:16" -"160","pitvis","PitVis","Surgical workflow and instrument recognition in endonasal surgery","The pituitary gland, found just off the base of the brain, is commonly known as “the master gland”, performing essential functions required for sustaining human life. Clinically relevant tumours that have grown on the pituitary gland have an estimated prevalence of 1 in 1000 of the population, and if left untreated can be life-limiting. The “gold standard” treatment is endoscopic pituitary surgery, where the tumour is directly removed by entering through a nostril. This surgery is particularly challenging due to the small working space which limits both vision and instrument manoeuvrability and thus can lead to poor surgical technique causing adverse outcomes for the patient. Computer-assisted intervention can help overcome these challenges by providing guidance for senior surgeons and operative staff during surgery, and for junior surgeons during training.","","https://www.synapse.org/#!Synapse:syn51232283/wiki/","active","intermediate","1","","2023-06-29","2023-09-10","2023-08-04 23:58:01","2023-09-28 23:12:09" -"161","mvseg2023","MVSEG2023","Automatically segment mitral valve leaflets from single frame 3D trans-esoph...","Mitral valve (MV) disease is a common pathologic problem occurring in approximately 2 % of the general population but climbing to 10 % in those over the age of 75. The preferred intervention for mitral regurgitation is valve repair, due to superior patient outcomes compared to those following valve replacement. Mitral valve interventions are technically challenging due to the functional and anatomical complexity of mitral pathologies. Repair must be tailored to the patient-specific anatomy and pathology, which requires considerable expert training and experience. Automatic segmentation of the mitral valve leaflets from 3D transesophageal echocardiography (TEE) may play an important role in treatment planning, as well as physical and computational modelling of patient-specific valve pathologies and potential repair approaches. This may have important implications in the drive towards personalized care and has the potential to impact clinical outcomes for those undergoing mitral val...","","https://www.synapse.org/#!Synapse:syn51186045/wiki/621356","completed","intermediate","1","","2023-05-29","2023-08-07","2023-08-05 0-04-36","2023-09-28 23:12:19" -"162","crossmoda23","crossMoDA23","This challenge proposes is the third edition of the first medical imaging be...","Domain Adaptation (DA) has recently raised strong interest in the medical imaging community. By encouraging algorithms to be robust to unseen situations or different input data domains, Domain Adaptation improves the applicability of machine learning approaches to various clinical settings. While a large variety of DA techniques has been proposed, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly address single-class problems. To tackle these limitations, the crossMoDA challenge introduced the first large and multi-class dataset for unsupervised cross-modality Domain Adaptation. From an application perspective, crossMoDA focuses on MRI segmentation for Vestibular Schwannoma. Compared to the previous crossMoDA instance, which made use of multi-institutional data acquired in controlled conditions for radiosurgery planning and focused on a 2 class segmentation task (tumour and cochlea), the...","","https://www.synapse.org/#!Synapse:syn51236108/wiki/621615","completed","intermediate","1","","2023-04-15","2023-07-10","2023-08-05 0-13-23","2023-10-12 18:10:18" -"163","icr-identify-age-related-conditions","ICR - Identifying Age-Related Conditions","Use Machine Learning to detect conditions with measurements of anonymous cha...","The goal of this competition is to predict if a person has any of three medical conditions. You are being asked to predict if the person has one or more of any of the three medical conditions (Class 1), or none of the three medical conditions (Class 0). You will create a model trained on measurements of health characteristics. To determine if someone has these medical conditions requires a long and intrusive process to collect information from patients. With predictive models, we can shorten this process and keep patient details private by collecting key characteristics relative to the conditions, then encoding these characteristics.","","https://www.kaggle.com/competitions/icr-identify-age-related-conditions","completed","intermediate","8","","2023-05-11","2023-08-10","2023-08-05 0-32-01","2023-10-12 18:15:08" -"164","cafa-5-protein-function-prediction","CAFA 5: Protein Function Prediction","Predict the biological function of a protein","The goal of this competition is to predict the function of a set of proteins. You will develop a model trained on the amino-acid sequences of the proteins and on other data. Your work will help ​​researchers better understand the function of proteins, which is important for discovering how cells, tissues, and organs work. This may also aid in the development of new drugs and therapies for various diseases.","","https://www.kaggle.com/competitions/cafa-5-protein-function-prediction","completed","intermediate","8","","2023-04-18","2023-08-21","2023-08-05 5-18-40","2023-10-19 00:13:14" -"165","rsna-2023-abdominal-trauma-detection","RSNA 2023 Abdominal Trauma Detection","Detect and classify traumatic abdominal injuries","Traumatic injury is the most common cause of death in the first four decades of life and a major public health problem around the world. There are estimated to be more than 5 million annual deaths worldwide from traumatic injury. Prompt and accurate diagnosis of traumatic injuries is crucial for initiating appropriate and timely interventions, which can significantly improve patient outcomes and survival rates. Computed tomography (CT) has become an indispensable tool in evaluating patients with suspected abdominal injuries due to its ability to provide detailed cross-sectional images of the abdomen. Interpreting CT scans for abdominal trauma, however, can be a complex and time-consuming task, especially when multiple injuries or areas of subtle active bleeding are present. This challenge seeks to harness the power of artificial intelligence and machine learning to assist medical professionals in rapidly and precisely detecting injuries and grading their severity. The development...","","https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection","completed","intermediate","8","","2023-07-26","2023-10-13","2023-08-05 5-24-09","2023-09-28 23:14:12" -"166","hubmap-hacking-the-human-vasculature","HuBMAP: Hacking the Human Vasculature","Segment instances of microvascular structures from healthy human kidney tiss...","The goal of this competition is to segment instances of microvascular structures, including capillaries, arterioles, and venules. You'll create a model trained on 2D PAS-stained histology images from healthy human kidney tissue slides. Your help in automating the segmentation of microvasculature structures will improve researchers' understanding of how the blood vessels are arranged in human tissues.","","https://www.kaggle.com/competitions/hubmap-hacking-the-human-vasculature","completed","intermediate","8","","2023-05-22","2023-07-31","2023-08-05 5-31-12","2023-10-12 18:15:00" -"167","amp-parkinsons-disease-progression-prediction","AMP(R)-Parkinson's Disease Progression Prediction","Use protein and peptide data measurements from Parkinson's Disease patients ...","The goal of this competition is to predict MDS-UPDR scores, which measure progression in patients with Parkinson's disease. The Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is a comprehensive assessment of both motor and non-motor symptoms associated with Parkinson's. You will develop a model trained on data of protein and peptide levels over time in subjects with Parkinson’s disease versus normal age-matched control subjects. Your work could help provide important breakthrough information about which molecules change as Parkinson’s disease progresses.","","https://www.kaggle.com/competitions/amp-parkinsons-disease-progression-prediction","completed","intermediate","8","","2023-02-16","2023-05-18","2023-08-05 5-37-12","2023-10-10 19:52:34" -"168","open-problems-multimodal","Open Problems -Multimodal Single-Cell Integration","Predict how DNA, RNA & protein measurements co-vary in single cells","The goal of this competition is to predict how DNA, RNA, and protein measurements co-vary in single cells as bone marrow stem cells develop into more mature blood cells. You will develop a model trained on a subset of 300,000-cell time course dataset of CD34+ hematopoietic stem and progenitor cells (HSPC) from four human donors at five time points generated for this competition by Cellarity, a cell-centric drug creation company. In the test set, taken from an unseen later time point in the dataset, competitors will be provided with one modality and be tasked with predicting a paired modality measured in the same cell. The added challenge of this competition is that the test data will be from a later time point than any time point in the training data. Your work will help accelerate innovation in methods of mapping genetic information across layers of cellular state. If we can predict one modality from another, we may expand our understanding of the rules governing these complex re...","","https://www.kaggle.com/competitions/open-problems-multimodal","completed","intermediate","8","","2022-08-15","2022-11-15","2023-08-05 5-43-25","2023-10-10 19:52:41" -"169","multi-atlas-labeling-beyond-the-cranial-vault","Multi-Atlas Labeling Beyond the Cranial Vault","","Multi-atlas labeling has proven to be an effective paradigm for creating segmentation algorithms from training data. These approaches have been extraordinarily successful for brain and cranial structures (e.g., our prior MICCAI workshops-MLSF’11, MAL’12, SATA’13). After the original challenges closed, the data continue to drive scientific innovation; 144 groups have registered for the 2012 challenge (brain only) and 115 groups for the 2013 challenge (brain/heart/canine leg). However, innovation in application outside of the head and to soft tissues has been more limited. This workshop will provide a snapshot of the current progress in the field through extended discussions and provide researchers an opportunity to characterize their methods on a newly created and released standardized dataset of abdominal anatomy on clinically acquired CT. The datasets will be freely available both during and after the challenge. We have two separate new challenges-abdomen and cervix on routinely ...","","https://www.synapse.org/#!Synapse:syn3193805/wiki/89480","active","intermediate","1","","2015-04-15","\N","2023-08-07 20:21:22","2023-10-10 19:52:39" -"170","hubmap-organ-segmentation","HuBMAP + HPA: Hacking the Human Body","Segment multi-organ functional tissue units","In this competition, you’ll identify and segment functional tissue units (FTUs) across five human organs. You'll build your model using a dataset of tissue section images, with the best submissions segmenting FTUs as accurately as possible. If successful, you'll help accelerate the world’s understanding of the relationships between cell and tissue organization. With a better idea of the relationship of cells, researchers will have more insight into the function of cells that impact human health. Further, the Human Reference Atlas constructed by HuBMAP will be freely available for use by researchers and pharmaceutical companies alike, potentially improving and prolonging human life.","","https://www.kaggle.com/competitions/hubmap-organ-segmentation","completed","intermediate","8","","2022-06-22","2022-09-22","2023-08-08 16:30:22","2023-10-12 18:14:20" -"171","hubmap-kidney-segmentation","HuBMAP: Hacking the Kidney","Identify glomeruli in human kidney tissue images","This competition, “Hacking the Kidney, starts by mapping the human kidney at single cell resolution. Your challenge is to detect functional tissue units (FTUs) across different tissue preparation pipelines. An FTU is defined as a “three-dimensional block of cells centered around a capillary, such that each cell in this block is within diffusion distance from any other cell in the same block” ([de Bono, 2013](https://www.ncbi.nlm.nih.gov/pubmed/24103658)). The goal of this competition is the implementation of a successful and robust glomeruli FTU detector. You will also have the opportunity to present your findings to a panel of judges for additional consideration. Successful submissions will construct the tools, resources, and cell atlases needed to determine how the relationships between cells can affect the health of an individual. Advancements in HuBMAP will accelerate the world’s understanding of the relationships between cell and tissue organization and function and human health.","","https://www.kaggle.com/competitions/hubmap-kidney-segmentation","completed","intermediate","8","","2020-11-16","2021-05-10","2023-08-08 17:31:46","2023-10-12 18:14:16" -"172","ventilator-pressure-prediction","Google Brain: Ventilator Pressure Prediction","Simulate a ventilator connected to a sedated patient's lung","In this competition, you’ll simulate a ventilator connected to a sedated patient's lung. The best submissions will take lung attributes compliance and resistance into account. If successful, you'll help overcome the cost barrier of developing new methods for controlling mechanical ventilators. This will pave the way for algorithms that adapt to patients and reduce the burden on clinicians during these novel times and beyond. As a result, ventilator treatments may become more widely available to help patients breathe.","","https://www.kaggle.com/competitions/ventilator-pressure-prediction","completed","intermediate","8","","2021-09-22","2021-11-03","2023-08-08 17:53:33","2023-10-12 18:14:11" -"173","stanford-covid-vaccine","OpenVaccine - COVID-19 mRNA Vaccine Degradation Prediction","Urgent need to bring the COVID-19 vaccine to mass production","In this competition, we are looking to leverage the data science expertise of the Kaggle community to develop models and design rules for RNA degradation. Your model will predict likely degradation rates at each base of an RNA molecule, trained on a subset of an Eterna dataset comprising over 3000 RNA molecules (which span a panoply of sequences and structures) and their degradation rates at each position. We will then score your models on a second generation of RNA sequences that have just been devised by Eterna players for COVID-19 mRNA vaccines. These final test sequences are currently being synthesized and experimentally characterized at Stanford University in parallel to your modeling efforts--Nature will score your models!","","https://www.kaggle.com/competitions/stanford-covid-vaccine","completed","intermediate","8","","2020-09-10","2020-10-06","2023-08-08 18:06:17","2023-10-12 18:14:27" -"174","openvaccine","OpenVaccine","To develop mRNA vaccines stable enough to be deployed to everyone in the wor...","mRNA vaccines are a relatively new technology that have come into the limelight with the onset of COVID-19. They were the first COVID-19 vaccines to start clinical trials (initially formulated in a matter of days) and the first to be approved and distributed. mRNA vaccines have the potential to transform immunization, being significantly faster to formulate and produce, cheaper, and more effective-including against mutant strains. However, there is one key bottleneck to their widespread viability and our ability to immunize the entire world-poor refrigerator stability in prefilled syringes. The OpenVaccine challenge aims to allow a worldwide community of game players to create an enhanced vaccine to be injected into millions of people. The challenge-design an mRNA that codes for the same amino acid sequence of the spike protein, but is 2x-10x+ more stable. Through a number of academic partnerships and the launch of a Kaggle machine learning challenge to create best-in-class algori...","","https://eternagame.org/challenges/10845741","completed","intermediate","13","https://doi.org/10.1038/s41467-022-28776-w","\N","2021-12-12","2023-08-08 18:22:49","2023-09-28 23:17:02" -"175","opentb","OpenTB","What if we could use RNA to detect a gene sequence found to be present only ...","OpenTB used a recently reported gene signature for active tuberculosis based on three RNAs in the blood. This signature could form the basis for a fast, color-based test for TB, similar to an over-the-counter pregnancy test. What was needed was a sensor that could detect the concentrations of three RNAs, carry out the needed calculation, and report the result by binding another molecule. Over four rounds, players designed RNA sensors that can do the math on these 3 genes. Through experimental feedback, they honed their skills and techniques, which resulted in the creation of multiple designs that have been shown to be successful. These findings are being prepared to be published, and future work will be done to develop diagnostic devices integrating these designs","","https://eternagame.org/challenges/10845742","completed","intermediate","13","","2016-05-04","2018-04-15","2023-08-08 18:43:09","2023-09-28 23:17:09" -"176","opencrispr","OpenCRISPR","A project to discover design patterns for guide RNAs to make gene editing mo...","CRISPR gene editing is a RNA-based method that can target essentially any gene in a living organism for genetic changes. Since its first demonstration, CRISPR has been revolutionizing biology and promises to change how we tackle numerous human diseases from malaria to cancer. Stanford's Center for Personal Dynamic Regulomes and UC Berkeley's Innovative Genomics Institute have challenged Eterna players to solve a remaining hurdle in making this technology safe for use. Scientists want the power to turn on and off CRISPR on demand with small molecules. This is almost a perfect match to the small-molecule switches that the Eterna community has worked on. In fact, the MS2 RNA hairpin often used in Eterna is routinely used to recruit new functionality to CRISPR complexes through other molecules tethered to the MS2 protein. The puzzles began with OpenCRISPR Controls, looking for solutions to lock in or lock out the MS2 RNA hairpin within a special loop in the CRISPR RNA. We hope the res...","","https://eternagame.org/challenges/10845743","completed","intermediate","13","https://doi.org/10.1021/acssynbio.9b00142","2017-08-26","\N","2023-08-08 18:43:14","2023-10-10 19:57:07" -"177","openknot","OpenKnot","Many important biological processes depend on RNAs that form pseudoknots, an...","RNA pseudoknots have significant biological importance in various processes. They participate in gene regulation by influencing translation initiation or termination in mRNA molecules. Pseudoknots also play a role in programmed ribosomal frameshifting, leading to the production of different protein products from a single mRNA. RNA viruses, including SARS-CoV-2 and Dengue virus, utilize pseudoknots to regulate their replication and control the synthesis of viral proteins. Additionally, certain RNA molecules with pseudoknot structures exhibit enzymatic activity, acting as ribozymes and catalyzing biochemical reactions. These functions highlight the crucial role of RNA pseudoknots in gene expression, proteomic diversity, viral replication, and enzymatic processes. Several unanswered scientific questions surround RNA pseudoknots. One key area of inquiry is understanding the folding pathways of pseudoknots and how they form from linear RNA sequences. Elucidating the structural dynamics...","","https://eternagame.org/challenges/11843006","active","intermediate","13","","2022-06-17","\N","2023-08-08 18:43:22","2023-10-10 19:52:53" -"178","openaso","OpenASO","A research initiative aimed at developing innovative design principles for R...","The DNA genome is the blueprint for building and operating cells, but this information must be decoded into RNA molecules to be useful. Transcription is the process of decoding DNA genomic information into RNA, resulting in RNA transcripts. Genes are specific sequences of DNA that contain information to produce a specific RNA transcript. The fate of most mRNA molecules in the cell is to be translated by ribosomes into protein molecules. However, mRNA splicing is a crucial step that occurs between the formation of an RNA transcript and protein translation. This step is essential because genes contain non-protein coding introns and protein-coding exons. Splicing removes introns and joins exons to produce a mature mRNA molecule that can be decoded into the correct protein molecule. When the splicing process is corrupted due to genetic mutations, the resulting RNA can become toxic, leading to the synthesis of non-functional proteins or no protein at all, causing various human diseases...","","https://eternagame.org/challenges/11546273","active","intermediate","13","","2023-02-20","\N","2023-08-08 18:43:25","2023-10-10 19:52:57" -"179","openribosome","OpenRibosome","We aim to 1) gain fundamental insights into the ribosome's RNA sequence-fold...","Our modern world has many challenges-challenges like climate change, increasing waste production, and human health. Imagine-we could replace petrochemistry with biology, single-use plastics with selectively degradable polymers, broad chemotherapeutics with targeted medicines for fighting specific cancer cells, and complex health equipment with point-of-care diagnostics. These innovations and many more can empower us to confront the challenges affecting humanity, our world, and beyond. But how do we actually create these smart materials and medicines? Is it possible to do so by repurposing one of Nature's molecular machines? We think we can. The answer? Customized ribosomes. In Nature, ribosomes are the catalysts for protein assembly. And proteins are more or less similar, chemically, to the smart materials and medicines we want to synthesize. If we could modify ribosomes to build polymers with diverse components-beyond the canonical amino acids us","","https://eternagame.org/challenges/11043833","active","intermediate","13","https://doi.org/10.1038/s41467-023-35827-3","2019-01-31","\N","2023-08-08 18:43:27","2023-10-10 19:53:01" -"180","lish-moa","Mechanisms of Action (MoA) Prediction","Can you improve the algorithm that classifies drugs based on their biologica...","Can you improve the algorithm that classifies drugs based on their biological activity?","","https://www.kaggle.com/competitions/lish-moa","completed","intermediate","8","","2020-09-03","2020-11-30","2023-08-08 19:09:31","2023-09-28 23:18:04" -"181","recursion-cellular-image-classification","Recursion Cellular Image Classification","CellSignal-Disentangling biological signal from experimental noise in cellul...","This competition will have you disentangling experimental noise from real biological signals. Your entry will classify images of cells under one of 1,108 different genetic perturbations. You can help eliminate the noise introduced by technical execution and environmental variation between experiments. If successful, you could dramatically improve the industry’s ability to model cellular images according to their relevant biology. In turn, applying AI could greatly decrease the cost of treatments, and ensure these treatments get to patients faster.","","https://www.kaggle.com/competitions/recursion-cellular-image-classification","completed","intermediate","8","","2019-06-27","2019-09-26","2023-08-08 19:38:42","2023-10-10 19:53:05" -"182","tlvmc-parkinsons-freezing-gait-prediction","Parkinson's Freezing of Gait Prediction","Event detection from wearable sensor data","The goal of this competition is to detect freezing of gait (FOG), a debilitating symptom that afflicts many people with Parkinson’s disease. You will develop a machine learning model trained on data collected from a wearable 3D lower back sensor. Your work will help researchers better understand when and why FOG episodes occur. This will improve the ability of medical professionals to optimally evaluate, monitor, and ultimately, prevent FOG events.","","https://www.kaggle.com/competitions/tlvmc-parkinsons-freezing-gait-prediction","completed","intermediate","8","","2023-03-09","2023-06-08","2023-08-08 19:47:54","2023-10-10 19:53:08" -"183","chaimeleon","CHAIMELEON Open Challenges","","The CHAIMELEON Open Challenges is a competition designed to train and refine AI models to answer clinical questions about five types of cancer-prostate, lung, breast, colon, and rectal. Participants are challenged to collaborate and develop innovative AI-powered solutions that can significantly impact cancer diagnosis, management, and treatment. They will be evaluated considering a balance between the performance of their AI algorithms to predict different clinical endpoints such as disease staging, treatment response or progression free survival and their trustworthiness. The challenges are open to the whole scientific and tech community interested in AI. They are a unique opportunity to showcase how AI can be used to advance medical research and improve patient outcomes within the CHAIMELEON project.","","https://chaimeleon.grand-challenge.org/","upcoming","intermediate","5","","\N","2023-12-31","2023-08-09 17:13:09","2023-10-10 19:53:10" -"184","topcow23","Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA","","The aim of the challenge is to extract the CoW angio-architecture from 3D angiographic imaging by segmentation of the vessel components. There are two sub-tasks-binary segmentation of CoW vessels, and multi-class CoW anatomical segmentation. We release a new dataset of joint-modalities, CTA and MRA of the same patient cohort, both with annotations of the anatomy of CoW. Our challenge has two tracks for the same segmentation task, namely CTA track and MRA track. We made use of the clinical information from both modalities during our annotation. And participants can pick whichever modality they want, both CTA and MRA, and choose to tackle the task for either modality.","","https://topcow23.grand-challenge.org/","completed","intermediate","5","","2023-08-20","2023-09-25","2023-08-09 17:16:22","2023-09-28 23:24:41" -"185","circle-of-willis-intracranial-artery-classification-and-quantification-challenge-2023","Circle of Willis Intracranial Artery Classification and Quantification Challenge 2023","","The purpose of this challenge is to compare automatic methods for classification of the circle of Willis (CoW) configuration and quantification of the CoW major artery diameters and bifurcation angles.","","https://crown.isi.uu.nl/","completed","intermediate","14","","2023-05-01","2023-08-15","2023-08-09 22:13:24","2023-09-28 23:24:54" -"186","making-sense-of-electronic-health-record-ehr-race-and-ethnicity-data","Making Sense of Electronic Health Record (EHR) Race and Ethnicity Data","The US Food and Drug Administration (FDA) calls on stakeholders, including t...","The urgency of the coronavirus disease 2019 (COVID-19) pandemic has heightened interest in the use of real-world data (RWD) to obtain timely information about patients and populations and has focused attention on EHRs. The pandemic has also heightened awareness of long-standing racial and ethnic health disparities along a continuum from underlying social determinants of health, exposure to risk, access to insurance and care, quality of care, and responses to treatments. This highlighted the potential that EHRs can be used to describe and contribute to our understanding of racial and ethnic health disparities and their solutions. The OMB Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity provides minimum standards for maintaining, collecting, and presenting data on race and ethnicity for all Federal reporting purposes, and defines the two separate constructs of race and ethnicity.","","https://precision.fda.gov/challenges/30","completed","intermediate","6","","2023-05-31","2023-06-23","2023-08-10 18:28:06","2023-10-10 19:53:12" -"187","the-veterans-cardiac-health-and-ai-model-predictions-v-champs","The Veterans Cardiac Health and AI Model Predictions (V-CHAMPS)","The Veterans Health Administration Innovation Ecosystem, the Digital Health ...","To better understand the risk and protective factors in the Veteran population, the VHA IE and its collaborating partners are calling upon the public to develop AI/ML models to predict cardiovascular health outcomes, including readmission and mortality, using synthetically generated Veteran health records. The Challenge consists of two Phases-Phase 1 is focused on synthetic data. In this Phase of the Challenge, AI/ML models will be developed by Challenge participants and trained and tested on the synthetic data sets provided to them, with a view towards predicting outcome variables for Veterans who have been diagnosed with chronic heart failure (please note that in Phase 1, the data is synthetic Veteran health records). Phase 2 will focus on validating and further exploring the limits of the AI/ML models. During this Phase, high-performing AI/ML models from Phase 1 will be brought into the VA system and validated on the real-world Veterans health data within the VHA. These models...","","https://precision.fda.gov/challenges/31","completed","intermediate","6","","2023-05-25","2023-08-02","2023-08-10 21:41:10","2023-09-28 23:25:45" -"188","predicting-high-risk-breast-cancer-phase-1","Predicting High Risk Breast Cancer - Phase 1","Predicting High Risk Breast Cancer-a Nightingale OS & AHLI data challenge","Every year, 40 million women get a mammogram; some go on to have an invasive biopsy to better examine a concerning area. Underneath these routine tests lies a deep—and disturbing—mystery. Since the 1990s, we have found far more ‘cancers’, which has in turn prompted vastly more surgical procedures and chemotherapy. But death rates from metastatic breast cancer have hardly changed. When a pathologist looks at a biopsy slide, she is looking for known signs of cancer-tubules, cells with atypical looking nuclei, evidence of rapid cell division. These features, first identified in 1928, still underlie critical decisions today-which women must receive urgent treatment with surgery and chemotherapy? And which can be prescribed “watchful waiting”, sparing them invasive procedures for cancers that would not harm them? There is already evidence that algorithms can predict which cancers will metastasize and harm patients on the basis of the biopsy image. Fascinatingly, these algorithms also h...","","https://app.nightingalescience.org/contests/3jmp2y128nxd","completed","intermediate","15","","2022-06-01","2023-01-12","2023-08-22 17:07:00","2023-10-12 17:55:10" -"189","predicting-high-risk-breast-cancer-phase-2","Predicting High Risk Breast Cancer - Phase 2","Predicting High Risk Breast Cancer-a Nightingale OS & AHLI data challenge","Every year, 40 million women get a mammogram; some go on to have an invasive biopsy to better examine a concerning area. Underneath these routine tests lies a deep—and disturbing—mystery. Since the 1990s, we have found far more ‘cancers’, which has in turn prompted vastly more surgical procedures and chemotherapy. But death rates from metastatic breast cancer have hardly changed. When a pathologist looks at a biopsy slide, she is looking for known signs of cancer-tubules, cells with atypical looking nuclei, evidence of rapid cell division. These features, first identified in 1928, still underlie critical decisions today-which women must receive urgent treatment with surgery and chemotherapy? And which can be prescribed “watchful waiting”, sparing them invasive procedures for cancers that would not harm them? There is already evidence that algorithms can predict which cancers will metastasize and harm patients on the basis of the biopsy image. Fascinatingly, these algorithms also...","","https://app.nightingalescience.org/contests/vd8g98zv9w0p","completed","intermediate","15","","2023-02-03","2023-05-13","2023-08-22 17:07:01","2023-10-12 17:55:08" -"190","dream-2-in-silico-network-inference","DREAM 2 - In Silico Network Inference","Predicting the connectivity and properties of in-silico networks.","Three in-silico networks were created and endowed with a dynamics that simulate biological interactions. The challenge consists of predicting the connectivity and some of the properties of one or more of these three networks.","","https://www.synapse.org/#!Synapse:syn2825394/wiki/71150","completed","intermediate","1","","2007-03-25","\N","2023-08-24 18:54:05","2023-10-12 17:55:03" -"191","dream-3-in-silico-network-challenge","DREAM 3 - In Silico Network Challenge","The goal of the in silico challenges is the reverse engineering of gene netw...","The goal of the in silico challenges is the reverse engineering of gene networks from steady state and time series data. Participants are challenged to predict the directed unsigned network topology from the given in silico generated gene topic_3170sets.","","https://www.synapse.org/#!Synapse:syn2853594/wiki/71567","completed","intermediate","1","https://doi.org/10.1089/cmb.2008.09TT","2008-06-09","\N","2023-08-25 16:43:41","2023-10-12 17:55:02" -"192","dream-4-in-silico-network-challenge","DREAM 4 - In Silico Network Challenge","The goal of the in silico network challenge is to reverse engineer gene regu...","The goal of the in silico network challenge is to reverse engineer gene regulation networks from simulated steady-state and time-series data. Participants are challenged to infer the network structure from the given in silico gene topic_3170sets. Optionally, participants may also predict the response of the networks to a set of novel perturbations that were not included in the provided datasets.","","https://www.synapse.org/#!Synapse:syn3049712/wiki/74628","completed","intermediate","1","https://doi.org/10.1073/pnas.0913357107","2009-06-09","\N","2023-08-25 16:43:42","2023-10-12 17:55:00" -"193","dream-5-network-inference-challenge","DREAM 5 - Network Inference Challenge","The goal of this Network Inference Challenge is to reverse engineer gene reg...","The goal of this Network Inference Challenge is to reverse engineer gene regulatory networks from gene topic_3170sets. Participants are given four microarray compendia and are challenged to infer the structure of the underlying transcriptional regulatory networks. Three of the four compendia were obtained from microorganisms, some of which are pathogens of clinical relevance. The fourth compendium is based on an in-silico (i.e., simulated) network. Each compendium consists of hundreds of microarray experiments, which include a wide range of genetic, drug, and environmental perturbations (or in the in-silico network case, simulations thereof). Network predictions will be evaluated on a subset of known interactions for each organism, or on the known network for the in-silico case.","","https://www.synapse.org/#!Synapse:syn2787209/wiki/70349","completed","intermediate","1","https://doi.org/10.1038/nmeth.2016","2010-06-09","2010-10-31","2023-08-25 16:43:43","2023-10-12 17:54:57" -"194","nlp-sandbox-date-annotation","NLP Sandbox Date Annotation","Identify dates in clinical notes.","An NLP Sandbox Date Annotator takes as input a clinical note and outputs a list of predicted date annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","intermediate","1","https://doi.org/10.7303/syn22277123","2021-06-04","2023-09-01","2023-08-25 16:45:22","2023-09-28 23:59:02" -"195","nlp-sandbox-person-name-annotation","NLP Sandbox Person Name Annotation","Identify person names in clinical notes.","An NLP Sandbox Person Name Annotator takes as input a clinical note and outputs a list of predicted person name annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","intermediate","1","https://doi.org/10.7303/syn22277123","2021-06-04","2023-09-01","2023-09-08 16:44:20","2023-09-28 23:59:20" -"196","nlp-sandbox-location-annotation","NLP Sandbox Location Annotation","Identify location information in clinical notes.","An NLP Sandbox Location Annotator takes as input a clinical note and outputs a list of predicted location annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","intermediate","1","https://doi.org/10.7303/syn22277123","2021-06-04","2023-09-01","2023-09-08 16:44:21","2023-09-28 23:59:21" -"197","nlp-sandbox-contact-annotation","NLP Sandbox Contact Annotation","Identify contact information in clinical notes.","An NLP Sandbox contact annotator takes as input a clinical note and outputs a list of predicted contact annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","intermediate","1","https://doi.org/10.7303/syn22277123","2021-06-04","2023-09-01","2023-09-08 16:44:22","2023-09-28 23:59:21" -"198","nlp-sandbox-id-annotation","NLP Sandbox ID Annotation","Identify identifiers in clinical notes.","An NLP Sandbox ID annotator takes as input a clinical note and outputs a list of predicted ID annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","intermediate","1","https://doi.org/10.7303/syn22277123","2021-06-04","2023-09-01","2023-09-08 16:44:22","2023-09-28 23:59:22" -"199","dream-2-bcl6-transcriptomic-target-prediction","DREAM 2 - BCL6 Transcriptomic Target Prediction","","A number of potential transcriptional targets of BCL6, a gene that encodes for a transcription factor active in B cells, have been identified with ChIP-on-chip data and functionally validated by perturbing the BCL6 pathway with CD40 and anti-IgM, and by over-expressing exogenous BCL6 in Ramos cell. We subselected a number of targets found in this way (the gold standard positive set), and added a number decoys (genes that have no evidence of being BCL6 targets, named the gold standard negative set), compiling a list of 200 genes in total. Given this list of 200 genes, the challenge consists of identifying which ones are the true targets and which ones are the decoys, using an independent panel of gene topic_3170.","","https://www.synapse.org/#!Synapse:syn3034857/wiki/","completed","intermediate","1","https://doi.org/10.1073/pnas.0437996100","2007-04-19","\N","2023-09-12 21:26:22","2023-10-12 17:53:55" -"200","dream-2-protein-protein-interaction-network-inference","DREAM 2 - Protein-Protein Interaction Network Inference","Predict a PPI network of 47 proteins","For many pairs of bait and prey genes, yeast protein-protein interactions were tested in an unbiased fashion using a high saturation, high-stringency variant of the yeast two-hybrid (Y2H) method. A high confidence subset of gene pairs that were found to interact in at least three repetitions of the experiment but that hadn’t been reported in the literature was extracted. There were 47 yeast genes involved in these pairs. Including self interactions, there are a total of 47*48/2 possible pairs of genes that can be formed with these 47 genes. As mentioned above some of these gene pairs were seen to consistently interact in at least three repetitions of the Y2H experiments-these gene pairs form the gold standard positive set. A second set among these gene pairs were seen never to interact in repeated experiments and were not reported as interacting in the literature; we call this the gold standard negative set. Finally in a third set of gene pairs, which we shall call the undecided s...","","https://www.synapse.org/#!Synapse:syn2825374/wiki/","completed","intermediate","1","https://doi.org/10.1126/science.1158684","2007-05-24","\N","2023-09-12 21:26:28","2023-10-12 17:54:00" -"201","dream-2-genome-scale-network-inference","DREAM 2 - Genome-Scale Network Inference","","A panel of single-channel microarrays was collected for a particular microorganism, including some already published and some in-print data. The data was appropriately normalized (to the logarithmic scale). The challenge consists of reconstructing a genome-scale transcriptional network for this organism. The accuracy of network inference will be judged using chromatin precipitation and otherwise experimentally verified Transcription Factor (TF)-target interactions.","","https://www.synapse.org/#!Synapse:syn3034894/wiki/74418","completed","intermediate","1","https://doi.org/10.1371/journal.pbio.0050008","2007-06-05","2007-10-31","2023-09-12 21:26:34","2023-10-12 17:54:03" -"202","dream-2-synthetic-five-gene-network-inference","DREAM 2 - Synthetic Five-Gene Network Inference","","A synthetic-biology network consisting of 5 interacting genes was created and transfected to an in-vivo model organism. The challenge consists of predicting the connectivity of the five-gene network from in-vivo measurements.","","https://www.synapse.org/#!Synapse:syn3034869/wiki/74411","completed","intermediate","1","https://doi.org/10.1016/j.cell.2009.01.055","2007-06-20","2007-10-31","2023-09-12 21:26:56","2023-10-12 17:54:05" -"203","dream-3-signaling-cascade-identification","DREAM 3 - Signaling Cascade Identification","","The concentration of four intracellular proteins or phospho-proteins (X1, X2, X3 and X4) participating in a signaling cascade were measured in about 104 cells by antibody staining and flow cytometry. The idea of this challenge is to explore what key aspects of the dynamics and topology of interactions of a signaling cascade can be inferred from incomplete flow cytometry data.","","https://www.synapse.org/#!Synapse:syn3033068/wiki/74362","completed","intermediate","1","","2008-06-01","2008-10-31","2023-09-12 21:27:04","2023-10-12 17:54:08" -"204","dream-3-gene-expression-prediction","DREAM 3 - Gene Expression Prediction","","Gene expression time course data is provided for four different strains of yeast (S. Cerevisiae), after perturbation of the cells. The challenge is to predict the rank order of induction/repression of a small subset of genes (the prediction targets) in one of the four strains, given complete data for three of the strains, and data for all genes except the prediction targets in the other strain. You are also allowed to use any information that is in the public domain and are expected to be forthcoming about what information was used.","","https://www.synapse.org/#!Synapse:syn3033083/wiki/74369","completed","intermediate","1","","2008-06-01","2008-10-31","2023-09-12 21:27:12","2023-10-12 17:54:10" -"205","dream-4-predictive-signaling-network-modelling","DREAM 4 - Predictive Signaling Network Modelling","Cell-type specific high-throughput experimental data","This challenge explores the extent to which our current knowledge of signaling pathways, collected from a variety of cell types, agrees with cell-type specific high-throughput experimental data. Specifically, we ask the challenge participants to create a cell-type specific model of signal transduction using the measured activity levels of signaling proteins in HepG2 cell lines. The model, which can leverage prior information encoded in a generic signaling pathway provided in the challenge, should be biologically interpretable as a network, and capable of predicting the outcome of new experiments.","","https://www.synapse.org/#!Synapse:syn2825304/wiki/71129","completed","intermediate","1","","2009-03-09","\N","2023-09-12 21:27:14","2023-10-12 17:54:30" -"206","dream-3-signaling-response-prediction","DREAM 3 - Signaling Response Prediction","Predict missing protein concentrations from a large corpus of measurements","Approximately 10,000 intracellular measurements (fluorescence signals proportional to the concentrations of phosphorylated proteins) and extracellular measurements (concentrations of cytokines released in response to cell stimulation) were acquired in human normal hepatocytes and the hepatocellular carcinoma cell line HepG2 cells. The datasets consist of measurements of 17 phospho-proteins (at 0 min, 30 min, and 3 hrs) and 20 cytokines (at 0 min, 3 hrs, and 24 hrs) in two cell types (normal and cancer) after perturbations to the pathway induced by the combinatorial treatment of 7 stimuli and 7 selective inhibitors.","","https://www.synapse.org/#!Synapse:syn2825325/wiki/","completed","intermediate","1","https://doi.org/10.1126%2Fscisignal.2002212","2009-03-09","\N","2023-09-12 21:27:20","2023-10-12 17:54:33" -"207","dream-4-peptide-recognition-domain-prd-specificity-prediction","DREAM 4 - Peptide Recognition Domain (PRD) Specificity Prediction","","Many important protein-protein interactions are mediated by peptide recognition domains (PRD), which bind short linear sequence motifs in other proteins. For example, SH3 domains typically recognize proline-rich motifs, PDZ domains recognize hydrophobic C-terminal tails, and kinases recognize short sequence regions around a phosphorylatable residue (Pawson, 2003). Given the sequence of the domains, the challenge consists of predicting a position weight matrix (PWM) that describes the specificity profile of each of the given domains to their target peptides. Any publicly accessible peptide specificity information available for the domain may be used.","","https://www.synapse.org/#!Synapse:syn2925957/wiki/72976","completed","intermediate","1","","2009-06-01","2009-10-31","2023-09-12 21:27:35","2023-10-12 17:54:35" -"208","dream-5-transcription-factor-dna-motif-recognition-challenge","DREAM 5 - Transcription-Factor, DNA-Motif Recognition Challenge","","Transcription factors (TFs) control the expression of genes through sequence-specific interactions with genomic DNA. Different TFs bind preferentially to different sequences, with the majority recognizing short (6-12 base), degenerate ‘motifs’. Modeling the sequence specificities of TFs is a central problem in understanding the function and evolution of the genome, because many types of genomic analyses involve scanning for potential TF binding sites. Models of TF binding specificity are also important for understanding the function and evolution of the TFs themselves. The challenge consists of predicting the signal intensities for the remaining TFs.","","https://www.synapse.org/#!Synapse:syn2887863/wiki/72185","completed","intermediate","1","https://doi.org/10.1038/nbt.2486","2011-06-01","2011-09-30","2023-09-12 21:27:41","2023-10-12 17:54:36" -"209","dream-5-epitope-antibody-recognition-ear-challenge","DREAM 5 - Epitope-Antibody Recognition (EAR) Challenge","Predict the binding specificity of peptide-antibody interactions.","Humoral immune responses are mediated through antibodies. About 1010 to 1012 different antigen binding sites called paratopes are generated by genomic recombination. These antibodies are capable to bind to a variety of structures ranging from small molecules to protein complexes, including any posttranslational modification thereof. When studying protein-antibody interactions, two types of epitopes (the region paratopes interact with) are to be distinguished from each other-i) conformational and ii) linear epitopes. All potential linear epitopes of a protein can be represented by short peptides derived from the primary amino acid sequence. These peptides can be synthesized and arrayed on solid supports, e.g. glass slides (see Lorenz et al., 2009 [1]). By incubating these peptide arrays with antibody mixtures such as human serum or plasma, peptides can be determined that interact with antibodies in a specific fashion.","","https://www.synapse.org/#!Synapse:syn2820433/wiki/71017","completed","intermediate","1","","2010-06-09","\N","2023-09-12 21:27:44","2023-10-12 17:54:39" -"210","dream-gene-expression-prediction-challenge","DREAM Gene Expression Prediction Challenge","Predict gene expression levels from promoter sequences in eukaryotes","The level by which genes are transcribed is determined in large part by the DNA sequence upstream to the gene, known as the promoter region. Although widely studied, we are still far from a quantitative and predictive understanding of how transcriptional regulation is encoded in gene promoters. One obstacle in the field is obtaining accurate measurements of transcription derived by different promoters. To address this, an experimental system was designed to measure the transcription derived by different promoters, all of which are inserted into the same genomic location upstream to a reporter gene -a yellow florescence protein gene (YFP). The challenge consists of the prediction of the promoter activity given a promoter sequence and a specific experimental condition. To study a set of promoters that share many elements of the regulatory program, and thus are suitable for computational learning, the data pertains to promoters of most of the ribosomal protein genes (RP) of yeast (S....","","https://www.synapse.org/#!Synapse:syn2820426/wiki/71010","completed","intermediate","1","","2010-07-09","\N","2023-09-12 21:28:00","2023-10-19 23:32:10" -"211","dream-5-systems-genetics-challenge","DREAM 5 - Systems Genetics Challenge","Predict disease phenotypes and infer Gene Networks from Systems Genetics data","The central goal of systems biology is to gain a predictive, system-level understanding of biological networks. This can be done, for example, by inferring causal networks from observations on a perturbed biological system. An ideal experimental design for causal inference is randomized, multifactorial perturbation. The recognition that the genetic variation in a segregating population represents randomized, multifactorial perturbations (Jansen and Nap (2001), Jansen (2003)) gave rise to Systems Genetics (SG), where a segregating or genetically randomized population is genotyped for many DNA variants, and profiled for phenotypes of interest (e.g. disease phenotypes), gene expression, and potentially other ‘omics’ variables (protein expression, metabolomics, DNA methylation, etc.; Figure 1. Figure 1 was taken from Jansen and Nap (2001)). In this challenge we explore the use of Systems Genetics data for elucidating causal network models among genes, i.e. Gene Networks (DREAM5 SYSGEN...","","https://www.synapse.org/#!Synapse:syn2820440/wiki/","completed","intermediate","1","","2010-07-09","\N","2023-09-12 21:28:10","2023-10-12 17:54:42" -"212","dream-6-estimation-of-model-parameters-challenge","DREAM 6 - Estimation of Model Parameters Challenge","","Given the complete model structures (including expressions for the kinetic rate laws) for three gene regulatory networks, participants are asked to develop and/or apply optimization methods, including the selection of the most informative experiments, to accurately estimate parameters and predict outcomes of perturbations in Systems Biology models.","","https://www.synapse.org/#!Synapse:syn2841366/wiki/71372","completed","intermediate","1","","2011-06-01","2011-10-31","2023-09-12 21:28:12","2023-10-12 17:54:45" -"213","dream-6-flowcap2-molecular-classification-of-acute-myeloid-leukemia-challenge","DREAM 6 - FlowCAP2 Molecular Classification of Acute Myeloid Leukemia Challenge","The goal of this challenge is to diagnose Acute Myeloid Leukaemia from patie...","Flow cytometry (FCM) has been widely used by immunologists and cancer biologists for more than 30 years as a biomedical research tool to distinguish different cell types in mixed populations, based on the expression of cellular markers. It has also become a widely used diagnostic tool for clinicians to identify abnormal cell populations associated with disease. In the last decade, advances in instrumentation and reagent technologies have enabled simultaneous single-cell measurement of tens of surface and intracellular markers, as well as tens of signaling molecules, positioning FCM to play an even bigger role in medicine and systems biology [1,2]. However, the rapid expansion of FCM applications has outpaced the functionality of traditional analysis tools used to interpret FCM data such that scientists are faced with the daunting prospect of manually identifying interesting cell populations in 20 dimensional data from a collection of millions of cells. For these reasons a reliable...","","https://www.synapse.org/#!Synapse:syn2887788/wiki/72178","completed","intermediate","1","https://doi.org/10.1038/nmeth.2365","2011-06-01","2011-09-30","2023-09-12 21:28:19","2023-10-12 17:54:47" -"214","dream-6-alternative-splicing-challenge","DREAM 6 - Alternative Splicing Challenge","","The goal of the mRNA-seq alternative splicing challenge is to assess the accuracy of the reconstruction of alternatively spliced mRNA transcripts from Illumina short-read mRNA-seq. Reconstructed transcripts will be scored against Pacific Biosciences long-read mRNA-seq. The ensuing analysis of the transcriptomes from mandrill and rhinoceros fibroblasts and their derived induced pluripotent stem cells (iPSC), as well as the transcriptome for human Embrionic Stem Cells (hESC) is an opportunity to discover novel biology as well as investigate species-bias of different methods.","","https://www.synapse.org/#!Synapse:syn2817724/wiki/","completed","intermediate","1","","2011-08-09","\N","2023-09-12 21:28:25","2023-10-12 17:54:50" -"215","causalbench-challenge","CausalBench Challenge","A machine learning contest for gene network inference from single-cell pertu...","Mapping gene-gene interactions in cellular systems is a fundamental step in early-stage drug discovery that helps generate hypotheses on what molecular mechanisms may effectively be targeted by potential future medicines. In the CausalBench Challenge, we invite the machine-learning community to advance the state-of-the-art in deriving gene-gene networks from large-scale real-world perturbational single-cell datasets to improve our ability to glean causal insights into disease-relevant biology.","","https://www.gsk.ai/causalbench-challenge/","completed","intermediate","16","https://doi.org/10.48550/arXiv.2308.15395","2023-03-01","2023-04-21","2023-09-12 21:28:25","2023-10-19 23:32:34" -"216","iclr-computational-geometry-and-topology-challenge-2022","ICLR Computational Geometry & Topology Challenge 2022","","The purpose of this challenge is to foster reproducible research in geometric (deep) learning, by crowdsourcing the open-source implementation of learning algorithms on manifolds. Participants are asked to contribute code for a published/unpublished algorithm, following Scikit-Learn/Geomstats' or pytorch's APIs and computational primitives, benchmark it, and demonstrate its use in real-world scenarios.","","https://github.com/geomstats/challenge-iclr-2022","completed","intermediate","14","","\N","2022-04-04","2023-09-13 16:54:06","2023-10-19 23:28:44" -"217","iclr-computational-geometry-and-topology-challenge-2021","ICLR Computational Geometry & Topology Challenge 2021","","The purpose of this challenge is to push forward the fields of computational differential geometry and topology, by creating the best data analysis, computational method, or numerical experiment relying on state-of-the-art geometric and topological Python packages.","","https://github.com/geomstats/challenge-iclr-2021","completed","intermediate","14","https://doi.org/10.48550/arXiv.2108.09810","\N","2021-05-02","2023-09-13 17:02:12","2023-10-19 23:28:44" -"218","genedisco-challenge","GeneDisco Challenge","","The GeneDisco challenge is a machine learning community challenge for evaluating batch active learning algorithms for exploring the vast experimental design space in genetic perturbation experiments. Genetic perturbation experiments, using for example CRISPR technologies to perturb the genome, are a vital component of early-stage drug discovery, including target discovery and target validation. The GeneDisco challenge is organized in conjunction with the Machine Learning for Drug Discovery workshop at ICLR-22.","","https://www.gsk.ai/genedisco-challenge/","completed","intermediate","16","https://doi.org/10.48550/arXiv.2110.11875","2022-01-31","2022-03-31","2023-09-13 17:20:30","2023-10-19 23:32:43" -"219","hidden-treasures-warm-up","Hidden Treasures: Warm Up","","In the context of human genome sequencing, software pipelines typically involve a wide range of processing elements, including aligning sequencing reads to a reference genome and subsequently identifying variants (differences). One way of assessing the performance of such pipelines is by using well-characterized datasets such as Genome in a Bottle’s NA12878. However, because the existing NGS reference datasets are very limited and have been widely used to train/develop software pipelines, benchmarking of pipeline performance would ideally be done on samples with unknown variants. This challenge will provide a unique opportunity for participants to investigate the accuracy of their pipelines by testing the ability to find in silico injected variants in FASTQ files from exome sequencing of reference cell lines. It will be a warm up for the community ahead of a more difficult in silico challenge to come in the fall. This challenge will provide users with a FASTQ file of a NA12878 se...","","https://precision.fda.gov/challenges/1","completed","intermediate","6","","2017-07-17","2017-09-13","2023-09-13 23:31:39","2023-10-12 17:55:23" -"220","data-management-and-graph-extraction-for-large-models-in-the-biomedical-space","Data management and graph extraction for large models in the biomedical space","Collaborative hackathon on the topic of data management and graph extraction...","This fall, CMU Libraries is hosting a hackathon in partnership with DNAnexus on the topic of data management and graph extraction for large models in the biomedical space. The hackathon will be held in person at CMU, October 19-21, 2023. The hackathon is a collaborative, rather than competitive, event, with each team working on a dedicated part of the problem. The teams will be focused on the following topics-1) Knowledge graph-based validation for variant (genomic) assertions; 2) Continuous monitoring for RLHF and flexible infrastructure for layering assertions with rollback; 3) Flexible tokenization of complex data types; 4) Assertion tracking in large models; 5) Column headers for data harmonization. The outputs are often published as preprints or on the F1000 hackathon channel. Contact Ben Busby (bbusby@dnanexus.com) with any questions about the hackathon or serving as a team lead.","","https://library.cmu.edu/about/news/2023-08/hackathon-2023","active","intermediate","14","","2023-10-19","2023-10-21","2023-09-13 23:32:59","2023-09-27 21:08:26" -"221","cagi2-asthma-twins","CAGI2: Asthma discordant monozygotic twins","With the provided whole genome and RNA sequencing data, identify which two i...","The dataset includes whole genomes of 8 pairs of discordant monozygotic twins (randomly numbered from 1 to 16) that is, in each pair identical twins one has asthma and one does not. In addition, RNA sequencing data for each individual is provided. One of the twins in each pair suffers from asthma while the other twin is healthy.","","https://genomeinterpretation.org/CAGI2-asthma-twins.html","completed","intermediate","2","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 18:11:42" -"222","cagi4-bipolar","CAGI4: Bipolar disorder","With the provided exome data, identify which individuals have BD and which i...","Bipolar disorder (BD) is a serious mental illness characterized by recurrent episodes of manias and depression, which are syndromes of abnormal mood, thinking and behavior. It affects 1.0-4.5% of the population [1], and it is among the major causes of disability worldwide. This challenge involved the prediction of which of a set of individuals have been diagnosed with bipolar disorder, given exome data. 500 of the 1000 exome samples were provided for training.","","https://genomeinterpretation.org/CAGI4-bipolar.html","completed","intermediate","2","","\N","2016-04-04","2023-09-28 18:19:48","2023-09-28 18:25:17" -"223","cagi3-brca","CAGI3: BRCA1 & BRCA2","For each variant, provide the probability that Myriad Genetics has classifie...","In normal cells, the BRCA1 and BRCA2 genes are involved in homologous recombination for double strand break repair and ensure the stability of a cell's genetic material. Mutations in these genes have been linked to development of breast and ovarian cancer. Myriad Genetics created the BRACAnalysis test in order to assess a woman’s risk of developing hereditary breast or ovarian cancer based on detection of mutations in the BRCA1 and BRCA2 genes. This test has become the standard of care in identification of individuals with hereditary breast and ovarian cancer (HBOC) syndrome. It is based on proprietary methods.","","https://genomeinterpretation.org/CAGI3-brca.html","completed","intermediate","2","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-19 23:32:48" -"224","cagi2-breast-cancer-pkg","CAGI2: Breast cancer pharmacogenomics","Cancer tissues are specifically responsive to different drugs. For this expe...","Cell-cycle-checkpoint kinase 2 (CHEK2; OMIM #604373) is a protein that plays an important role in the maintenance of genome integrity and in the regulation of the G2/M cell cycle checkpoint. CHEK2 has been shown to interact with other proteins involved in DNA repair processes such as BRCA1 and TP53. These findings render CHEK2 an 23 attractive candidate susceptibility gene for a variety of cancers.","","https://genomeinterpretation.org/CAGI2-breast-cancer-pkg.html","completed","intermediate","2","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 17:46:22" -"225","cagi4-2eqtl","CAGI4: eQTL causal SNPs","Participants are asked to submit predictions of the regulatory sequences tha...","Identifying the causal alleles responsible for variation in expression of human genes has been particularly difficult. This is an important problem, as genome-wide association studies (GWAS) suggest that much of the variation underlying common traits and diseases maps within regions of the genome that do not encode protein. A massively parallel reporter assay (MPRA) has been applied to thousands of single nucleotide polymorphisms (SNPs) and small insertion/deletion polymorphisms in linkage disequilibrium (LD) with cis-expression quantitative trait loci (eQTLs). The results identify variants showing differential expression between alleles. The challenge is to identify the regulatory sequences and the expression-modulating variants (emVars) underlying each eQTL and estimate their effects in the assay.","","https://genomeinterpretation.org/CAGI4-2eqtl.html","completed","intermediate","2","","\N","2016-04-04","2023-09-28 18:19:48","2023-09-29 3-58-33" -"226","cagi1-cbs","CAGI1: CBS","Participants were asked to submit predictions for the effect of the variants...","CBS is a vitamin-dependent enzyme involved in cysteine biosynthesis. The human CBS requires two cofactors for function, vitamin B6 and heme. Homocystinuria due to CBS deficiency (OMIM #236200) is a recessive inborn error of sulfur amino acid metabolism.","","https://genomeinterpretation.org/CAGI1-cbs.html","completed","intermediate","2","","\N","2010-12-10","2023-09-28 18:19:48","2023-10-12 17:46:07" -"227","cagi2-cbs","CAGI2: CBS","Participants were asked to submit predictions for the effect of the variants...","CBS is a vitamin-dependent enzyme involved in cysteine biosynthesis. The human CBS requires two cofactors for function, vitamin B6 and heme. Homocystinuria due to CBS deficiency (OMIM #236200) is a recessive inborn error of sulfur amino acid metabolism.","","https://genomeinterpretation.org/CAGI2-cbs.html","completed","intermediate","2","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 17:45:56" -"228","cagi1-chek2","CAGI1: CHEK2","Variants in the ATM & CHEK2 genes are associated with breast cancer.","Predictors will be provided with 41 rare missense, nonsense, splicing, and indel variants in CHEK2.","","https://genomeinterpretation.org/CAGI1-chek2.html","completed","intermediate","2","","\N","2010-12-10","2023-09-28 18:19:48","2023-10-19 23:32:57" -"229","cagi3-fch","CAGI3: FCH","The challenge involved exome sequencing data for 5 subjects in an FCH family...","Familial combined hyperlipidemia (FCH; OMIM 14380) the most prevalent hyperlipidemia, is a complex metabolic disorder characterized by variable occurrence of elevated low-density lipoprotein cholesterol (LDL-C) level and high triglycerides (TG)—a condition that is commonly associated with coronary artery disease (CAD).","","https://genomeinterpretation.org/CAGI3-fch.html","completed","intermediate","2","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-12 17:45:43" -"230","cagi3-ha","CAGI3: HA","The dataset for this challenge comprises of exome sequencing data for 4 subj...","Hypoalphalipoproteinemia (HA; OMIM #604091) is characterized by severely decreased serum high-density lipoprotein cholesterol (HDL-C) levels and low apolipoprotein A1 (APOA1). Low HDL-C is a risk factor for coronary artery disease.","","https://genomeinterpretation.org/CAGI3-ha.html","completed","intermediate","2","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-12 17:45:41" -"231","cagi2-croshn-s","CAGI2: Crohn's disease","With the provided exome data, identify which individuals have Crohn's diseas...","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/CAGI2-croshn-s.html","completed","intermediate","2","","\N","2011-10-06","2023-09-28 18:19:48","2023-09-27 21:09:04" -"232","cagi3-crohn-s","CAGI3: Crohn's disease","With the provided exome data, identify which individuals have Crohn's diseas...","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/CAGI3-crohn-s.html","completed","intermediate","2","","\N","2013-04-25","2023-09-28 18:19:48","2023-09-28 18:25:20" -"233","cagi4-chron-s-exome","CAGI4: Crohn's exomes","With the provided exome data, identify which individuals have Crohn's diseas...","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/cagi4-chron-s-exome.html","completed","intermediate","2","","\N","2016-04-04","2023-09-28 18:19:48","2023-10-16 18:29:11" -"234","cagi4-hopkins","CAGI4: Hopkins clinical panel","Participants were tasked with identifying the disease class for each of 106 ...","The Johns Hopkins challenge, provided by the Johns Hopkins DNA Diagnostic Laboratory (http://www.hopkinsmedicine.org/dnadiagnostic), comprised of exonic sequence for 83 genes associated with one of 14 disease classes, including 5 decoys","","https://genomeinterpretation.org/CAGI4-hopkins.html","completed","intermediate","2","","\N","2016-04-04","2023-09-28 18:19:48","2023-10-12 17:45:27" -"235","cagi2-mouse-exomes","CAGI2: Mouse exomes","The challenge involved identifying the causative variants leading to one of ...","Predictors were given SNVs and indels found from exome sequencing. Causative variants had been identified for the L11Jus74 and Sofa phenotypes by the use of traditional breeding crosses,47 and the predictions were compared to these results, which were unpublished at the time of the CAGI submissions. The L11Jus74 phenotype is caused by two SNVs (chr11-102258914A> and chr11-77984176A>T), whereas a 15-nucleotide deletion in the Pfas gene is responsible for the Sofa phenotype. The predictions for Frg and Stn phenotypes could not be compared to experimental data, as the causative variants could not successfully be mapped by linkage","","https://genomeinterpretation.org/CAGI2-mouse-exomes.html","completed","intermediate","2","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 17:45:19" -"236","cagi3-mrn-mre11","CAGI3: MRE11","Genomes are subject to constant threat by damaging agents that generate DNA ...","Predict probability of pathogenicity (a number between 0 and 1) for individual rare variants of MRE11 and NBS1.","","https://genomeinterpretation.org/CAGI3-mrn.html","completed","intermediate","2","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-16 18:38:44" -"237","cagi4-naglu","CAGI4: NAGLU","Participants are asked to submit predictions on the effect of the variants o...","NAGLU is a lysosomal glycohydrolyase. Deficiency of NAGLU causes the rare disorder Mucopolysaccharidosis IIIB or Sanfilippo B disease. Naturally occurring NAGLU mutants have been assayed for enzymatic activity in transfected cell lysates. The challenge is to predict the fractional activity of each mutant protein compared to the wild-type enzyme.","","https://genomeinterpretation.org/CAGI4-naglu.html","completed","intermediate","2","","\N","2016-04-04","2023-09-28 18:19:48","2023-10-20 23:28:52" -"238","cagi4-npm-alk","CAGI4: NPM: ALK","Participants are asked to submit predictions of both the kinase activity and...","NPM-ALK is a fusion protein in which the ALK tyrosine kinase is constitutively activated, contributing to cancer. NPM-ALK constructs with mutations in the kinase domain have been assayed in extracts of transfected cells. The challenge is to predict the kinase activity and the Hsp90 binding affinity of the mutant proteins relative to the reference NPM-ALK fusion protein.","","https://genomeinterpretation.org/CAGI4-npm-alk.html","completed","intermediate","2","","\N","2016-04-04","2023-09-28 18:19:48","2023-10-20 23:28:53" -"239","cagi3-mrn-nbs1","CAGI3: NBS1","Genomes are subject to constant threat by damaging agents that generate DNA ...","Predict probability of pathogenicity (a number between 0 and 1) for individual rare variants of MRE11 and NBS1.","","https://genomeinterpretation.org/CAGI3-mrn.html","completed","intermediate","2","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-16 18:38:55" -"240","cagi3-p16","CAGI3: p16","CDKN2A is the most common, high penetrance, susceptibility gene identified t...","Evaluate how different variants of p16 protein impact its ability to block cell proliferation.","","https://genomeinterpretation.org/CAGI3-p16.html","completed","intermediate","2","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-20 23:28:57" -"241","cagi2-p53","CAGI2: p53 reactivation","Predictors are asked to submit predictions on the effect of the cancer rescu...","The transcription factor p53 is a central tumor suppressor protein that controls DNA repair, cell cycle arrest, and apoptosis (programmed cell death). About half of human cancers have p53 mutations that inactivate p53. Over 250,000 US deaths yearly are due to tumors that express full-length p53 that has been inactivated by a single point mutation. For the past several years, the group of Rick Lathrop at University of California, Irvine, has been engaged in a complete functional census of p53 second-site suppressor (“cancer rescue”) mutations. These cancer rescue mutations are additional amino acids changes (to otherwise cancerous p53 mutations), which have been found to rescue p53 tumor suppressor function, reactivating otherwise inactive p53. These intragenic rescue mutations reactivate cancer mutant p53 in yeast and human cell assays by providing structural changes that compensate for the cancer mutation.","","https://genomeinterpretation.org/CAGI2-p53.html","completed","intermediate","2","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-20 23:28:58" -"242","cagi1-pgp","CAGI1: PGP","PGP challenge requires matching of full genome sequences to extensive phenot...","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/CAGI1-pgp.html","completed","intermediate","2","","\N","2010-12-10","2023-09-28 18:19:48","2023-09-27 21:05:22" -"243","cagi2-pgp","CAGI2: PGP","PGP challenge requires matching of full genome sequences to extensive phenot...","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/CAGI2-pgp.html","completed","intermediate","2","","\N","2011-10-06","2023-09-28 18:19:48","2023-09-27 21:05:23" -"244","cagi3-pgp","CAGI3: PGP","PGP challenge requires matching of full genome sequences to extensive phenot...","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/CAGI3-pgp.html","completed","intermediate","2","","\N","2013-04-25","2023-09-28 18:19:48","2023-09-27 21:05:23" -"245","cagi4-pgp","CAGI4: PGP","PGP challenge requires matching of full genome sequences to extensive phenot...","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/CAGI4-pgp.html","completed","intermediate","2","","\N","2016-04-04","2023-09-28 18:19:48","2023-09-27 21:05:24" -"246","cagi4-pyruvate-kinase","CAGI4: Pyruvate kinase","Participants are asked to submit predictions on the effect of the mutations ...","Pyruvate kinase catalyzes the last step in glycolysis and is regulated by allosteric effectors. Variants in the gene encoding the isozymes expressed in red blood cells and liver, including missense variants mapping near the effector binding sites, cause PK deficiency. A large set of single amino acid mutations in the liver enzyme has been assayed in E. coli extracts for the effect on allosteric regulation of enzyme activity. The challenge is to predict the impacts of mutations on enzyme activity and allosteric regulation.","","https://genomeinterpretation.org/CAGI4-pyruvate-kinase.html","completed","intermediate","2","","\N","2015-01-11","2023-09-28 18:19:48","2023-09-29 22:06:22" -"247","cagi2-rad50","CAGI2: RAD50","Predict the probability of the variant occurring in a case individual.","RAD50 is a candidate intermediate-risk breast cancer susceptibility gene. The RAD50 data provided for CAGI challenge include a list of potentially interesting sequence variants observed from sequencing RAD50 gene in about 1,400 breast cancer cases and 1,200 ethnically matched controls. Variants in the list were observed between 1 and 20 times.","","https://genomeinterpretation.org/CAGI2-rad50.html","completed","intermediate","2","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-19 23:33:11" -"248","cagi2-risksnps","CAGI2: riskSNPs","The goal of these challenges is to investigate the community’s ability to id...","The goal of this experiment is to explore current understanding of the molecular level mechanisms underlying associations between SNPs and disease risk, incorporating expertise in each of the known mechanism areas, and as far as possible assigning possible mechanisms for each association locus. The correct mechanisms are unknown, so there can be no ranking of accuracy-that is not the point of the experiment. Rather, the goal is to ascertain which mechanisms appear most relevant, how confidently they can be assigned, and what fraction of loci can currently be assigned plausible mechanisms.","","https://genomeinterpretation.org/CAGI2-risksnps.html","completed","intermediate","2","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-19 23:33:11" -"249","cagi3-risksnps","CAGI3: riskSNPs","The goal of these challenges is to investigate the community’s ability to id...","The goal of this experiment is to explore current understanding of the molecular level mechanisms underlying associations between SNPs and disease risk, incorporating expertise in each of the known mechanism areas, and as far as possible assigning possible mechanisms for each association locus. The correct mechanisms are unknown, so there can be no ranking of accuracy-that is not the point of the experiment. Rather, the goal is to ascertain which mechanisms appear most relevant, how confidently they can be assigned, and what fraction of loci can currently be assigned plausible mechanisms.","","https://genomeinterpretation.org/CAGI3-risksnps.html","completed","intermediate","2","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-19 23:33:13" -"250","cagi2-nav1-5","CAGI2: SCN5A","Predictors are asked to submit predictions on the effect of the mutants on t...","The cardiac action potential (AP) is the sum of a number of distinct ionic currents. It can be divided into five phases (phase 0‐4). From pacemaker cells of the SA node the initial depolarizing wave front will spread throughout the cardiomyocytes via gap junctions. If the depolarization is sufficient voltage‐dependent sodium channels (Nav1.5) are activated and allow Na+ influx. This results in a further depolarization of the membrane which will lead to opening of even more Nav channels. This positive feedback mechanism is seen as the rapid upstroke in the initial phase (phase 0) of the action potential. Nav1.5 is encoded by SCN5A and mutations in this gene have been associated with various diseases such as Atrial fibrillation, Long QT syndrome, Cardiac Conduction Defect, Sick Sinus Disease, and Brugada Syndrome (BrS).","","https://genomeinterpretation.org/CAGI2-nav1.5.html","completed","intermediate","2","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-16 18:32:16" -"251","cagi2-mr-1","CAGI2: Shewanella oneidensis strain MR-1","Shewanella oneidensis strain MR-1 (formerly known as S. putrefaciens) is a m...","Predictors are asked to submit predictions on how insertions in the given gene of MR-1 affect the fitness of that gene in a given condition (stressor).","","https://genomeinterpretation.org/CAGI2-mr-1.html","completed","intermediate","2","","\N","2011-10-06","2023-09-28 18:19:55","2023-10-16 18:32:21" -"252","cagi3-mr-1","CAGI3: Shewanella oneidensis strain MR-1","Shewanella oneidensis strain MR-1 (formerly known as S. putrefaciens) is a m...","Predictors are asked to submit predictions on how insertions in the given gene of MR-1 affect the fitness of that gene in a given condition (stressor).","","https://genomeinterpretation.org/CAGI3-mr-1.html","completed","intermediate","2","","\N","2013-04-25","2023-09-28 18:20:01","2023-10-16 18:18:07" -"253","cagi4-sickkids","CAGI4: SickKids","The challenge presented here is to use computational methods to match each g...","Realizing the promise of precision medicine will require developing methods for interpreting genome sequence data to infer individuals’ phenotypic traits and predispositions to disease. This challenge involves 25 children with suspected genetic disorders who were referred for clinical genome sequencing. Predictors are given their genome sequences and their clinical phenotypic descriptions, as provided to the diagnostic laboratory, and asked to predict which genome corresponds to which clinical description. Additionally, identify the diagnostic variants underlying the predictions. Optionally, identify predictive secondary variants conferring high risk of other diseases whose phenotypes are not reported in the clinical descriptions.","","https://genomeinterpretation.org/CAGI4-sickkids.html","completed","intermediate","2","","\N","2016-04-04","2023-09-28 18:19:48","2023-10-06 20:48:13" -"254","cagi4-sumo-ligase","CAGI4: SUMO ligase","Participants are asked to submit predictions of the effect of the variants o...","SUMO ligase identifies target proteins and covalently attaches SUMO to them, thereby modulating the functions of hundreds of proteins including proteins implicated in cancer, neurodegeneration, and other diseases. A large library of missense mutations in human SUMO ligase has been assessed for competitive growth in a high-throughput yeast-based complementation assay. The challenge is to predict the effect of mutations on function, as measured by the change in fractional representation of each mutant SUMO ligase clone, relative to wild-type clones, in a competitive yeast growth assay.","","https://genomeinterpretation.org/CAGI4-sumo-ligase.html","completed","intermediate","2","","\N","2016-04-04","2023-09-28 18:19:48","2023-10-19 23:31:57" -"255","cagi3-splicing","CAGI3: TP53 splicing","With the provided data determine which disease-causing mutations in the TP53...","The function of exonic splicing regulatory elements can be undermined by DNA sequence variation and in some cases can contribute to pathogenesis. Thousands of disease-causing mutations disrupt exonic splicing regulatory elements. These data suggest that >25 percent of missense mutations may impact pre-mRNA splicing rather than mRNA translation. Using minigene constructs derived from a fragment of the TP53 gene, we have experimentally determined if each mutation influences splicing fidelity in HEK293T cells. We hope that CAGI participants will be able to predict the outcome of our experiments. A long-term goal will be the computational prioritization of disease-causing mutations prior to experimental validation. This contribution is expected to have major impacts in understanding the pathogenic basis of disease-causing mutations.","","https://genomeinterpretation.org/CAGI3-splicing.html","completed","intermediate","2","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-10 19:48:10" -"256","cagi4-warfarin","CAGI4: Warfarin exomes","With the provided exome data and clinical covariates, predict the therapeuti...","With over 33 million prescriptions in 2011, warfarin is the most commonly used anticoagulant for preventing thromboembolic events. Warfarin has a twenty-fold inter-individual dose variability and a narrow therapeutic index, and it is responsible for a third of adverse drug event hospitalizations in older Americans [2]. Alternatives to warfarin, such as direct thrombin inhibitors and factor Xa inhibitors, are now available. However, these are more expensive, irreversible, and may cause a higher rate of acute coronary events compared to warfarin [3,4]. Thus, warfarin remains a mainstay of anticoagulant therapy, and better methods of dosing warfarin will lead to fewer adverse events due to overcoagulation.","","https://genomeinterpretation.org/CAGI4-warfarin.html","completed","intermediate","2","","\N","2016-04-04","2023-09-28 18:19:48","2023-09-28 21:19:03" -"257","cagi6-calmodulin","CAGI6: Calmodulin","participants were asked to submit predictions for the competitive growth sco...","Calmodulin (CaM) is a ubiquitous calcium (Ca2+) sensor protein interacting with more than 200 molecular partners, thereby regulating a variety of biological processes. Missense point mutations in the genes encoding CaM have been associated with ventricular tachycardia and sudden cardiac death. A library encompassing up to 17 point mutations was assessed by far-UV circular dichroism (CD) by measuring melting temperature (Tm) and percentage of unfolding (%unfold) upon thermal denaturation at pH and salt concentration that mimic the physiological conditions. The challenge is to predict- the Tm and %unfold values for isolated CaM variants under Ca2+-saturating conditions (Ca2+-CaM) and in the Ca2+-free (apo) state; whether the point mutation stabilizes or destabilizes the protein (based on Tm and %unfold).","","https://genomeinterpretation.org/CAGI6-cam.html","completed","intermediate","1","","\N","2021-12-31","2023-09-28 18:19:48","2023-10-19 23:33:19" -"258","cagi2-splicing","CAGI2: splicing","Predictors are asked to compare exons from wild type and disease-associated ...","Accurate precursor mRNA (pre-mRNA) splicing is required for the expression of protein coding genes from the human genome. In this process, intervening sequences (introns) are removed from pre-mRNA and coding/regulatory sequences (exons) are ligated together generating a mature mRNA. A large ribonucleoprotein machine called the spliceosome assembles de novo upon every nascent intron and catalyzes the chemical steps of splicing.","","https://genomeinterpretation.org/CAGI2-splicing.html","completed","intermediate","2","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-18 15:32:55" -"259","cagi6-arsa","CAGI6: ARSA","Predicting the effect of naturally occurring missense mutations on enzymatic...","Metachromatic Leukodystrophy (MLD) is an autosomal recessive, lysosomal-storage disease caused by mutations in Arylsulfatase A (ARSA) and toxic accumulation of sulfatide substrate. Genome sequencing has revealed hundreds of protein-altering, ARSA missense variants, but the functional effect of most variants remains unknown. ARSA enzyme activity using a high-throughput cellular assay was measured for a large set of variants of known significance and variants of unknown significance. The challenge is to predict the fractional enzyme activity of each mutant protein compared to the wildtype protein.","","https://genomeinterpretation.org/CAGI6-lc-arsa.html","completed","intermediate","1","","\N","2022-11-16","2023-09-28 18:20:23","2023-10-12 18:11:51" -"260","predict-hits-for-the-wdr-domain-of-lrrk2","CACHE1: PREDICT HITS FOR THE WDR DOMAIN OF LRRK2","Finding ligands targeting the central cavity of the WD-40 repeat (WDR) domai...","The first CACHE Challenge target is LRRK2, the most commonly mutated gene in familial Parkinson's Disease. Participants are asked to find hits for the WD40 repeat (WDR) domain of LRRK2. Read more under Details below.","","https://cache-challenge.org/challenges/predict-hits-for-the-wdr-domain-of-lrrk2","completed","intermediate","17","","2021-12-01","2022-01-31","2023-09-27 19:01:55","2023-10-16 19:03:47" -"261","finding-ligands-targeting-the-conserved-rna-binding-site-of-sars-cov-2-nsp13","CACHE2: FINDING LIGANDS TARGETING THE CONSERVED RNA BINDING SITE OF SARS-CoV-2 NSP13","Finding ligands targeting the conserved RNA binding site of SARS-CoV-2 NSP13.","Predicted compounds will be procured and tested at CACHE using both enzymatic and binding assays","","https://cache-challenge.org/challenges/finding-ligands-targeting-the-conserved-rna-binding-site-of-sars-cov-2-nsp13","completed","intermediate","17","","2022-06-22","2022-09-04","2023-09-27 19:02:43","2023-10-16 19:01:17" -"262","finding-ligands-targeting-the-macrodomain-of-sars-cov-2-nsp3","CACHE3: Finding ligands targeting the macrodomain of SARS-CoV-2 Nsp3","Severe acute respiratory syndrome coronavirus 2","To predict ligands that bind to the ADPr site of SARS-CoV-2 Nsp3 macrodomain (Mac1).","","https://cache-challenge.org/challenges/finding-ligands-targeting-the-macrodomain-of-sars-cov-2-nsp3","completed","intermediate","17","","2022-11-02","2023-01-01","2023-09-27 19:03:13","2023-10-16 19:01:19" -"263","finding-ligands-targeting-the-tkb-domain-of-cblb","CACHE4: Finding ligands targeting the TKB domain of CBLB","Several cancers (PMID-33306199), potential immunotherapy (PMID-24875217), in...","Predict compounds that bind to the closed conformation of the CBLB TKB domain with novel chemical templates and KD below 30 micromolar.","","https://cache-challenge.org/challenges/finding-ligands-targeting-the-tkb-domain-of-cblb","completed","intermediate","17","","2023-03-09","2023-05-09","2023-09-27 19:03:14","2023-10-16 19:01:22" -"264","jan2024-rare-disease-ai-hackathon","Jan2024: Rare Disease AI Hackathon","Researchers and medical experts are invited to collaborate on our patient ca...","Bring AI and medical experts together to build open source models for rare diseases. Create zero-barrier access to rare disease expertise for patients, researchers and physicians. Use AI to Uncover novel links between rare diseases. Establish validation methods for medical AI models. Jumpstart an open source community for rare disease AI models. Launch models for Beta testing on Hypophosphatasia.ai and EhlersDanlos.ai.","","https://www.rarediseaseaihackathon.org/","active","intermediate","14","","2023-09-30","2024-01-15","2023-09-27 19:10:40","2023-10-12 18:13:38" -"265","cometh-benchmark","COMETH Benchmark","Quantify tumor heterogeneity-how many cell types are present and in which pr...","Successful treatment of cancer is still a challenge and this is partly due to a wide heterogeneity of cancer composition across patient population. Unfortunately, accounting for such heterogeneity is very difficult. Clinical evaluation of tumor heterogeneity often requires the expertise of anatomical pathologists and radiologists.This benchmark is dedicated to the quantification of intra-tumor heterogeneity using appropriate statistical methods on cancer omics data.In particular, it focuses on estimating cell types and proportion in biological samples based on methylation and methylome data sets. The goal is to explore various statistical methods for source separation/deconvolution analysis (Non-negative Matrix Factorization, Surrogate Variable Analysis, Principal component Analysis, Latent Factor Models, ...) using both RNA-seq and methylome data.","","https://www.codabench.org/competitions/218/","completed","intermediate","10","","2020-06-14","2020-12-29","2023-09-28 23:25:52","2023-10-10 19:47:14" -"266","the-miccai-2014-machine-learning-challenge","The MICCAI 2014 Machine Learning Challenge","Predicting Binary and Continuous Phenotypes from Structural Brain MRI Data","Machine learning tools have been increasingly applied to structural brain magnetic resonance imaging (MRI) scans, largely for developing models to predict clinical phenotypes at the individual level. Despite significant methodological developments and novel application domains, there has been little effort to conduct benchmark studies with standardized datasets, which researchers can use to validate new tools, and more importantly conduct an objective comparison with state-of-the-art algorithms. The MICCAI 2014 Machine Learning Challenge (MLC) will take a significant step in this direction, where we will employ four separate, carefully compiled, and curated large-scale (each N > 70) structural brain MRI datasets with accompanying clinically relevant phenotypes. Our goal is to provide a snapshot of the current state of the art in the field of neuroimage-based prediction, and attract machine-learning practitioners to the MICCAI community and the field of medical image computing in g...","","https://competitions.codalab.org/competitions/1471","completed","intermediate","9","","2014-04-16","2014-06-14","2023-09-28 23:36:12","2023-10-19 23:31:50" -"267","cagi6-annotate-all-missense","CAGI6: Annotate All Missense","Predictors are asked to predict the functional effect predict each coding SNV.","dbNSFP currently describes 81,782,923 possible protein-altering variants in the human genome. The challenge is to predict the functional effect of every such variant. For the vast majority of these missense and nonsense variants, the functional impact is not currently known, but experimental and clinical evidence is accruing rapidly. Rather than drawing upon a single discrete dataset as typical with CAGI, predictions will be assessed by comparing with experimental or clinical annotations made available after the prediction submission date, on an ongoing basis. If predictors assent, predictions will also be incorporated into dbNSFP.","","https://genomeinterpretation.org/CAGI6-annotate-all-missense.html","completed","intermediate","1","","2021-06-01","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:13:42" -"268","cagi6-hmbs","CAGI6: HMBS","Participants are asked to submit predictions of the fitness score for each o...","Hydroxymethylbilane synthase (HMBS), also known as porphobilinogen deaminase (PBGD) or uroporphyrinogen I synthase, is an enzyme involved in heme production. In humans, variants that affect HMBS function result in acute intermittent porphyria (AIP), an autosomal dominant genetic disorder caused by a build-up of porphobilinogen in the cytoplasm. A large library of HMBS missense variants was assessed with respect to their effects on protein function using a high-throughput yeast complementation assay. The challenge is to predict the functional effects of these variants.","","https://genomeinterpretation.org/CAGI6-hmbs.html","completed","intermediate","1","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:05" -"269","cagi6-intellectual-disability-panel","CAGI6: Intellectual Disability Panel","In this challenge predictors are asked to analyze the sequence data for the ...","The objective in this challenge is to predict a patient's clinical phenotype and the causal variant(s) based on their gene panel sequences. Sequence data for 74 genes from a cohort of 500 patients with a range of neurodevelopmental presentations (intellectual disability, autistic spectrum disorder, epilepsy, microcephaly, macrocephaly, hypotonia, ataxia) has been made available for this challenge. Additional data from 150 patients from the same clinical study is available for training and validation.","","https://genomeinterpretation.org/CAGI6-id-panel.html","completed","intermediate","1","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:09" -"270","cagi6-mapk1","CAGI6: MAPK1","For each variant, participants are asked to predict the ΔΔGH20 value for the...","MAPK1 (ERK2) is active as serine/threonine kinase in the Ras-Raf-MEK-ERK signal transduction cascade that regulates cell proliferation, transcription, differentiation, and cell cycle progression. MAPK1 is activated by phosphorylation which occurs with strict specificity by MEK1/2 on Thr185 and Tyr187, and may also act as a transcriptional repressor independent of its kinase activity. A library of eleven missense variants selected from the COSMIC database was assessed by near and far-UV circular dichroism and intrinsic fluorescence spectra to determine thermodynamic stability at different concentrations of denaturant. These data were used to calculate a ΔΔGH20 value; i.e., the difference in unfolding free energy ΔGH20 between each variant and the wildtype protein, both in phosphorylated and unphosphorylated forms. The challenge is to predict these two ΔΔGH20 values and the catalytic efficiency (kcat/km)mut/(kcat/km)wt, as determined by a fluorescence assay, of the phosphorylated fo...","","https://genomeinterpretation.org/CAGI6-mapk1.html","completed","intermediate","1","","2021-07-08","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:13" -"271","cagi6-mapk3","CAGI6: MAPK3","For each variant, participants are asked to predict the ΔΔGH20 value for the...","MAPK3 (ERK1) is active as serine/threonine kinase in the Ras-Raf-MEK-ERK signal transduction cascade that regulates cell proliferation, transcription, differentiation, and cell cycle progression. MAPK3 is activated by phosphorylation which occurs with strict specificity by MEK1/2 on Thr202 and Tyr204, and may also act as a transcriptional repressor independent of its kinase activity. A library of twelve missense variants selected from the COSMIC database was assessed by near and far-UV circular dichroism and intrinsic fluorescence spectra to determine thermodynamic stability at different concentrations of denaturant. These data were used to calculate a ΔΔGH20 value; i.e., the difference in unfolding free energy ΔGH20 between each variant and the wildtype protein, both in phosphorylated and unphosphorylated forms. The challenge is to predict these two ΔΔGH20 values and the catalytic efficiency (kcat/km)mut/(kcat/km)wt, as determined by a fluorescence assay, of the phosphorylated fo...","","https://genomeinterpretation.org/CAGI6-mapk3.html","completed","intermediate","1","","2021-08-04","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:15" -"272","cagi6-mthfr","CAGI6: MTHFR","Participants are asked to submit predictions of the fitness score for each m...","Methylenetetrahydrofolate reductase (MTHFR) catalyzes the production of 5-methyltetrahydrofolate, which is needed for conversion of homocysteine to methionine. Humans with variants affecting MTHFR function present with a wide range of phenotypes, including homocystinuria, homocysteinemia, developmental delay, severe mental retardation, psychiatric disturbances, and late-onset neurodegenerative disorders. A further complication to interpretation of variants in this gene is a common variant, Ala222Val, carried by a large fraction of the human population. A large library of MTHFR missense variants was assessed with respect to their effects on protein function using a high-throughput yeast complementation assay. The challenge is to predict the functional effects of these variants in two different settings- for the wildtype protein, and for the protein with the common Ala222Val variant.","","https://genomeinterpretation.org/CAGI6-mthfr.html","completed","intermediate","1","","2021-05-03","2021-06-30","2023-06-23 00:00:00","2023-10-12 18:12:18" -"273","cagi6-polygenic-risk-scores","CAGI6: Polygenic Risk Scores","Participants will be expected to provide a fully trained prediction model th...","Polygenic risk scores (PRS) have potential clinical utility for risk surveillance, prevention and personalized medicine. Participants will be provided with datasets of four real phenotypes (Type 2 Diabetes, Breast Cancer, Inflammatory Bowel Disease and Coronary Artery Disease) and of thirty simulated phenotypes representing a range of genetic architectures of common polygenic diseases. The challenge is to predict the disease outcomes of individuals in held-out validation cohorts.","","https://genomeinterpretation.org/CAGI6-prs.html","completed","intermediate","1","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:23" -"274","cagi6-rare-genomes-project","CAGI6: Rare Genomes Project","The prediction challenge involves approximately 30 families.The prediction s...","The Rare Genomes Project (RGP) is a direct-to-participant research study on the utility of genome sequencing for rare disease diagnosis and gene discovery. The study is led by genomics experts and clinicians at the Broad Institute of MIT and Harvard. Research subjects are consented for genomic sequencing and the sharing of their sequence and phenotype information with researchers working to understand the molecular causes of rare disease. When a candidate disease variant believed to be related to the phenotype is identified, the variant is confirmed with Sanger sequencing in a clinical setting and returned to the participant via his or her local physician. In this challenge, whole genome sequence data and phenotype data from a subset of the solved and unsolved RGP families will be provided. Participants in the challenge will try to identify the causative variant(s) in each case. For the unsolved cases, prioritized variants from the participating teams will be examined to see if ad...","","https://genomeinterpretation.org/CAGI6-rgp.html","completed","intermediate","1","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:27" -"275","cagi6-sherloc-clinical-classification","CAGI6: Sherloc clinical classification","Over 122,000 coding (missense, silent, frameshift, stop gained, in-frame cod...","Invitae is a genetic testing company that publishes their variant interpretations to ClinVar. In this challenge, over 122,000 previously uncharacterized variants are provided, spanning the range of effects seen in the clinic. Following the close of this challenge, Invitae will submit their interpretations for these variants to ClinVar. Predictors are asked to interpret the pathogenicity of these variants, and the clinical utility of predictions will be assessed across multiple categories by Invitae.","","https://genomeinterpretation.org/CAGI6-invitae.html","completed","intermediate","1","","2021-07-08","2021-12-01","2023-06-23 00:00:00","2023-10-12 18:12:31" -"276","cagi6-splicing-vus","CAGI6: Splicing VUS","Predict whether the experimentally validated variants of unknown significanc...","Variants causing aberrant splicing have been implicated in a range of common and rare disorders, including retinitis pigmentosa, autism spectrum disorder, amyotrophic lateral sclerosis, and a variety of cancers. However, such variants are frequently overlooked by diagnostic sequencing pipelines, leading to missed diagnoses for patients. Clinically ascertained variants of unknown significance underwent whole-blood based RT-PCR to test for impact on splicing. The challenge is to predict which of the tested variants disrupt splicing.","","https://genomeinterpretation.org/CAGI6-splicing-vus.html","completed","intermediate","1","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:34" -"277","cagi6-stk11","CAGI6: STK11","Participants are asked to submit predictions on the impact of the variants l...","Serine/Threonine Kinase 11 (STK11) is considered a master kinase that functions as a tumor suppressor and nutrient sensor within a heterotrimeric complex with pseudo-kinase STRAD-alpha and structural protein MO25. Germline variants resulting in loss of STK11 define Peutz-Jaghers Syndrome, an autosomal dominant cancer predisposition syndrome marked by gastrointestinal hamartomas and freckling of the oral mucosa. Somatic loss of function variants, both nonsense and missense, occur in 15-30% of non-small cell lung adenocarcinomas, where they correlate clinically with insensitivity to anti-PD1 monoclonal antibody therapy. The challenge is to predict the impact on STK11 function for each missense variant in relation to wildtype STK11.","","https://genomeinterpretation.org/CAGI6-stk11.html","completed","intermediate","1","","2021-06-08","2021-09-01","2023-06-23 00:00:00","2023-10-12 18:12:38" -"278","qbi-hackathon","QBI hackathon","A 48-hour event connecting the Bay Area developer community with scientists ...","The QBI hackathon is a 48-hour event connecting the vibrant Bay Area developer community with the scientists from UCSF, UCB and UCSC, during which we work together on the cutting edge biomedical problems. Advances in computer vision, AI, and machine learning have enabled computers to pick out cat videos, recognize people’s faces from photos, play video games and drive cars. More recently, application of deep neural nets to protein structure prediction completely revolutionized the field. We look forward to seeing how far we can push science ahead when we apply these latest algorithms to biomedically relevant light microscopy, electron microscopy, and proteomics data. If you love FFTs, transformers, language models, topological data processing, or simply writing code, this is your chance to apply your skills to make an impact on global healthcare. Beyond the actual event, we hope to establish a better connection between talented developers and scientists in the Bay Area, so that we...","","https://www.eventbrite.com/e/qbi-hackathon-2023-tickets-633794304827?aff=oddtdtcreator","upcoming","intermediate","14","","20231104","20231105","2023-10-06 21:22:51","2023-10-19 23:49:11" -"279","niddk-central-repository-data-centric-challenge","NIDDK Central Repository Data-Centric Challenge","Enhancing NIDDK datasets for future Artificial Intelligence (AI) applications.","The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repository (https://repository.niddk.nih.gov/home/) is conducting a Data Centric Challenge aimed at augmenting existing Repository data for future secondary research including data-driven discovery by artificial intelligence (AI) researchers. The NIDDK Central Repository (NIDDK-CR) program strives to increase the utilization and impact of the resources under its guardianship. However, lack of standardization and consistent metadata within and across studies limit the ability of secondary researchers to easily combine datasets from related studies to generate new insights using data science methods. In the fall of 2021, the NIDDK-CR began implementing approaches to augment data quality to improve AI-readiness by making research data FAIR (findable, accessible, interoperable, and reusable) via a small pilot project utilizing Natural Language Processing (NLP) to tag study variables. In 2022, the NIDD...","","https://www.challenge.gov/?challenge=niddk-central-repository-data-centric-challenge","active","intermediate","14","","2023-09-20","2023-11-03","2023-10-18 16:58:17","2023-10-18 20:52:49" +"id","slug","name","headline","description","avatar_url","website_url","status","difficulty","platform","doi","operation","start_date","end_date","created_at","updated_at" +"1","network-topology-and-parameter-inference","Network Topology and Parameter Inference","","Participants are asked to develop and/or apply optimization methods, including the selection of the most informative experiments, to accurately estimate parameters and predict outcomes of perturbations in Systems Biology models.","","https://www.synapse.org/#!Synapse:syn2821735","completed","intermediate","1","","","2012-06-01","2012-10-01","2023-06-23 00:00:00","2023-10-19 00:10:08" +"2","breast-cancer-prognosis","Breast Cancer Prognosis","","The goal of the breast cancer prognosis Challenge is to assess the accuracy of computational models designed to predict breast cancer survival, based on clinical information about the patient's tumor as well as genome-wide molecular profiling data including gene expression and copy number profiles.","","https://www.synapse.org/#!Synapse:syn2813426","completed","intermediate","1","","","2012-07-12","2012-10-15","2023-06-23 00:00:00","2023-10-17 23:00:12" +"3","phil-bowen-als-prediction-prize4life","Phil Bowen ALS Prediction Prize4Life","","Amyotrophic Lateral Sclerosis (ALS)-also known as Lou Gehrig's disease (in the US) or Motor Neurone disease (outside the US)-is a fatal neurological disease causing death of the nerve cells in the brain and spinal cord which control voluntary muscle movements. This leaves patients struggling with a progressive loss of motor function while leaving cognitive functions intact. Symptoms usually do not manifest until the age of 50 but can start earlier. At any given time, approximately five out of every 100,000 people worldwide suffer from ALS, though there would be a higher prevalence if the disease did not progress so rapidly, leading to the death of the patient. There are no known risk factors for developing ALS other than having a family member who has a hereditary form of the disease, which accounts for about 5-10% of ALS patients. There is also no known cure for ALS. The only FDA-approved drug for the disease is Riluzole, which has been shown to prolong the life span of someone w...","","https://www.synapse.org/#!Synapse:syn2826267","completed","intermediate","1","","","2012-06-01","2012-10-01","2023-06-23 00:00:00","2023-10-14 05:38:09" +"4","drug-sensitivity-and-drug-synergy-prediction","Drug Sensitivity and Drug Synergy Prediction","","Development of new cancer therapeutics currently requires a long and protracted process of experimentation and testing. Human cancer cell lines represent a good model to help identify associations between molecular subtypes, pathways, and drug response. In recent years there have been several efforts to generate genomic profiles of collections of cell lines and to determine their response to panels of candidate therapeutic compounds. These data provide the basis for the development of in silico models of sensitivity based either on the unperturbed genetic potential of a cancer cell, or by using perturbation data to incorporate knowledge of actual cell response. Making predictions from either of these data profiles will be beneficial in identifying single and combinatorial chemotherapeutic response in patients. To that end, the present challenge seeks computational methods, derived from the molecular profiling of cell lines both in a static state and in response to perturbation of ...","","https://www.synapse.org/#!Synapse:syn2785778","completed","intermediate","1","","operation_3207","2012-06-01","2012-10-01","2023-06-23 00:00:00","2023-10-19 00:11:48" +"5","niehs-ncats-unc-toxicogenetics","NIEHS-NCATS-UNC Toxicogenetics","","This challenge is designed to build predictive models of cytotoxicity as mediated by exposure to environmental toxicants and drugs. To approach this question, we will provide a dataset containing cytotoxicity estimates as measured in lymphoblastoid cell lines derived from 884 individuals following in vitro exposure to 156 chemical compounds. In subchallenge 1, participants will be asked to model interindividual variability in cytotoxicity based on genomic profiles in order to predict cytotoxicity in unknown individuals. In subchallenge 2, participants will be asked to predict population-level parameters of cytotoxicity across chemicals based on structural attributes of compounds in order to predict median cytotoxicity and mean variance in toxicity for unknown compounds.","","https://www.synapse.org/#!Synapse:syn1761567","completed","intermediate","1","","","2013-06-10","2013-09-15","2023-06-23 00:00:00","2023-10-14 05:38:13" +"6","whole-cell-parameter-estimation","Whole-Cell Parameter Estimation","","The goal of this challenge is to explore and compare innovative approaches to parameter estimation of large, heterogeneous computational models. Participants are encouraged to develop and/or apply optimization methods, including the selection of the most informative experiments. The organizers encourage participants to form teams to collaboratively solve the challenge.","","https://www.synapse.org/#!Synapse:syn1876068","completed","intermediate","1","","","2013-06-10","2013-09-23","2023-06-23 00:00:00","2023-10-14 05:38:13" +"7","hpn-dream-breast-cancer-network-inference","HPN-DREAM Breast Cancer Network Inference","","The overall goal of the Heritage-DREAM breast cancer network inference challenge is to quickly and effectively advance our ability to infer causal signaling networks and predict protein phosphorylation dynamics in cancer. We provide extensive training data from experiments on four breast cancer cell lines stimulated with various ligands. The data comprise protein abundance time-courses under inhibitor perturbations.","","https://www.synapse.org/#!Synapse:syn1720047","completed","intermediate","1","","","2013-06-10","2013-09-16","2023-06-23 00:00:00","2023-10-14 05:38:14" +"8","rheumatoid-arthritis-responder","Rheumatoid Arthritis Responder","","The goal of this project is to use a crowd-based competition framework to develop a validated molecular predictor of anti-TNF response in RA. There is an increasing need for predictors of response to therapy in inflammatory disease driven by the observation that most clinically defined diseases show variable response and the growing availability of alternative therapies. Anti-TNF drugs in Rheumatoid Arthritis represent a prototypical example of this opportunity. A number of studies have tried, over the past decade, to develop a robust predictor of response. We believe the time is right to try a different approach to developing such a biomarker with a crowd-sourced collaborative competition. This is based on DREAM and Sage Bionetworks' experience with running competitions and the availability of new unpublished large-scale data relating to RA treatment response.THIS CHALLENGE RAN FROM FEBRUARY TO OCTOBER 2014 AND IS NOW CLOSED.","","https://www.synapse.org/#!Synapse:syn1734172","completed","intermediate","1","","","2014-02-10","2014-06-04","2023-06-23 00:00:00","2023-10-14 05:38:14" +"9","icgc-tcga-dream-mutation-calling","ICGC-TCGA DREAM Mutation Calling","","The ICGC-TCGA DREAM Genomic Mutation Calling Challenge (herein, The Challenge) is an international effort to improve standard methods for identifying cancer-associated mutations and rearrangements in whole-genome sequencing (WGS) data. Leaders of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cancer genomics projects are joining with Sage Bionetworks and IBM-DREAM to initiate this innovative open crowd-sourced Challenge [1-3].","","https://www.synapse.org/#!Synapse:syn312572","completed","intermediate","1","","","2013-12-14","2016-04-22","2023-06-23 00:00:00","2023-10-14 05:38:15" +"10","acute-myeloid-leukemia-outcome-prediction","Acute Myeloid Leukemia Outcome Prediction","","The AML Outcome Prediction Challenge provides a unique opportunity to access and interpret a rich dataset for AML patients that includes clinical covariates, select gene mutation status and proteomic data. Capitalizing on a unique AML reverse phase protein array (RPPA) dataset obtained at M.D. Anderson Cancer Center that captures 271 measurements for each patient, participants of the DREAM 9 Challenge will help uncover what drives AML. Outcomes of this Challenge have the potential to be used immediately to tailor therapies for newly diagnosed leukemia patients and to accelerate the development of new drugs for leukemia.","","https://www.synapse.org/#!Synapse:syn2455683","completed","intermediate","1","","","2014-06-02","2014-09-15","2023-06-23 00:00:00","2023-10-14 05:38:16" +"11","broad-dream-gene-essentiality-prediction","Broad-DREAM Gene Essentiality Prediction","","The goal of this project is to use a crowd-based competition to develop predictive models that can infer gene dependency scores in cancer cells (genes that are essential to cancer cell viability when suppressed) using features of those cell lines. An additional goal is to find a small set of biomarkers (gene expression, copy number, and mutation features) that can best predict a single gene or set of genes.","","https://www.synapse.org/#!Synapse:syn2384331","completed","intermediate","1","","","2014-06-02","2014-09-29","2023-06-23 00:00:00","2023-10-14 05:38:16" +"12","alzheimers-disease-big-data","Alzheimer's Disease Big Data","","The goal of the Alzheimer's Disease Big Data DREAM Challenge #1 (AD#1) was to apply an open science approach to rapidly identify accurate predictive AD biomarkers that can be used by the scientific, industrial and regulatory communities to improve AD diagnosis and treatment. AD#1 will be the first in a series of AD Data Challenges to leverage genetics and brain imaging in combination with cognitive assessments, biomarkers and demographic information from cohorts ranging from cognitively normal to mild cognitively impaired to individuals with AD.","","https://www.synapse.org/#!Synapse:syn2290704","completed","intermediate","1","","","2014-06-02","2014-10-17","2023-06-23 00:00:00","2023-10-14 05:38:17" +"13","olfaction-prediction","Olfaction Prediction","","The goal of the DREAM Olfaction Prediction Challenge is to find models that can predict how a molecule smells from its physical and chemical features. A model that allows us to predict a smell from a molecule will provide fundamental insights into how odor chemicals are transformed into a smell percept in the brain. Further, being able to predict how a chemical smells will greatly accelerate the design of new molecules to be used as fragrances. Currently, fragrance chemists synthesize many molecules to obtain a new ingredient, but most of these will not have the desired qualities.","","https://www.synapse.org/#!Synapse:syn2811262","completed","intermediate","1","","","2015-01-15","2015-05-01","2023-06-23 00:00:00","2023-10-14 05:38:17" +"14","prostate-cancer","Prostate Cancer","","This challenge will attempt to improve the prediction of survival and toxicity of docetaxel treatment in patients with metastatic castration-resistant prostate cancer (mCRPC). The primary benefit of this Challenge will be to establish new quantitative benchmarks for prognostic modeling in mCRPC, with a potential impact for clinical decision making and ultimately understanding the mechanism of disease progression. Participating teams will be asked to submit predictive models based on clinical variables from the comparator arms of four phase III clinical trials with over 2,000 mCRPC patients treated with first-line docetaxel. The comparator arm of a clinical trial represents the patients that receive a treatment that is considered to be effective. This arm of the clinical trial is used to evaluate the effectiveness of the new therapy being tested.","","https://www.synapse.org/#!Synapse:syn2813558","completed","intermediate","1","","","2015-03-16","2015-07-27","2023-06-23 00:00:00","2023-10-14 05:38:18" +"15","als-stratification-prize4life","ALS Stratification Prize4Life","","As illustrated by the overview figure below, (a) Challenge Data includes data from ALS clinical trials and ALS registries. ALS clinical trials consist of patients from clinical trials available open access on the PRO-ACT database and patients from 6 clinical trials not yet added into the database. Data from ALS registries was collected from patients in national ALS registries. (b) Data is divided into three subsets-training data provided to solvers in full, leaderboard, and validation data that is available only to the organizers and is reserved for the scoring of the challenge. (c) The goal of this challenge is then to predict the Clinical Targets, i.e. the disease progression as ALSFRS slope as well as survival. (d) For Building the Models, participants create two algorithms-one that selects features and one that predicts outcomes. To perform predictions, data from a given patient is fed into the selector . The selector selects 6 features and a cluster/model ID (3), e.g. from a...","","https://www.synapse.org/#!Synapse:syn2873386","completed","intermediate","1","","","2015-06-22","2015-10-04","2023-06-23 00:00:00","2023-10-14 05:38:19" +"16","astrazeneca-sanger-drug-combination-prediction","AstraZeneca-Sanger Drug Combination Prediction","","To accelerate the understanding of drug synergy, AstraZeneca has partnered with the European Bioinformatic Institute, the Sanger Institute, Sage Bionetworks, and the distributed DREAM community to launch the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. This Challenge is designed to explore fundamental traits that underlie effective combination treatments and synergistic drug behavior using baseline genomic data, i.e. data collected pretreatment. As the basis of the Challenge, AstraZeneca is releasing ~11.5k experimentally tested drug combinations measuring cell viability over 118 drugs and 85 cancer cell lines (primarily colon, lung, and breast), and monotherapy drug response data for each drug and cell line. Moreover, in coordination with the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Sanger Institute, genomic data including gene expression, mutations (whole exome), copy-number alterations, and methylation data will be released into the publ...","","https://www.synapse.org/#!Synapse:syn4231880","completed","intermediate","1","","","2015-09-03","2016-03-14","2023-06-23 00:00:00","2023-10-14 05:38:19" +"17","smc-dna-meta","SMC-DNA Meta","","The goal of this Challenge is to identify the most accurate meta-pipeline for somatic mutation detection, and establish the state-of-the-art. The algorithms in this Challenge must use as input mutations predicted by one or more variant callers and output mutation calls associated with cancer. An additional goal is to highlight the complementarity of the calling algorithms and help understand their individual advantages/deficiencies.","","https://www.synapse.org/#!Synapse:syn4588939","completed","intermediate","1","","","2015-08-17","2016-04-10","2023-06-23 00:00:00","2023-10-14 05:38:20" +"18","smc-het","SMC-Het","","The ICGC-TCGA DREAM Somatic Mutation Calling-Tumour Heterogeneity Challenge (SMC-Het) is an international effort to improve standard methods for subclonal reconstruction-to quantify and genotype each individual cell population present within a tumor. Leaders of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cancer genomics projects are joining with Sage Bionetworks and IBM-DREAM to initiate this innovative open crowd-sourced Challenge [1-3].","","https://www.synapse.org/#!Synapse:syn2813581","completed","intermediate","1","","","2015-11-16","2016-06-30","2023-06-23 00:00:00","2023-10-14 05:38:21" +"19","respiratory-viral","Respiratory Viral","","Respiratory viruses are highly infectious and cause acute illness in millions of people every year. However, there is wide variation in the physiologic response to exposure at the individual level. Some people that are exposed to virus are able to completely avoid infection. Others contract virus but are able to fight it off without exhibiting any symptoms of illness such as coughing, sneezing, sore throat or fever. It is not well understood what characteristics may protect individuals from respiratory viral infection. These individual responses are likely influenced by multiple processes including both the basal state of the human host upon exposure and the dynamics of host immune response in the early hours immediately following exposure. Many of these processes play out in the peripheral blood through activation and recruitment of circulating immune cells. Global gene expression patterns measured in peripheral blood at the time of symptom onset-several days after viral exposure...","","https://www.synapse.org/#!Synapse:syn5647810","completed","intermediate","1","","","2016-05-16","2016-09-28","2023-06-23 00:00:00","2023-10-14 05:38:21" +"20","disease-module-identification","Disease Module Identification","","The Disease Module Identification DREAM Challenge is an open community effort to systematically assess module identification methods on a panel of state-of-the-art genomic networks and leverage the “wisdom of crowds” to discover novel modules and pathways underlying complex diseases.","","https://www.synapse.org/#!Synapse:syn6156761","completed","intermediate","1","https://doi.org/10.1038/s41592-019-0509-5","","2016-06-24","2016-10-01","2023-06-23 00:00:00","2023-10-16 21:17:48" +"21","encode","ENCODE","","Transcription factors (TFs) are regulatory proteins that bind specific DNA sequence patterns (motifs) in the genome and affect transcription rates of target genes. Binding sites of TFs differ across cell types and experimental conditions. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an experimental method that is commonly used to obtain the genome-wide binding profile of a TF of interest in a specific cell type/condition. However, profiling the binding landscape of every TF in every cell type/condition is infeasible due to constraints on cost, material and effort. Hence, accurate computational prediction of in vivo TF binding sites is critical to complement experimental results.","","https://www.synapse.org/#!Synapse:syn6131484","completed","intermediate","1","","","2016-07-07","2017-01-11","2023-06-23 00:00:00","2023-10-14 05:38:26" +"22","idea","Idea","","The DREAM Idea Challenge is designed to collaboratively shape and enable the solution of a question fundamental to improving human health. In the process, all proposals and their evaluation will be made publicly available for the explicit purpose of connecting modelers and experimentalists who want to address the same question. This Wall of Models will enable new collaborations, and help turn every good modeling idea into a success story. It will further serve as a basis for new DREAM challenges.","","https://www.synapse.org/#!Synapse:syn5659209","completed","advanced","1","","","2016-06-15","2017-04-30","2023-06-23 00:00:00","2023-10-14 05:38:26" +"23","smc-rna","SMC-RNA","","The ICGC-TCGA DREAM Somatic Mutation Calling-RNA Challenge (SMC-RNA) is an international effort to improve standard methods for identifying cancer-associated rearrangements in RNA sequencing (RNA-seq) data. Leaders of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cancer genomics projects are joining with Sage Bionetworks and IBM-DREAM to initiate this innovative open crowd-sourced Challenge [1-3].","","https://www.synapse.org/#!Synapse:syn2813589","completed","intermediate","1","","","2016-06-29","2017-05-02","2023-06-23 00:00:00","2023-10-14 05:38:29" +"24","digital-mammography","Digital Mammography","","The Digital Mammography DREAM Challenge will attempt to improve the predictive accuracy of digital mammography for the early detection of breast cancer. The primary benefit of this Challenge will be to establish new quantitative tools-machine learning, deep learning or other-that can help decrease the recall rate of screening mammography, with a potential impact on shifting the balance of routine breast cancer screening towards more benefit and less harm. Participating teams will be asked to submit predictive models based on over 640,000 de-identified digital mammography images from over 86000 subjects, with corresponding clinical variables.","","https://www.synapse.org/#!Synapse:syn4224222","completed","advanced","1","https://doi.org/10.1001/jamanetworkopen.2020.0265","","2016-11-18","2017-05-16","2023-06-23 00:00:00","2023-10-14 05:38:29" +"25","multiple-myeloma","Multiple Myeloma","","Multiple myeloma (MM) is a cancer of the plasma cells in the bone marrow, with about 25,000 newly diagnosed patients per year in the United States alone. The disease's clinical course depends on a complex interplay of clinical traits and molecular characteristics of the plasma cells.1 Since risk-adapted therapy is becoming standard of care, there is an urgent need for a precise risk stratification model to assist in therapeutic decision-making and research. While progress has been made, there remains a significant opportunity to improve patient stratification to optimize treatment and to develop new therapies for high-risk patients. A DREAM Challenge represents a chance not only to integrate available data and analytical approaches to tackle this important problem, but also provides the ability to benchmark potential methods to identify those with the greatest potential to yield patient care benefits in the future.","","https://www.synapse.org/#!Synapse:syn6187098","completed","intermediate","1","","","2017-06-30","2017-11-08","2023-06-23 00:00:00","2023-10-14 05:38:31" +"26","ga4gh-dream-workflow-execution","GA4GH-DREAM Workflow Execution","","The highly distributed and disparate nature of genomic and clinical data generated around the world presents an enormous challenge for those scientists who wish to integrate and analyze these data. The sheer volume of data often exceeds the capacity for storage at any one site and prohibits the efficient transfer between sites. To address this challenge, researchers must bring their computation to the data. Numerous groups are now developing technologies and best practice methodologies for running portable and reproducible genomic analysis pipelines as well as tools and APIs for discovering genomic analysis resources. Software development, deployment, and sharing efforts in these groups commonly rely on the use of modular workflow pipelines and virtualization based on Docker containers and related tools.","","https://www.synapse.org/#!Synapse:syn8507133","completed","intermediate","1","","","2017-07-21","2017-12-31","2023-06-23 00:00:00","2023-10-14 05:38:31" +"27","parkinsons-disease-digital-biomarker","Parkinson's Disease Digital Biomarker","","The Parkinson's Disease Digital Biomarker DREAM Challenge is a first of it's kind challenge, designed to benchmark methods for the processing of sensor data for development of digital signatures reflective of Parkinson's Disease. Participants will be provided with raw sensor (accelerometer, gyroscope, and magnetometer) time series data recorded during the performance of pre-specified motor tasks, and will be asked to extract data features which are predictive of PD pathology. In contrast to traditional DREAM challenges, this one will focus on feature extraction rather than predictive modeling, and submissions will be evaluated based on their ability to predict disease phenotype using an array of standard machine learning algorithms.","","https://www.synapse.org/#!Synapse:syn8717496","completed","intermediate","1","","","2017-07-06","2017-11-10","2023-06-23 00:00:00","2023-10-14 05:38:32" +"28","nci-cptac-proteogenomics","NCI-CPTAC Proteogenomics","","Cancer is driven by aberrations in the genome [1,2], and these alterations manifest themselves largely in the changes in the structure and abundance of proteins, the main functional gene products. Hence, characterization and analyses of alterations in the proteome has the promise to shed light into cancer development and may improve development of both biomarkers and therapeutics. Measuring the proteome is very challenging, but recent rapid technology developments in mass spectrometry are enabling deep proteomics analysis [3]. Multiple initiatives have been launched to take advantage of this development to characterize the proteome of tumours, such as the Clinical Proteomic Tumor Analysis Consortium (CPTAC). These efforts hold the promise to revolutionize cancer research, but this will only be possible if the community develops computational tools powerful enough to extract the most information from the proteome, and to understand the association between genome, transcriptome and ...","","https://www.synapse.org/#!Synapse:syn8228304","completed","intermediate","1","","","2017-06-26","2017-11-20","2023-06-23 00:00:00","2023-10-14 05:38:33" +"29","multi-targeting-drug","Multi-Targeting Drug","","The objective of this challenge is to incentivize development of methods for predicting compounds that bind to multiple targets. In particular, methods that are generalizable to multiple prediction problems are sought. To achieve this, participants will be asked to predict 2 separate compounds, each having specific targets to which they should bind, and a list of anti-targets to avoid. Participants should use the same methods to produce answers for questions 1 and 2.","","https://www.synapse.org/#!Synapse:syn8404040","completed","intermediate","1","","","2017-10-05","2018-02-26","2023-06-23 00:00:00","2023-10-14 05:38:33" +"30","single-cell-transcriptomics","Single Cell Transcriptomics","","In this Challenge on Single-Cell Transcriptomics, participants will reconstruct the location of single cells in the Drosophila embryo using single-cell transcriptomic data. Data will be made available in late August and participating challenge teams can work on the data and submit their results previous to the DREAM Conference. The best performers will be announced at the DREAM conference on Dec 8.","","https://www.synapse.org/#!Synapse:syn15665609","completed","intermediate","1","","","2018-09-04","2018-11-21","2023-06-23 00:00:00","2023-10-14 05:38:34" +"31","idg-drug-kinase-binding","IDG Drug-Kinase Binding","","This IDG-DREAM Drug-Kinase Binding Prediction Challenge seeks to evaluate the power of statistical and machine learning models as a systematic and cost-effective means for catalyzing compound-target interaction mapping efforts by prioritizing most potent interactions for further experimental evaluation. The Challenge will focus on kinase inhibitors, due to their clinical importance [2], and will be implemented in a screening-based, pre-competitive drug discovery project in collaboration with theIlluminating the Druggable Genome (IDG) Kinase-focused Data and Resource Generation Center, consortium, with the aim to establish kinome-wide target profiles of small-molecule agents, with the goal of extending the druggability of the human kinome space.","","https://www.synapse.org/#!Synapse:syn15667962","completed","intermediate","1","","","2018-10-01","2019-04-18","2023-06-23 00:00:00","2023-10-14 05:38:35" +"32","malaria","Malaria","","The Malaria DREAM Challenge is open to anyone interested in contributing to the development of computational models that address important problems in advancing the fight against malaria. The overall goal of the first Malaria DREAM Challenge is to predict Artemisinin (Art) drug resistance level of a test set of malaria parasites using their in vitro transcription data and a training set consisting of published in vivo and unpublished in vitrotranscriptomes. The in vivodataset consists of ~1000 transcription samples from various geographic locations covering a wide range of life cycles and resistance levels, with other accompanying data such as patient age, geographic location, Art combination therapy used, etc [Mok et al (2015) Science]. The in vitro transcription dataset consists of 55 isolates, with transcription collected at two timepoints (6 and 24 hours post-invasion), in the absence or presence of an Art perturbation, for two biological replicates using a custom microarray a...","","https://www.synapse.org/#!Synapse:syn16924919","completed","intermediate","1","","","2019-04-30","2019-08-15","2023-06-23 00:00:00","2023-10-14 05:38:35" +"33","preterm-birth-prediction-transcriptomics","Preterm Birth Prediction - Transcriptomics","","A basic need in pregnancy care is to establish gestational age, and inaccurate estimates may lead to unnecessary interventions and sub-optimal patient management. Current approaches to establish gestational age rely on patient's recollection of her last menstrual period and/or ultrasound, with the latter being not only costly but also less accurate if not performed during the first trimester of pregnancy. Therefore development of an inexpensive and accurate molecular clock of pregnancy would be of benefit to patients and health care systems. Participants in sub-challenge 1 (Prediction of gestational age) will be given whole blood gene topic_3170 collected from pregnant women to develop prediction models for the gestational age at blood draw. Another challenge in obstetrics, in both low and high-income countries, is identification and treatment of women at risk of developing the ‘great obstetrical syndromes‘. Of these, preterm birth (PTB), defined as giving birth prior to completio...","","https://www.synapse.org/#!Synapse:syn18380862","completed","good_for_beginners","1","","","2019-05-04","2019-12-05","2023-06-23 00:00:00","2023-10-14 05:38:36" +"34","single-cell-signaling-in-breast-cancer","Single-Cell Signaling in Breast Cancer","","Signaling underlines nearly every cellular event. Individual cells, even if genetically identical, respond to perturbation in different ways. This underscores the relevance of cellular heterogeneity, in particular in how cells respond to drugs. This is of high relevance since the fact that a subset of cells do not respond (or only weakly) to drugs can render this drug an ineffective treatment. In spite of its relevance to many diseases, comprehensive studies on the heterogeneous signaling in single cells are still lacking. We have generated the, to our knowledge, currently largest single cell signaling dataset on a panel of 67 well-characterized breast cancer cell lines by mass cytometry (3'015 conditions, ~80 mio single cells, 38 markers; Bandura et al. 2009; Bendall et al., 2011; Bodenmiller et al., 2012; Lun et al., 2017; Lun et al., 2019). These cell lines are, among others, also characterized at the genomic, transcriptomic, and proteomic level (Marcotte et al., 2016). We ask ...","","https://www.synapse.org/#!Synapse:syn20366914","completed","intermediate","1","","","2018-08-20","2019-11-15","2023-06-23 00:00:00","2023-10-14 05:38:37" +"35","ehr-dream-challenge-patient-mortality-prediction","EHR DREAM Challenge - Patient Mortality Prediction","","The recent advent of new CRISPR-based molecular tools allows the reconstruction of cell lineages based on the phylogenetical analysis of DNA mutations induced by CRISPR during development and promises to solve the lineage of complex model organisms at single-cell resolution (see image from McKenna et al Science 2016). To date, however, no lineage reconstruction algorithms have been rigorously examined for their performance/robustness across diverse molecular tools, datasets, and number of cells/size of lineage trees. It also remains unclear whether new Machine-Learning algorithms that go beyond the classical ones developed for reconstructing phylogenetic trees, could consistently reconstruct cell lineages to a high degree of accuracy. The challenge-a partnership between The Allen Institute and DREAM-will comprise 3 subchallenges that consist of reconstructing cell lineage trees of different sizes and nature. In subchallenge 1, participants will be given experimental molecular data...","","https://www.synapse.org/#!Synapse:syn18405991","completed","intermediate","1","https://doi.org/10.1093/jamia/ocad159","operation_0231","2019-09-09","2020-01-23","2023-06-23 00:00:00","2023-10-19 00:12:33" +"36","allen-institute-cell-lineage-reconstruction","Allen Institute Cell Lineage Reconstruction","","The recent advent of new CRISPR-based molecular tools allows the reconstruction of cell lineages based on the phylogenetical analysis of DNA mutations induced by CRISPR during development and promises to solve the lineage of complex model organisms at single-cell resolution. To date, however, no lineage reconstruction algorithms have been rigorously examined for their performance/robustness across diverse molecular tools, datasets, and number of cells/size of lineage trees. It also remains unclear whether new Machine-Learning algorithms that go beyond the classical ones developed for reconstructing phylogenetic trees, could consistently reconstruct cell lineages to a high degree of accuracy. The challenge-a partnership between The Allen Institute and DREAM-will comprise 3 subchallenges that consist of reconstructing cell lineage trees of different sizes and nature. In subchallenge 1, participants will be given experimental molecular data to reconstruct in vitro cell lineages of l...","","https://www.synapse.org/#!Synapse:syn20692755","completed","intermediate","1","","operation_0231","2019-10-15","2020-02-06","2023-06-23 00:00:00","2023-10-19 00:12:46" +"37","tumor-deconvolution","Tumor Deconvolution","","The extent of stromal and immune cell infiltration within solid tumors has prognostic and predictive significance. Unfortunately, expression profiling of tumors has, until very recently, largely been undertaken using bulk techniques (e.g., microarray and RNA-seq). Unlike single-cell methods (e.g., single-cell RNA-seq, FACS, mass cytometry, or immunohistochemistry), bulk approaches average expression across all cells (cancer, stromal, and immune) within the sample and, hence, do not directly quantitate tumor infiltration. This information can be recovered by computational tumor deconvolution methods, which would thus allow interrogation of immune subpopulations across the large collection of public bulk topic_3170sets. The goal of this Challenge is to evaluate the ability of computational methods to deconvolve bulk topic_3170, reflecting a mixture of cell types, into individual immune components. Methods will be assessed based on in vitro and in silico admixtures specifically gener...","","https://www.synapse.org/#!Synapse:syn15589870","completed","intermediate","1","","","2019-06-26","2020-04-30","2023-06-23 00:00:00","2023-10-14 05:38:39" +"38","ctd2-pancancer-drug-activity","CTD2 Pancancer Drug Activity","","Over the last two years, the Columbia CTD2 Center developed PANACEA (Pancancer Analysis of Chemical Entity Activity), a comprehensive repertoire of dose response curves and molecular profiles representative of cellular responses to drug perturbations. PANACEA covers a broad spectrum of cellular contexts representative of poor outcome malignancies, including rare ones such as GIST sarcoma and gastroenteropancreatic neuroendocrine tumors (GEP-NETs). PANACEA is uniquely suited to support DREAM Challenges related to the elucidation of drug mechanism of action (MOA), drug sensitivity, and drug synergy. The goal of the CTD2 Pancancer Drug Activity DREAM Challenge is to foster the development and benchmarking of algorithms to predict targets of chemotherapeutic compounds from post-treatment transcriptional data.","","https://www.synapse.org/#!Synapse:syn20968331","completed","good_for_beginners","1","","","2019-12-02","2020-02-13","2023-06-23 00:00:00","2023-10-20 23:11:10" +"39","ctd2-beataml","CTD2 BeatAML","","In the era of precision medicine, AML patients have few therapeutic options, with “7 + 3” induction chemotherapy having been the standard for decades (Bertoli et al. 2017). While several agents targeting the myeloid marker CD33 or alterations in FLT3 or IDH2 have demonstrated efficacy in patients (Wei and Tiong 2017), responses are uncertain in some populations (Castaigne et al. 2012) and relapse remains prevalent (Stone et al. 2017). These drugs highlight both the promise of targeted therapies in AML and the urgent need for additional treatment options that are tailored to more refined patient subpopulations in order to achieve durable responses. The BeatAML initiative was launched as a comprehensive study of the relationship between molecular alterations and ex-vivo drug sensitivity in patients with AML. One of the primary goals of this multi-center study was to develop a discovery cohort that could yield new drug target hypotheses and predictive biomarkers of therapeutic respon...","","https://www.synapse.org/#!Synapse:syn20940518","completed","good_for_beginners","1","","","2019-12-19","2020-04-28","2023-06-23 00:00:00","2023-10-14 05:38:42" +"40","metadata-automation","Metadata Automation","","The Cancer Research Data Commons (CRDC) will collate data across diverse groups of cancer researchers, each collecting biomedical data in different formats. This means the data must be retrospectively harmonized and transformed to enable this data to be submitted. In addition, to be findable by the broader scientific community, coherent information (metadata) is necessary about the data fields and values. Coherent metadata annotation of the data fields and their values can enable computational data transformation, query, and analysis. Creation of this type of descriptive metadata can require biomedical expertise to determine the best annotations and thus is a time-consuming and manual task which is both an obstacle and a bottleneck in data sharing and submissions. Goal-Using structured biomedical data files, challenge participants will develop tools to semi-automate annotation of metadata fields and values, using available research data annotations (e.g. caDSR CDEs) as well as es...","","https://www.synapse.org/#!Synapse:syn18065891","completed","intermediate","1","","","2020-01-14","2020-06-02","2023-06-23 00:00:00","2023-10-14 05:38:42" +"41","automated-scoring-of-radiographic-joint-damage","Automated Scoring of Radiographic Joint Damage","","The purpose of the RA2-DREAM Challenge is to develop an automated method to quickly and accurately quantify the degree of joint damage associated with rheumatoid arthritis (RA). Based on radiographs of the hands and feet, a novel, automated scoring method could be applied broadly for patient care and research. We challenge participants to develop algorithms to automatically assess joint space narrowing and erosions using a large set of existing radiographs with damage scores generated by visual assessment of images by trained readers using standard protocols. The end result will be a generalizable, publicly available, automated method to generate accurate, reproducible and unbiased RA damage scores to replace the current tedious, expensive, and non-scalable method of scoring by human visual inspection.","","https://www.synapse.org/#!Synapse:syn20545111","completed","intermediate","1","","","2019-11-04","2020-05-21","2023-06-23 00:00:00","2023-10-18 00:38:55" +"42","beat-pd","BEAT-PD","","Recent advances in mobile health have demonstrated great potential to leverage sensor-based technologies for quantitative, remote monitoring of health and disease-particularly for diseases affecting motor function such as Parkinson's disease. Such approaches have been rolled out using research-grade wearable sensors and, increasingly, through the use of smartphones and consumer wearables, such as smart watches and fitness trackers. These devices not only provide the ability to measure much more detailed disease phenotypes but also provide the ability to follow patients longitudinally with much higher frequency than is possible through clinical exams. However, the conversion of sensor-based data streams into digital biomarkers is complex and no methodological standards have yet evolved to guide this process. Parkinson's disease (PD) is a neurodegenerative disease that primarily affects the motor system but also exhibits other symptoms. Typical motor symptoms of the disease include...","","https://www.synapse.org/#!Synapse:syn20825169","completed","intermediate","1","","","2020-01-13","2020-05-13","2023-06-23 00:00:00","2023-10-14 05:38:45" +"43","ctd2-pancancer-chemosensitivity","CTD2 Pancancer Chemosensitivity","","Over the last two years, the Columbia CTD2 Center developed PANACEA (Pancancer Analysis of Chemical Entity Activity), a comprehensive repertoire of dose response curves and molecular profiles representative of cellular responses to drug perturbations. PANACEA covers a broad spectrum of cellular contexts representative of poor outcome malignancies, including rare ones such as GIST sarcoma and gastroenteropancreatic neuroendocrine tumors (GEP-NETs). PANACEA is uniquely suited to support DREAM Challenges related to the elucidation of drug mechanism of action (MOA), drug sensitivity, and drug synergy. The goal of this Challenge is to foster development and benchmarking of algorithms to predict the sensitivity, as measured by the area under the dose-response curve, of a cell line to a compound based on the baseline transcriptional profiles of the cell line. The drug perturbational RNAseq profiles of 11 cell lines for 30 chosen compounds will be provided to challenge participants, with...","","https://www.synapse.org/#!Synapse:syn21763589","completed","good_for_beginners","1","","","2020-04-28","2020-07-27","2023-06-23 00:00:00","2023-10-14 05:38:45" +"44","ehr-dream-challenge-covid-19","EHR DREAM Challenge-COVID-19","","The rapid rise of COVID-19 has challenged healthcare globally. The underlying risks and outcomes of infection are still incompletely characterized even as the world surpasses 4 million infections. Due to the importance and emergent need for better understanding of the condition and the development of patient specific clinical risk scores and early warning tools, we have developed a platform to support testing analytic and machine learning hypotheses on clinical data without data sharing as a platform to rapidly discover and implement approaches for care. We have previously applied this approach in the successful EHR DREAM Challenge focusing on Patient Mortality Prediction with UW Medicine. We have the goal of incorporating machine learning and predictive algorithms into clinical care and COVID-19 is an important and highly urgent challenge. In our first iteration, we will facilitate understanding risk factors that lead to a positive test utilizing electronic health recorded dat...","","https://www.synapse.org/#!Synapse:syn21849255","completed","intermediate","1","https://doi.org/10.1001/jamanetworkopen.2021.24946","","2020-04-30","2021-07-01","2023-06-23 00:00:00","2023-10-14 05:38:46" +"45","anti-pd1-response-prediction","Anti-PD1 Response Prediction","","While durable responses and prolonged survival have been demonstrated in some lung cancer patients treated with immuno-oncology (I-O) anti-PD-1 therapy, there remains a need to improve the ability to predict which patients are more likely to receive benefit from treatment with I-O. The goal of this challenge is to leverage clinical and biomarker data to develop predictive models of response to I-O therapy in lung cancer and ultimately gain insights that may facilitate potential novel monotherapies or combinations with I-O.","","https://www.synapse.org/#!Synapse:syn18404605","completed","intermediate","1","","","2020-11-17","2021-02-25","2023-06-23 00:00:00","2023-10-14 05:38:47" +"46","brats-2021-challenge","BraTS 2021 Challenge","","Glioblastoma, and diffuse astrocytic glioma with molecular features of glioblastoma (WHO Grade 4 astrocytoma), are the most common and aggressive malignant primary tumor of the central nervous system in adults, with extreme intrinsic heterogeneity in appearance, shape, and histology. Glioblastoma patients have very poor prognosis, and the current standard of care treatment comprises surgery, followed by radiotherapy and chemotherapy. The International Brain Tumor Segmentation (BraTS) Challenges —which have been running since 2012— assess state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans.","","https://www.synapse.org/#!Synapse:syn25829067","completed","advanced","1","","","2021-07-07","2021-10-15","2023-06-23 00:00:00","2023-10-14 05:38:48" +"47","cancer-data-registry-nlp","Cancer Data Registry NLP","","A critical bottleneck in translational and clinical research is access to large volumes of high-quality clinical data. While structured data exist in medical EHR systems, a large portion of patient information including patient status, treatments, and outcomes is contained in unstructured text fields. Research in Natural Language Processing (NLP) aims to unlock this hidden and often inaccessible information. However, numerous challenges exist in developing and evaluating NLP methods, much of it centered on having “gold-standard” metrics for evaluation, and access to data that may contain personal health information (PHI). This DREAM Challenge will focus on the development and evaluation of of NLP algorithms that can improve clinical trial matching and recruitment.","","https://www.synapse.org/#!Synapse:syn18361217","upcoming","intermediate","1","","","\N","\N","2023-06-23 00:00:00","2023-10-14 05:38:49" +"48","barda-community-challenge-pediatric-covid-19-data-challenge","BARDA Community Challenge - Pediatric COVID-19 Data Challenge","","While most children with COVID-19 are asymptomatic or have mild symptoms, healthcare providers have difficulty determining which among their pediatric patients will progress to moderate or severe COVID-19 early in the progression. Some of these patients develop multisystem inflammatory syndrome in children (MIS-C), a life-threatening inflammation of organs and tissues. Methods to distinguish children at risk for severe COVID-19 complications, including conditions such as MIS-C, are needed for earlier interventions to improve pediatric patient outcomes. Multiple HHS divisions are coming together for a data challenge competition that will leverage de-identified electronic health record data to develop, train and validate computational models that can predict severe COVID-19 complications in children, equipping healthcare providers with the information and tools they need to identify pediatric patients at risk.","","https://www.synapse.org/#!Synapse:syn25875374/wiki/611225","completed","intermediate","1","","","2021-08-19","2021-12-17","2023-06-23 00:00:00","2023-10-14 05:38:50" +"49","brats-continuous-evaluation","BraTS Continuous Evaluation","","Brain tumors are among the deadliest types of cancer. Specifically, glioblastoma, and diffuse astrocytic glioma with molecular features of glioblastoma (WHO Grade 4 astrocytoma), are the most common and aggressive malignant primary tumor of the central nervous system in adults, with extreme intrinsic heterogeneity in appearance, shape, and histology, with a median survival of approximately 15 months. Brain tumors in general are challenging to diagnose, hard to treat and inherently resistant to conventional therapy because of the challenges in delivering drugs to the brain, as well as the inherent high heterogeneity of these tumors in their radiographic, morphologic, and molecular landscapes. Years of extensive research to improve diagnosis, characterization, and treatment have decreased mortality rates in the U.S by 7% over the past 30 years. Although modest, these research innovations have not translated to improvements in survival for adults and children in low-and middle-income...","","https://www.synapse.org/brats_ce","completed","advanced","1","","","2022-01-01","\N","2023-06-23 00:00:00","2023-10-14 05:38:51" +"50","fets-2022","FeTS 2022","","FeTS 2022 focuses on benchmarking methods for federated learning (FL), and particularly i) weight aggregation methods for federated training, and ii) algorithmic generalizability on out-of-sample data based on federated evaluation. In line with its last instance (FeTS 2021-the 1st FL challenge ever organized), FeTS 2022 targets the task of brain tumor segmentation and builds upon i) the centralized dataset of >8,000 clinically-acquired multi-institutional MRI scans (from the RSNA-ASNR-MICCAI BraTS 2021 challenge) with their real-world partitioning, and ii) the collaborative network of remote independent institutions included in a real-world federation. Participants are welcome to compete in either of the two challenge tasks- Task 1 (“Federated Training”) seeks effective weight aggregation methods for the creation of a consensus model given a pre-defined segmentation algorithm for training, while also (optionally) accounting for network outages. Task 2 (“Federated Evaluation”) see...","","https://www.synapse.org/#!Synapse:syn28546456/wiki/617093","completed","advanced","1","","","2022-04-08","2022-08-15","2023-06-23 00:00:00","2023-10-18 00:36:14" +"51","random-promotor","Random Promotor","","Decoding how gene expression is regulated is critical to understanding disease. Regulatory DNA is decoded by the cell in a process termed “cis-regulatory logic”, where proteins called Transcription Factors (TFs) bind to specific DNA sequences within the genome and work together to produce as output a level of gene expression for downstream adjacent genes. This process is exceedingly complex to model as a large number of parameters is needed to fully describe the process (see Rationale, de Boer et al. 2020; Zeitingler J. 2020). Understanding the cis-regulatory logic of the human genome is an important goal and would provide insight into the origins of many diseases. However, learning models from human data is challenging due to limitations in the diversity of sequences present within the human genome (e.g. extensive repetitive DNA), the vast number of cell types that differ in how they interpret regulatory DNA, limited reporter assay data, and substantial technical biases present ...","","https://www.synapse.org/#!Synapse:syn28469146/wiki/617075","completed","intermediate","1","","","2022-05-02","2022-08-07","2023-06-23 00:00:00","2023-10-14 05:38:53" +"52","preterm-birth-prediction-microbiome","Preterm Birth Prediction - Microbiome","","Globally, about 11% of infants every year are born preterm, defined as birth prior to 37 weeks of gestation, totaling nearly 15 million births.(5) In addition to the emotional and financial toll on families, preterm births have higher rates of neonatal death, nearly 1 million deaths each year, and long-term health consequences for some children. Infants born preterm are at risk for a variety of adverse outcomes, such as respiratory illnesses, cerebral palsy, infections, and blindness, with infants born very preterm (i.e., before 32 weeks) at increased risk of these conditions.(6) The ability to accurately predict which women are at a higher risk for preterm birth would help healthcare providers to treat in a timely manner those at higher risk of delivering preterm. Currently available treatments for pregnant women at risk of preterm delivery include corticosteroids for fetal maturation and magnesium sulfate provided prior to 32 weeks to prevent cerebral palsy.(7) There are several...","","https://www.synapse.org/#!Synapse:syn26133770/wiki/612541","completed","advanced","1","","","2022-07-19","2022-09-16","2023-06-23 00:00:00","2023-10-14 05:38:54" +"53","finrisk","FINRISK - Heart Failure and Microbiome","","Cardiovascular diseases are the leading cause of death both in men and women worldwide. Heart failure (HF) is the most common form of heart disease, characterised by the heart's inability to pump a sufficient supply of blood to meet the needs of the body. The lifetime risk of developing HF is roughly 20%, yet, it remains difficult to diagnose due to its and a lack of agreement of diagnostic criteria. As the diagnosis of HF is dependent on ascertainment of clinical histories and appropriate screening of symptomatic individuals, identifying those at risk of HF is essential. This DREAM challenge focuses on the prediction of HF using a combination of gut microbiome and clinical variables. This challenge is designed to predict incident risk for heart failure in a large human population study of Finnish adults, FINRISK 2002 (Borodulin et al., 2018). The FINRISK study has been conducted in Finland to investigate the risk factors for cardiovascular disease every 5 years since 1972. A rand...","","https://www.synapse.org/#!Synapse:syn27130803/wiki/616705","completed","advanced","1","","","2022-09-20","2023-01-30","2023-06-23 00:00:00","2023-10-16 21:19:55" +"54","scrna-seq-and-scatac-seq-data-analysis","scRNA-seq and scATAC-seq Data Analysis","","Understanding transcriptional regulation at individual cell resolution is fundamental to understanding complex biological systems such as tissues and organs. Emerging high-throughput sequencing technologies now allow for transcript quantification and chromatin accessibility at the single cell level. These technologies present unique challenges due to inherent data sparsity. Proper signal correction is key to accurate gene expression quantification via scRNA-seq, which propagates into downstream analyses such as differential gene expression analysis and cell-type identification. In the even more sparse scATAC-seq data, the correct identification of informative features is key to assessing cell heterogeneity at the chromatin level. The aims of this challenge will be two-fold- 1) To evaluate computational methods for signal correction and peak identification in scRNA-seq and scATAC-seq, respectively; 2) To assess the impact of these methods on downstream analysis","","https://www.synapse.org/#!Synapse:syn26720920/wiki/615338","completed","advanced","1","","","2022-11-29","2023-02-08","2023-06-23 00:00:00","2023-10-14 05:38:56" +"55","cough-diagnostic-algorithm-for-tuberculosis","COugh Diagnostic Algorithm for Tuberculosis","","Tuberculosis (TB), a communicable disease caused by Mycobacterium tuberculosis, is a major cause of ill health and one of the leading causes of death worldwide. Until the COVID-19 pandemic, TB was the leading cause of death from a single infectious agent, ranking even above HIV/AIDS. In 2020, an estimated 9.9 million people fell ill with TB and 1.3 million died of TB worldwide. However, approximately 40% of people with TB were not diagnosed or reported to public health authorities because of challenges in accessing health facilities or failure to be tested or treated when they do. The development of low-cost, non-invasive digital screening tools may improve some of the gaps in diagnosis. As cough is a common symptom of TB, it has the potential to be used as a biomarker for diagnosis of disease. Several previous studies have demonstrated the potential for cough sounds to be used to screen for TB[1-3], though these were typically done in small samples or limited settings. Further de...","","https://www.synapse.org/#!Synapse:syn31472953/wiki/617828","completed","advanced","1","","","2022-10-16","2023-02-13","2023-06-23 00:00:00","2023-10-14 05:38:57" +"56","nih-long-covid-computational-challenge","NIH Long COVID Computational Challenge","","The overall prevalence of post-acute sequelae of SARS-CoV-2 (PASC) is currently unknown, but there is growing evidence that more than half of COVID-19 survivors experience at least one symptom of PASC/Long COVID at six months after recovery of the acute illness. Reports also reflect an underlying heterogeneity of symptoms, multi-organ involvement, and persistence of PASC/Long COVID in some patients. Research is ongoing to understand prevalence, duration, and clinical outcomes of PASC/Long COVID. Symptoms of fatigue, cognitive impairment, shortness of breath, and cardiac damage, among others, have been observed in patients who had only mild initial disease. The breadth and complexity of data created in today's health care encounters require advanced analytics to extract meaning from longitudinal data on symptoms, laboratory results, images, functional tests, genomics, mobile health/wearable devices, written notes, electronic health records (EHR), and other relevant data types. Adva...","","https://www.synapse.org/#!Synapse:syn33576900/wiki/618451","completed","intermediate","1","","","2022-08-25","2022-12-15","2023-06-23 00:00:00","2023-10-18 00:39:03" +"57","bridge2ai","Bridge2AI","What makes a good color palette?","What makes a good color palette?","","","upcoming","good_for_beginners","1","","","\N","\N","2023-06-23 00:00:00","2023-10-14 05:38:58" +"58","rare-x-open-data-science","RARE-X Open Data Science","","The Xcelerate RARE-A Rare Disease Open Science Data Challenge is bringing together researchers and data scientists in a collaborative and competitive environment to make the best use of patient-provided data to solve big unknowns in healthcare. The Challenge will launch to researchers in late May 2023, focused on rare pediatric neurodevelopmental diseases.","","https://www.synapse.org/#!Synapse:syn51198355/wiki/621435","completed","intermediate","1","","","2023-05-17","2023-08-16","2023-06-23 00:00:00","2023-10-14 05:38:59" +"59","cagi5-regulation-saturation","CAGI5: Regulation saturation","","17,500 single nucleotide variants (SNVs) in 5 human disease associated enhancers (including IRF4, IRF6, MYC, SORT1) and 9 promoters (including TERT, LDLR, F9, HBG1) were assessed in a saturation mutagenesis massively parallel reporter assay. Promoters were cloned into a plasmid upstream of a tagged reporter construct, and reporter expression was measured relative to the plasmid DNA to determine the impact of promoter variants. Enhancers were placed upstream of a minimal promoter and assayed similarly. The challenge is to predict the functional effects of these variants in the regulatory regions as measured from the reporter expression.","","https://genomeinterpretation.org/CAGI5-regulation-saturation.html","completed","intermediate","2","","","2018-01-04","2018-05-03","2023-06-23 00:00:00","2023-10-18 15:36:06" +"60","cagi5-calm1","CAGI5: CALM1","","Calmodulin is a calcium-sensing protein that modulates the activity of a large number of proteins in the cell. It is involved in many cellular processes, and is especially important for neuron and muscle cell function. Variants that affect calmodulin function have been found to be causally associated with cardiac arrhythmias. A large library of calmodulin missense variants was assessed with respect to their effects on protein function using a high-throughput yeast complementation assay. The challenge is to predict the functional effects of these calmodulin variants on competitive growth in a high-throughput yeast complementation assay.","","https://genomeinterpretation.org/CAGI5-calm1.html","completed","intermediate","2","","","2017-10-21","2017-12-20","2023-06-23 00:00:00","2023-10-18 15:35:49" +"61","cagi5-pcm1","CAGI5: PCM1","","The PCM1 (Pericentriolar Material 1) gene is a component of centriolar satellites occurring around centrosomes in vertebrate cells. Several studies have implicated PCM1 variants as a risk factor for schizophrenia. Ventricular enlargement is one of the most consistent abnormal structural brain findings in schizophrenia Therefore 38 transgenic human PCM1 missense mutations implicated in schizophrenia were assayed in a zebrafish model to determine their impact on the posterior ventricle area. The challenge is to predict whether variants implicated in schizophrenia impact zebrafish ventricular area.","","https://genomeinterpretation.org/CAGI5-pcm1.html","completed","intermediate","2","","","2017-11-09","2018-04-19","2023-06-23 00:00:00","2023-10-18 15:35:49" +"62","cagi5-frataxin","CAGI5: Frataxin","","Fraxatin is a highly-conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Humans with a frataxin deficiency have the cardio-and neurodegenerative disorder Friedreich's ataxia. A library of eight missense variants was assessed by near and far-UV circular dichroism and intrinsic fluorescence spectra to determine thermodynamic stability at different concentration of denaturant. These were used to calculate a ΔΔGH20 value, the difference in unfolding free energy (ΔGH20) between the mutant and wild-type proteins for each variant. The challenge is to predict ΔΔGH20 for each frataxin variant.","","https://genomeinterpretation.org/CAGI5-frataxin.html","completed","intermediate","2","","","2017-11-30","2018-04-18","2023-06-23 00:00:00","2023-10-18 15:35:50" +"63","cagi5-tpmt","CAGI5: TPMT and p10","","The gene p10 encodes for PTEN (Phosphatase and TEnsin Homolog), an important secondary messenger molecule promoting cell growth and survival through signaling cascades including those controlled by AKT and mTOR. Thiopurine S-methyl transferase (TPMT) is a key enzyme involved in the metabolism of thiopurine drugs and functions by catalyzing the S-methylation of aromatic and heterocyclic sulfhydryl groups. A library of thousands of PTEN and TPMT mutations was assessed to measure the stability of the variant protein using a multiplexed variant stability profiling (VSP) assay, which detects the presence of EGFP fused to the mutated PTEN and TPMT protein respectively. The stability of the variant protein dictates the abundance of the fusion protein and thus the EGFP level of the cell. The challenge is to predict the effect of each variant on TPMT and/or PTEN protein stability.","","https://genomeinterpretation.org/CAGI5-tpmt.html","completed","intermediate","2","","","2017-11-30","2017-12-01","2023-06-23 00:00:00","2023-10-14 05:39:03" +"64","cagi5-annotate-all-missense","CAGI5: Annotate all nonsynonymous variants","","dbNSFP describes 810,848,49 possible protein-altering variants in the human genome. The challenge is to predict the functional effect of every such variant. For the vast majority of these missense variants, the functional impact is not currently known, but experimental and clinical evidence are accruing rapidly. Rather than drawing upon a single discrete dataset as typical with CAGI, predictions will be assessed by comparing with experimental or clinical annotations made available after the prediction submission date, on an ongoing basis. if predictors assent, predictions will also incorporated into dbNSFP.","","https://genomeinterpretation.org/CAGI5-annotate-all-missense.html","completed","intermediate","2","","","2017-11-30","2018-05-09","2023-06-23 00:00:00","2023-10-14 05:39:04" +"65","cagi5-gaa","CAGI5: GAA","","Acid alpha-glucosidase (GAA) is a lysosomal alpha-glucosidase. Some mutations in GAA cause a rare disorder, Pompe disease, (Glycogen Storage Disease II). Rare GAA missense variants found in a human population sample have been assayed for enzymatic activity in transfected cell lysates. The assessment of this challenge will include evaluations that recognize novelty of approach. The challenge is to predict the fractional enzyme activity of each mutant protein compared to the wild-type enzyme.","","https://genomeinterpretation.org/CAGI5-gaa.html","completed","intermediate","2","","","2017-11-09","2018-04-25","2023-06-23 00:00:00","2023-10-14 05:39:04" +"66","cagi5-chek2","CAGI5: CHEK2","","Variants in the CHEK2 gene are associated with breast cancer. This challenge includes CHEK2 gene variants from approximately 1200 Latino breast cancer cases and 1200 ethnically matched controls. This challenge is to estimate the probability of each gene variant occurring in an individual from the cancer affected cohort.","","https://genomeinterpretation.org/CAGI5-chek2.html","completed","intermediate","2","","","2017-12-20","2018-04-24","2023-06-23 00:00:00","2023-10-14 05:39:07" +"67","cagi5-enigma","CAGI5: ENIGMA","","Breast cancer is the most prevalent cancer among women worldwide. The association between germline mutations in the BRCA1 and BRCA2 genes and the development of cancer has been well established. The most common high-risk mutations associated with breast cancer are those in the autosomal dominant breast cancer genes 1 and 2 (BRCA1 and BRCA2). Mutations in these genes are found in 1-3% of breast cancer cases. The challenge is to predict which variants are associated with increased risk for breast cancer.","","https://genomeinterpretation.org/CAGI5-enigma.html","completed","intermediate","2","","","2017-12-20","2018-05-01","2023-06-23 00:00:00","2023-10-14 05:39:08" +"68","cagi5-mapsy","CAGI5: MaPSy","","The Massively Parallel Splicing Assay (MaPSy) approach was used to screen 797 reported exonic disease mutations using a mini-gene system, assaying both in vivo via transfection in tissue culture, and in vitro via incubation in cell nuclear extract. The challenge is to predict the degree to which a given variant causes changes in splicing.","","https://genomeinterpretation.org/CAGI5-mapsy.html","completed","intermediate","2","","","2017-11-29","2018-05-07","2023-06-23 00:00:00","2023-10-14 05:39:08" +"69","cagi5-vex-seq","CAGI5: Vex-seq","","A barcoding approach called Variant exon sequencing (Vex-seq) was applied to assess effect of 2,059 natural single nucleotide variants and short indels on splicing of a globin mini-gene construct transfected into HepG2 cells. This is reported as ΔΨ (delta PSI, or Percent Spliced In), between the variant Ψand the reference Ψ. The challenge is to predict ΔΨ for each variant.","","https://genomeinterpretation.org/CAGI5-vex-seq.html","completed","intermediate","2","","","2017-12-14","2018-05-02","2023-06-23 00:00:00","2023-10-16 17:51:58" +"70","cagi5-sickkids5","CAGI5: SickKids clinical genomes","","This challenge involves 30 children with suspected genetic disorders who were referred for clinical genome sequencing. Predictors are given the 30 genome sequences, and are also provided with the phenotypic descriptions as shared with the diagnostic laboratory. The challenge is to predict what class of disease is associated with each genome, and which genome corresponds to which clinical description. Predictors may additionally identify the diagnostic variant(s) underlying the predictions, and identify predictive secondary variants conferring high risk of other diseases whose phenotypes are not reported in the clinical descriptions.","","https://genomeinterpretation.org/CAGI5-sickkids5.html","completed","intermediate","2","","","2017-12-22","2018-04-26","2023-06-23 00:00:00","2023-10-14 05:39:10" +"71","cagi5-intellectual-disability","CAGI5: ID Panel","","The challenge presented here is to use computational methods to predict a patient's clinical phenotype and the causal variant(s) based on analysis of their gene panel sequence data. Sequence data for 74 genes associated with intellectual disability (ID) and/or Autism spectrum disorders (ASD) from a cohort of 150 patients with a range of neurodevelopmental presentations (ID, autism, epilepsy, etc..) have been made available for this challenge. For each patient, predictors must report the causative variants and which of seven phenotypes are present.","","https://genomeinterpretation.org/CAGI5-intellectual-disability.html","completed","intermediate","2","","","2017-12-22","2018-04-30","2023-06-23 00:00:00","2023-10-18 15:28:06" +"72","cagi5-clotting-disease","CAGI5: Clotting disease exomes","","African Americans have a higher incidence of developing venous thromboembolisms (VTE), which includes deep vein thrombosis (DVT) and pulmonary embolism (PE), than people of European ancestry. Participants are provided with exome data and clinical covariates for a cohort of African Americans who have been prescribed Warfarin either because they had experienced a VTE event or had been diagnosed with atrial fibrillation (which predisposes to clotting). The challenge is to distinguish between these conditions. At present, in contrast to European ancestry, there are no genetic methods for anticipating which African Americans are most at risk of a venous thromboembolism, and the results of this challenge may contribute to the development of such tools.","","https://genomeinterpretation.org/CAGI5-clotting-disease.html","completed","intermediate","2","","","2017-11-23","2018-04-28","2023-06-23 00:00:00","2023-10-18 15:30:55" +"73","cagi6-sickkids","CAGI6: SickKids clinical genomes and transcriptomes","The SickKids Genome Clinic is providing clinical phenotypic information in t...","This challenge involves data from 79 children who were referred to The Hospital for Sick Children's (SickKids) Genome Clinic for genome sequencing because of suspected but undiagnosed genetic disorders. Research subjects are consented for sharing of their sequence data and phenotype information with researchers working to understand the molecular causes of rare disease. When a candidate disease variant believed to be related to the phenotype is identified, the variant is adjudicated and confirmed in a clinical setting. In this challenge, transcriptomic and phenotype data from a subset of the “solved” (diagnosed) and “unsolved” SickKids patients will be provided, along with corresponding genomic sequence data. The challenge is to use a transcriptome-driven approach to identify the gene(s) and molecular mechanisms underlying the phenotypic descriptions in each case. For the unsolved cases, prioritized variants from the participating teams will be examined to see if additional diagno...","","https://genomeinterpretation.org/CAGI6-sickkids.html","completed","intermediate","1","","","2021-08-04","2021-12-31","2023-06-23 00:00:00","2023-10-18 20:53:36" +"74","cagi6-cam","CAGI6: CaM","","Calmodulin (CaM) is a ubiquitous calcium (Ca2+) sensor protein interacting with more than 200 molecular partners, thereby regulating a variety of biological processes. Missense point mutations in the genes encoding CaM have been associated with ventricular tachycardia and sudden cardiac death. A library encompassing up to 17 point mutations was assessed by far-UV circular dichroism (CD) by measuring melting temperature (Tm) and percentage of unfolding (%unfold) upon thermal denaturation at pH and salt concentration that mimic the physiological conditions. The challenge is to predict: the Tm and %unfold values for isolated CaM variants under Ca2+-saturating conditions (Ca2+-CaM) and in the Ca2+-free (apo) state; whether the point mutation stabilizes or destabilizes the protein (based on Tm and %unfold).","","https://genomeinterpretation.org/CAGI6-cam.html","completed","intermediate","1","","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-18 15:32:37" +"75","cami-ii","CAMI II","","CAMI II offers several challenges-an assembly, a genome binning, a taxonomic binning and a taxonomic profiling challenge, on several multi-sample data sets from different environments, including long and short read data. This includes a marine data set and a high-strain diversity data set, with a third data set to follow later. A pathogen detection challenge on a clinical sample is also provided.","","https://www.microbiome-cosi.org/cami/cami/cami2","completed","intermediate","3","","","2019-01-14","2021-01-31","2023-06-23 00:00:00","2023-10-17 23:15:00" +"76","camda18-metasub-forensics","CAMDA18-MetaSUB Forensics","","The MetaSUB International Consortium is building a longitudinal metagenomic map of mass-transit systems and other public spaces across the globe. The consortium maintains a strategic partnership with CAMDA and this year provides data from global City Sampling Days for the first-ever multi-city forensic analyses.","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#metasub_forensics_challenge","completed","intermediate","7","","","\N","\N","2023-06-23 00:00:00","2023-10-17 23:15:08" +"77","camda18-cmap-drug-safety","CAMDA18-CMap Drug Safety","","Attrition in drug discovery and development due to safety / toxicity issues remains a significant concern, and there are strong efforts to identify and mitigate risk as early as possible. Drug-induced liver injury (DILI) is one of the primary problems in drug development and regulatory clearance due to the poor performance of existing preclinical models. There is a pressing need to evaluate alternative methods for predicting DILI, with great hopes being placed in modern approaches from statistics and machine learning applied to genome scale profiling data. A critical question thus is if we can better integrate, understand, and exploit information from cell-based screens like the Broad Institute Connectivity Map (CMap, Science 313, Nature Reviews Cancer 7). This CAMDA challenge focuses on understanding or predicting drug induced liver injury in humans from cell-based screens, specifically the CMap gene expression responses of two different cancer cell lines (MCF7 and PC3) to 276 d...","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#cmap_drug_safety_challenge","completed","intermediate","7","","","\N","\N","2023-06-23 00:00:00","2023-10-14 05:39:18" +"78","camda18-cancer-data-integration","CAMDA18-Cancer Data Integration","","Examine the power of data integration in a real-world clinical settings. Many approaches work well on some data-sets yet not on others. We here challenge you to demonstrate a unified single approach to data-integration that matches or outperforms the current state of the art on two different diseases, breast cancer and neuroblastoma. Breast cancer affects about 3 million women every year (McGuire et al, Cancers 7), and this number is growing fast, especially in developed countries. Can you improve on the large Metabric study (Curtis et al., Nature 486, and Dream Challenge, Margolin et al, Sci Transl Med 5)? The cohort is biologically heterogeneous with all five distinct PAM50 breast cancer subtypes represented. Matched profiles for microarray and copy number data as well as clinical information (survival times, multiple prognostic markers, therapy data) are available for about 2,000 patients. Neuroblastoma is the most common extracranial solid tumor in children. The base study com...","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#cancer_data_integration_challenge","completed","intermediate","7","","","\N","\N","2023-06-23 00:00:00","2023-10-14 05:39:18" +"79","cafa-4","CAFA 4","","The goal of the Critical Assessment of Functional Annotation(CAFA) challenge is to evaluate automated protein function prediction algorithms in the task of predicting Gene Ontology and Human Phenotype Ontology terms for a given set of protein sequences. For the GO-based predictions, the evaluation will be carried out for the Molecular Function Ontology, Biological Process Ontology and Cellular Component Ontology. Participants develop protein function prediction algorithms using training protein sequence data and submit their predictions on target protein sequence data.","","https://www.biofunctionprediction.org/cafa/","completed","intermediate","1","","","2019-10-21","2020-02-12","2023-06-23 00:00:00","2023-10-14 05:39:20" +"80","casp13","CASP13","","CASP (Critical Assessment of Structure Prediction) is a community wide experiment to determine and advance the state of the art in modeling protein structure from amino acid sequence. Every two years, participants are invited to submit models for a set of proteins for which the experimental structures are not yet public. Independent assessors then compare the models with experiment. Assessments and results are published in a special issue of the journal PROTEINS. In the most recent CASP round, CASP12, nearly 100 groups from around the world submitted more than 50,000 models on 82 modeling targets","","https://predictioncenter.org/casp13/index.cgi","completed","intermediate","14","","","2018-04-18","2018-08-20","2023-06-23 00:00:00","2023-10-17 22:52:29" +"81","casp14","CASP14","","CASP (Critical Assessment of Structure Prediction) is a community wide experiment to determine and advance the state of the art in modeling protein structure from amino acid sequence. Every two years, participants are invited to submit models for a set of proteins for which the experimental structures are not yet public. Independent assessors then compare the models with experiment. Assessments and results are published in a special issue of the journal PROTEINS. In the most recent CASP round, CASP14, nearly 100 groups from around the world submitted more than 67,000 models on 90 modeling targets.","","https://predictioncenter.org/casp14/index.cgi","completed","intermediate","14","","","2020-05-04","2020-09-07","2023-06-23 00:00:00","2023-10-17 22:47:26" +"82","cfsan-pathogen-detection","CFSAN Pathogen Detection","","In the U.S. alone, one in six individuals, an estimated 48 million people, fall prey to foodborne illness, resulting in 128,000 hospitalizations and 3,000 deaths per year. Economic burdens are estimated cumulatively at $152 billion dollars annually, including $39 billion due to contamination of fresh and processed produce. One longstanding problem is the ability to rapidly identify the food-source associated with the outbreak being investigated. The faster an outbreak is identified and the increased certainty that a given source (e.g., papayas from Mexico) and patients are linked, the faster the outbreak can be stopped, limiting morbidity and mortality. In the last few years, the application of next-generation sequencing (NGS) technology for whole genome sequencing (WGS) of foodborne pathogens has revolutionized food pathogen outbreak surveillance. WGS of foodborne pathogens enables high-resolution identification of pathogens isolated from food or environmental samples. These pat...","","https://precision.fda.gov/challenges/2","completed","intermediate","6","","","2018-02-15","2018-04-26","2023-06-23 00:00:00","2023-10-14 05:39:23" +"83","cdrh-biothreat","CDRH Biothreat","","Many infectious diseases have similar signs and symptoms, making it challenging for healthcare providers to identify the disease-causing agent. Clinical samples are often tested by multiple test methods to help reveal the microbe that is causing the infectious disease. The results of these test methods can help healthcare professionals determine the best treatment for patients. Today, High-Throughput Sequencing (HTS) or Next Generation Sequencing (NGS) technology has the capability, as a single test, to accomplish what might have required several different tests in the past. NGS technology may allow the diagnosis of infections without prior knowledge of disease(s) cause. NGS technology can potentially reveal the presence of all microorganisms in a patient sample. Using infectious disease NGS (ID-NGS) technology, each microbial pathogen may be identified by its unique genomic fingerprint. The vision of ID-NGS technology is to further improve patient care by delivering diagnostics ...","","https://precision.fda.gov/challenges/3","completed","intermediate","6","","","2018-08-03","2018-10-18","2023-06-23 00:00:00","2023-10-14 05:39:24" +"84","multi-omics-enabled-sample-mislabeling-correction","Multi-omics Enabled Sample Mislabeling Correction","","In biomedical research, sample mislabeling (accidental swapping of patient samples) or data mislabeling (accidental swapping of patient omics data) has been a long-standing problem that contributes to irreproducible results and invalid conclusions. These problems are particularly prevalent in large scale multi-omics studies, in which multiple different omics experiments are carried out at different time periods and/or in different labs. Human errors could arise during sample transferring, sample tracking, large-scale data generation, and data sharing/management. Thus, there is a pressing need to identify and correct sample and data mislabeling events to ensure the right data for the right patient. Simultaneous use of multiple types of omics platforms to characterize a large set of biological samples, as utilized in The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects, has been demonstrated as a powerful approach to understanding the ...","","https://precision.fda.gov/challenges/4","completed","intermediate","6","https://doi.org/10.1038/s41591-018-0180-x","","2018-09-24","2018-12-19","2023-06-23 00:00:00","2023-10-14 05:39:25" +"85","biocompute-object-app-a-thon","BioCompute Object App-a-thon","","Like scientific laboratory experiments, bioinformatics analysis results and interpretation are faced with reproducibility challenges due to the variability in multiple computational parameters, including input format, prerequisites, platform dependencies, and more. Even small changes in these computational parameters may have a large impact on the results and carry big implications for their scientific validity. Because there are currently no standardized schemas for reporting computational scientific workflows and parameters together with their results, the ways in which these workflows are communicated is highly variable, incomplete, and difficult or impossible to reproduce. The US Food and Drug Administration (FDA) High Performance Virtual Environment (HIVE) group and George Washington University (GW) have partnered to establish a framework for community-based standards development and harmonization of high-throughput sequencing (HTS) computations and data formats based arou...","","https://precision.fda.gov/challenges/7/","completed","intermediate","6","https://doi.org/10.1101/2020.11.02.365528","","2019-05-14","2019-10-18","2023-06-23 00:00:00","2023-10-14 05:39:25" +"86","brain-cancer-predictive-modeling-and-biomarker-discovery","Brain Cancer Predictive Modeling and Biomarker Discovery","","An estimated 86,970 new cases of primary brain and other central nervous system tumors are expected to be diagnosed in the US in 2019. Brain tumors comprise a particularly deadly subset of all cancers due to limited treatment options and the high cost of care. Only a few prognostic and predictive markers have been successfully implemented in the clinic so far for gliomas, the most common malignant brain tumor type. These markers include MGMT promoter methylation in high-grade astrocytomas, co-deletion of 1p/19q in oligodendrogliomas, and mutations in IDH1 or IDH2 genes (Staedtke et al. 2016). There remains significant potential for identifying new clinical biomarkers in gliomas. Clinical investigators at Georgetown University are seeking to advance precision medicine techniques for the prognosis and treatment of brain tumors through the identification of novel multi-omics biomarkers. In support of this goal, precisionFDA and the Georgetown Lombardi Comprehensive Cancer Center and ...","","https://precision.fda.gov/challenges/8/","completed","advanced","6","","","2019-11-01","2020-02-14","2023-06-23 00:00:00","2023-10-14 05:39:25" +"87","gaining-new-insights-by-detecting-adverse-event-anomalies","Gaining New Insights by Detecting Adverse Event Anomalies","","The Food and Drug Administration (FDA) calls on the public to develop computational algorithms for automatic detection of adverse event anomalies using publicly available data.","","https://precision.fda.gov/challenges/9/","completed","intermediate","6","","","2020-01-17","2020-05-18","2023-06-23 00:00:00","2023-10-14 05:39:27" +"88","calling-variants-in-difficult-to-map-regions","Calling Variants in Difficult-to-Map Regions","","This challenge calls on the public to assess variant calling pipeline performance on a common frame of reference, with a focus on benchmarking in difficult-to-map regions, segmental duplications, and the Major Histocompatibility Complex (MHC).","","https://precision.fda.gov/challenges/10/","completed","intermediate","6","https://doi.org/10.1016/j.xgen.2022.100129","","2020-05-01","2020-06-15","2023-06-23 00:00:00","2023-10-14 05:39:28" +"89","vha-innovation-ecosystem-and-covid-19-risk-factor-modeling","VHA Innovation Ecosystem and COVID-19 Risk Factor Modeling","","The novel coronavirus disease 2019 (COVID-19) is a respiratory disease caused by a new type of coronavirus, known as “severe acute respiratory syndrome coronavirus 2,” or SARS-CoV-2. On March 11, 2020, the World Health Organization (WHO) declared the outbreak a global pandemic. As of Monday, June 1, the Johns Hopkins University COVID-19 dashboard reports over 6.21 million total confirmed cases worldwide, including over 1.79 million cases in the United States. Although most people have mild to moderate symptoms, the disease can cause severe medical complications leading to death in some people. The Centers for Disease Control and Prevention (CDC) have identified several groups at elevated risk for severe illness, including people 65 years and older, individuals living in nursing homes or long term care facilities, and those with serious underlying medical conditions, such as severe obesity, diabetes, chronic lung disease or moderate to severe asthma, chronic kidney or liver disease...","","https://precision.fda.gov/challenges/11/","completed","intermediate","6","","","2020-06-02","2020-07-03","2023-06-23 00:00:00","2023-10-14 05:39:28" +"90","covid-19-precision-immunology-app-a-thon","COVID-19 Precision Immunology App-a-thon","","The novel coronavirus disease 2019 (COVID-19), a respiratory disease caused by a new type of coronavirus, known as “severe acute respiratory syndrome coronavirus 2” or SARS-CoV-2, was declared a global pandemic by the World Health Organization on March 11, 2020. To date, the Johns Hopkins University COVID-19 dashboard reports over 62 million confirmed cases worldwide, with a wide range of disease severity from asymptomatic to deaths (over 1.46 million). To effectively combat the widespread transmission of COVID-19 infection and save lives especially of those vulnerable individuals, it is imperative to better understand its pathophysiology to enable effective diagnosis, prognosis and treatment strategies using rapidly shared data.","","https://precision.fda.gov/challenges/12/","completed","intermediate","6","","","2020-11-30","2021-01-29","2023-06-23 00:00:00","2023-10-14 05:39:29" +"91","smarter-food-safety-low-cost-tech-enabled-traceability","Smarter Food Safety Low Cost Tech-Enabled Traceability","","The motivation is tapping into new technologies and integrating data streams will help to advance the widespread, consistent implementation of traceability systems across the food industry. However, the affordability of such technologies, particularly for smaller companies, can be a barrier to implementing tech-enabled traceability systems. FDA's New Era of Smarter Food Safety initiative strives to work with stakeholders to explore low-cost or no-cost options so that our approaches are inclusive of and viable for human and animal food operations of all sizes. Democratizing the benefits of digitizing data will allow the entire food system to move more rapidly towards digital traceability systems. The primary goal is to encourage stakeholders, including technology providers, public health advocates, entrepreneurs, and innovators from all disciplines and around the world, to develop traceability hardware, software, or data analytics platforms that are low-cost or no-cost to the en...","","https://precision.fda.gov/challenges/13","completed","intermediate","6","","","2021-06-01","2021-07-30","2023-06-23 00:00:00","2023-10-17 23:05:49" +"92","tumor-mutational-burden-tmb-challenge-phase-1","Tumor Mutational Burden (TMB) Challenge Phase 1","","Tumor mutational burden (TMB) is generally defined as the number of mutations detected in a patient's tumor sample per megabase of DNA sequenced. However different algorithms use different methods for calculating TMB. Mutations in genes in tumor cells may lead to the creation of neoantigens, which have the potential to activate an immune system response against the tumor, and the likelihood of an immune system response may increase with the number of mutations. Thus, TMB is a biomarker for some immunotherapy drugs, called immune checkpoint inhibitors, such as those that target the PD-1 and PD-L1 pathways (Chan et al., 2019). An outstanding problem is the lack of standardization for TMB calculation and reporting between different assays. To address this problem, the Friends of Cancer Research convened a working group of industry and regulatory stakeholders to develop guidance and tools for TMB harmonization. Results from the first phase of this effort were presented at AACR 2020 (...","","https://precision.fda.gov/challenges/17","completed","advanced","6","","","2021-06-21","2021-09-13","2023-06-23 00:00:00","2023-10-14 05:39:32" +"93","kits21","Kidney and Kidney Tumor Segmentation","","The 2021 Kidney and Kidney Tumor Segmentation challenge (abbreviated KiTS21) is a competition in which teams compete to develop the best system for automatic semantic segmentation of renal tumors and surrounding anatomy. Kidney cancer is one of the most common malignancies in adults around the world, and its incidence is thought to be increasing [1]. Fortunately, most kidney tumors are discovered early while they're still localized and operable. However, there are important questions concerning management of localized kidney tumors that remain unanswered [2], and metastatic renal cancer remains almost uniformly fatal [3]. Kidney tumors are notorious for their conspicuous appearance in computed tomography (CT) imaging, and this has enabled important work by radiologists and surgeons to study the relationship between tumor size, shape, and appearance and its prospects for treatment [4,5,6]. It's laborious work, however, and it relies on assessments that are often subjective and impr...","","https://kits21.kits-challenge.org/","completed","advanced","5","","","2021-08-23","2021-09-17","2023-06-23 00:00:00","2023-10-16 18:30:07" +"94","realnoisemri","Real Noise MRI","","In recent years, there is a growing focus on the application of fast magnetic resonance imaging (MRI) based on prior knowledge. In the 1980s and 2000s the community used either purely mathematical models such as the partial Fourier transform or solutions derived through advanced engineering such as parallel imaging to speed up MRI acquisition. Since the mid-2000's, compressed sensing and artificial intelligence have been employed to speed up MRI acquisition. These newer methods rely on under sampling the data acquired in Fourier (aka k-) space and then interpolating or augmenting k-space data based on training data content. One of the underlying problems for the development of fast imaging techniques, that just as in e.g. [1], it is common to use a fully sampled image as ground truth and then under sample it in k-space in order to simulate under sampled data. The problem with this approach is that in cases were the under sampled data is corrupted, through e.g. motion, this under s...","","https://realnoisemri.grand-challenge.org/","completed","intermediate","5","","","2021-09-21","2021-12-06","2023-06-23 00:00:00","2023-10-14 05:39:33" +"95","deep-generative-model-challenge-for-da-in-surgery","Deep Generative Model Challenge for DA in Surgery","","Mitral regurgitation (MR) is the second most frequent indication for valve surgery in Europe and may occur for organic or functional causes [1]. Mitral valve repair, although considerably more difficult, is prefered over mitral valve replacement, since the native tissue of the valve is preserved. It is a complex on-pump heart surgery, often conducted only by a handful of surgeons in high-volume centers. Minimally invasive procedures, which are performed with endoscopic video recordings, became more and more popular in recent years. However, data availability and data privacy concerns are still an issue for the development of automatic scene analysis algorithms. The AdaptOR challenge aims to address these issues by formulating a domain adaptation problem from simulation to surgery. We provide a smaller number of datasets from real surgeries, and a larger number of annotated recordings of training and planning sessions from a physical mitral valve simulator. The goal is to reduce th...","","https://adaptor2021.github.io/","completed","intermediate","1","","","2021-04-01","2021-07-16","2023-06-23 00:00:00","2023-10-14 05:39:34" +"96","aimdatathon","AIM Datathon 2020","Join the AI in Medicine ( AIM ) Datathon 2020","Join the AI in Medicine ( AIM ) Datathon 2020","","https://www.kaggle.com/competitions/aimdatathon","completed","intermediate","8","","","2020-11-09","2020-11-22","2023-06-23 00:00:00","2023-10-16 17:55:00" +"97","opc-recurrence","Oropharynx Cancer (OPC) Radiomics Challenge :: Local Recurrence Prediction","Determine from CT data whether a tumor will be controlled by definitive radi...","Determine from CT data whether a tumor will be controlled by definitive radiation therapy.","","https://www.kaggle.com/competitions/opc-recurrence","completed","intermediate","8","","","2016-07-26","2016-09-12","2023-06-23 00:00:00","2023-10-16 18:10:11" +"98","oropharynx-radiomics-hpv","Oropharynx Cancer (OPC) Radiomics Challenge :: Human Papilloma Virus (HPV) Status Prediction","Predict from CT data the HPV phenotype of oropharynx tumors; compare to grou...","Predict from CT data the HPV phenotype of oropharynx tumors; compare to ground-truth results previously obtained by p16 or HPV testing.","","https://www.kaggle.com/competitions/oropharynx-radiomics-hpv","completed","intermediate","8","","","2016-07-26","2016-09-12","2023-06-23 00:00:00","2023-10-16 18:10:15" +"99","data-science-bowl-2017","Data Science Bowl 2017","Can you improve lung cancer detection?","Can you improve lung cancer detection?","","https://www.kaggle.com/competitions/data-science-bowl-2017","completed","intermediate","8","","","2017-01-12","2017-04-12","2023-06-23 00:00:00","2023-10-14 05:39:38" +"100","predict-impact-of-air-quality-on-death-rates","Predict impact of air quality on mortality rates","Predict CVD and cancer caused mortality rates in England using air quality d...","Predict CVD and cancer caused mortality rates in England using air quality data available from Copernicus Atmosphere Monitoring Service","","https://www.kaggle.com/competitions/predict-impact-of-air-quality-on-death-rates","completed","intermediate","8","","","2017-02-13","2017-05-05","2023-06-23 00:00:00","2023-10-14 05:39:38" +"101","intel-mobileodt-cervical-cancer-screening","Intel & MobileODT Cervical Cancer Screening","Which cancer treatment will be most effective?","Which cancer treatment will be most effective?","","https://www.kaggle.com/competitions/intel-mobileodt-cervical-cancer-screening","completed","intermediate","8","","","2017-03-15","2017-06-21","2023-06-23 00:00:00","2023-10-14 05:39:39" +"102","msk-redefining-cancer-treatment","Personalized Medicine-Redefining Cancer Treatment","Predict the effect of Genetic Variants to enable Personalized Medicine","Predict the effect of Genetic Variants to enable Personalized Medicine","","https://www.kaggle.com/competitions/msk-redefining-cancer-treatment","completed","intermediate","8","","","2017-06-26","2017-10-02","2023-06-23 00:00:00","2023-10-14 05:39:40" +"103","mubravo","Predicting Cancer Diagnosis","Bravo's machine learning competition!","Bravo's machine learning competition!","","https://www.kaggle.com/competitions/mubravo","completed","intermediate","8","","","2018-07-31","2018-08-13","2023-06-23 00:00:00","2023-10-14 05:39:41" +"104","histopathologic-cancer-detection","Histopathologic Cancer Detection","Identify metastatic tissue in histopathologic scans of lymph node sections","Identify metastatic tissue in histopathologic scans of lymph node sections","","https://www.kaggle.com/competitions/histopathologic-cancer-detection","completed","intermediate","8","","","2018-11-16","2019-03-30","2023-06-23 00:00:00","2023-10-14 05:39:41" +"105","tjml1920-decision-trees","TJML 2019-20 Breast Cancer Detection Competition","Use a decision tree to identify malignant breast cancer tumors","Use a decision tree to identify malignant breast cancer tumors","","https://www.kaggle.com/competitions/tjml1920-decision-trees","completed","intermediate","8","","","2019-09-22","2019-10-16","2023-06-23 00:00:00","2023-10-14 05:39:42" +"106","prostate-cancer-grade-assessment","Prostate cANcer graDe Assessment (PANDA) Challenge","Prostate cancer diagnosis using the Gleason grading system","Prostate cancer diagnosis using the Gleason grading system","","https://www.kaggle.com/competitions/prostate-cancer-grade-assessment","completed","intermediate","8","","","2020-04-21","2020-07-22","2023-06-23 00:00:00","2023-10-14 05:39:43" +"107","breast-cancer","Breast Cancer","Use cell nuclei categories to predict breast cancer tumor.","Use cell nuclei categories to predict breast cancer tumor.","","https://www.kaggle.com/competitions/breast-cancer","completed","intermediate","8","","","2020-08-12","2020-08-13","2023-06-23 00:00:00","2023-10-14 05:39:43" +"108","breast-cancer-detection","Breast Cancer Detection","breast cancer detection","breast cancer detection","","https://www.kaggle.com/competitions/breast-cancer-detection","completed","intermediate","8","","","2020-09-25","2020-12-31","2023-06-23 00:00:00","2023-10-14 05:39:44" +"109","hrpred","Prediction of High Risk Patients","Classification of high and low risk cancer patients","Classification of high and low risk cancer patients","","https://www.kaggle.com/competitions/hrpred","completed","intermediate","8","","","2020-11-25","2020-12-05","2023-06-23 00:00:00","2023-10-14 05:39:44" +"110","ml4moleng-cancer","MIT ML4MolEng-Predicting Cancer Progression","MIT 3.100, 10.402, 20.301 In class ML competition (Spring 2021)","MIT 3.100, 10.402, 20.301 In class ML competition (Spring 2021)","","https://www.kaggle.com/competitions/ml4moleng-cancer","completed","intermediate","8","","","2021-05-06","2021-05-21","2023-06-23 00:00:00","2023-10-14 05:39:45" +"111","uw-madison-gi-tract-image-segmentation","UW-Madison GI Tract Image Segmentation","Track healthy organs in medical scans to improve cancer treatment","Track healthy organs in medical scans to improve cancer treatment","","https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation","completed","intermediate","8","","","2022-04-14","2022-07-14","2023-06-23 00:00:00","2023-10-14 05:39:46" +"112","rsna-miccai-brain-tumor-radiogenomic-classification","RSNA-MICCAI Brain Tumor Radiogenomic Classification","Predict the status of a genetic biomarker important for brain cancer treatment","The Brain Tumor Segmentation (BraTS) challenge celebrates its 10th anniversary, and this year is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society. The RSNA-ASNR-MICCAI BraTS 2021 challenge utilizes multi-institutional pre-operative baseline multi-parametric magnetic resonance imaging (mpMRI) scans, and focuses on the evaluation of state-of-the-art methods for (Task 1) the segmentation of intrinsically heterogeneous brain glioblastoma sub-regions in mpMRI scans. Furthemore, this BraTS 2021 challenge also focuses on the evaluation of (Task 2) classification methods to predict the MGMT promoter methylation status. Participants are free to choose whether they want to focus only on one or both tasks.","","https://www.kaggle.com/competitions/rsna-miccai-brain-tumor-radiogenomic-classification","completed","advanced","8","","","2021-07-13","2021-10-15","2023-06-23 00:00:00","2023-10-14 05:39:46" +"113","breastcancer","Breast Cancer - Beginners ML","Beginners hands-on experience with ML basics","Beginners hands-on experience with ML basics","","https://www.kaggle.com/competitions/breastcancer","completed","intermediate","8","","","2021-12-21","2022-02-12","2023-06-23 00:00:00","2023-10-18 21:18:15" +"114","ml-olympiad-health-and-education","ML Olympiad -Let's Fight lung cancer","Use your ML expertise to help us step another step toward defeating cancer [...","Use your ML expertise to help us step another step toward defeating cancer [ Starts on the 14th February ]","","https://www.kaggle.com/competitions/ml-olympiad-health-and-education","completed","intermediate","8","","","2022-01-31","2022-03-19","2023-06-23 00:00:00","2023-10-14 05:39:48" +"115","cs98-22-dl-task1","CS98X-22-DL-Task1","This competition is related to Task 1 in coursework-breast cancer classification","This competition is related to Task 1 in coursework-breast cancer classification","","https://www.kaggle.com/competitions/CS98-22-DL-Task1","completed","intermediate","8","","","2022-02-28","2022-04-11","2023-06-23 00:00:00","2023-10-14 05:39:48" +"116","parasitedetection-iiitb2019","Parasite detection","detect if cell image has parasite or is uninfected","detect if cell image has parasite or is uninfected","","https://www.kaggle.com/competitions/parasitedetection-iiitb2019","completed","intermediate","8","","","2019-10-13","2019-11-25","2023-06-23 00:00:00","2023-10-14 05:39:49" +"117","hpa-single-cell-image-classification","Human Protein Atlas -Single Cell Classification","Find individual human cell differences in microscope images","Find individual human cell differences in microscope images","","https://www.kaggle.com/competitions/hpa-single-cell-image-classification","completed","intermediate","8","","","2021-01-26","2021-05-11","2023-06-23 00:00:00","2023-10-14 05:39:50" +"118","stem-cell-predcition","Stem Cell Predcition","Classify stem and non-stem cells using RNA-seq data","Classify stem and non-stem cells using RNA-seq data","","https://www.kaggle.com/competitions/stem-cell-predcition","completed","intermediate","8","","","2021-04-01","2021-07-01","2023-06-23 00:00:00","2023-10-14 05:39:50" +"119","sartorius-cell-instance-segmentation","Sartorius - Cell Instance Segmentation","Detect single neuronal cells in microscopy images","In this competition, you’ll detect and delineate distinct objects of interest in biological images depicting neuronal cell types commonly used in the study of neurological disorders. More specifically, you'll use phase contrast microscopy images to train and test your model for instance segmentation of neuronal cells. Successful models will do this with a high level of accuracy. If successful, you'll help further research in neurobiology thanks to the collection of robust quantitative data. Researchers may be able to use this to more easily measure the effects of disease and treatment conditions on neuronal cells. As a result, new drugs could be discovered to treat the millions of people with these leading causes of death and disability.","","https://www.kaggle.com/competitions/sartorius-cell-instance-segmentation","completed","intermediate","8","","","2021-10-14","2021-12-30","2023-06-23 00:00:00","2023-10-16 18:05:17" +"120","pvelad","Photovoltaic cell anomaly detection","Hosted by Hebei University of Technology (AIHebut research group) and Beihan...","Hosted by Hebei University of Technology (AIHebut research group) and Beihang University (NAVE research group)","","https://www.kaggle.com/competitions/pvelad","completed","intermediate","8","","","2022-03-15","2022-07-30","2023-06-23 00:00:00","2023-10-14 05:39:51" +"121","blood-mnist","Blood-MNIST","Classifying blood cell types using Weights and Biases","Classifying blood cell types using Weights and Biases","","https://www.kaggle.com/competitions/blood-mnist","completed","intermediate","8","","","2022-03-19","2022-03-19","2023-06-23 00:00:00","2023-10-14 05:39:52" +"122","insilicomolhack","MolHack","Apply deep learning to speedup drug validation","Apply deep learning to speedup drug validation","","https://www.kaggle.com/competitions/insilicomolhack","completed","intermediate","8","","","2018-04-02","2018-05-25","2023-06-23 00:00:00","2023-10-14 05:39:53" +"123","codata2019challenge","Cell Response Classification","From recorded timeseries of many cells in a well, predict which drug treatme...","From recorded timeseries of many cells in a well, predict which drug treatment has been applied","","https://www.kaggle.com/competitions/codata2019challenge","completed","intermediate","8","","","2019-04-08","2019-05-07","2023-06-23 00:00:00","2023-10-14 05:39:53" +"124","drug-solubility-challenge","Drug solubility challenge","Solubility is vital to achieve desired concentration of drug for anticipated...","Solubility is vital to achieve desired concentration of drug for anticipated pharmacological response.","","https://www.kaggle.com/competitions/drug-solubility-challenge","completed","intermediate","8","","","2019-05-18","2019-10-18","2023-06-23 00:00:00","2023-10-14 05:39:54" +"125","kinase-inhibition-challenge","Kinase inhibition challenge","Protein kinases have become a major class of drug targets, accumulating a hu...","Protein kinases have become a major class of drug targets, accumulating a huge amount of data","","https://www.kaggle.com/competitions/kinase-inhibition-challenge","completed","intermediate","8","","","2019-05-20","2019-12-28","2023-06-23 00:00:00","2023-10-14 05:39:54" +"126","ai-drug-discovery","AI Drug Discovery Workshop and Coding Challenge","Developing Fundamental AI Programming Skills for Drug Discovery","Developing Fundamental AI Programming Skills for Drug Discovery","","https://www.kaggle.com/competitions/ai-drug-discovery","completed","intermediate","8","","","2021-11-12","2021-12-31","2023-06-23 00:00:00","2023-10-14 05:39:56" +"127","protein-compound-affinity","Structure-free protein-ligand affinity prediction - Task 1 Fitting","Developing new AI models for drug discovery, main portal (Task-1 fitting)","Developing new AI models for drug discovery, main portal (Task-1 fitting)","","https://www.kaggle.com/competitions/protein-compound-affinity","completed","intermediate","8","","","2021-12-06","2021-12-31","2023-06-23 00:00:00","2023-10-16 18:13:18" +"128","cisc873-dm-f21-a5","CISC873-DM-F21-A5","Anti-Cancer Drug Activity Prediction","Anti-Cancer Drug Activity Prediction","","https://www.kaggle.com/competitions/cisc873-dm-f21-a5","completed","intermediate","8","","","2021-11-26","2021-12-10","2023-06-23 00:00:00","2023-10-14 05:39:56" +"129","pro-lig-aff-task2-mse","Structure-free protein-ligand affinity prediction - Task 2 Fitting","Developing new AI models for drug discovery (Task-2 fitting)","Developing new AI models for drug discovery (Task-2 fitting)","","https://www.kaggle.com/competitions/pro-lig-aff-task2-mse","completed","intermediate","8","","","2021-12-08","2021-12-31","2023-06-23 00:00:00","2023-10-16 18:13:22" +"130","pro-lig-aff-task1-pearsonr","Structure-free protein-ligand affinity prediction - Task 1 Ranking","Developing new AI models for drug discovery (Task-1 ranking)","Developing new AI models for drug discovery (Task-1 ranking)","","https://www.kaggle.com/competitions/pro-lig-aff-task1-pearsonr","completed","intermediate","8","","","2021-12-08","2021-12-31","2023-06-23 00:00:00","2023-10-16 18:13:26" +"131","pro-lig-aff-task2-pearsonr","Structure-free protein-ligand affinity prediction - Task 2 Ranking","Developing new AI models for drug discovery (Task-2 ranking)","Developing new AI models for drug discovery (Task-2 ranking)","","https://www.kaggle.com/competitions/pro-lig-aff-task2-pearsonr","completed","intermediate","8","","","2021-12-08","2021-12-31","2023-06-23 00:00:00","2023-10-16 18:13:28" +"132","pro-lig-aff-task3-spearmanr","Structure-free protein-ligand affinity prediction - Task 3 Ranking","Developing new AI models for drug discovery (Task-3 ranking)","Developing new AI models for drug discovery (Task-3 ranking)","","https://www.kaggle.com/competitions/pro-lig-aff-task3-spearmanr","completed","intermediate","8","","","2021-12-08","2021-12-31","2023-06-23 00:00:00","2023-10-16 18:13:32" +"133","hhp","Heritage Health Prize","Identify patients who will be admitted to a hospital within the next year us...","Identify patients who will be admitted to a hospital within the next year using historical claims data. (Enter by 06-59-59 UTC Oct 4 2012)","","https://www.kaggle.com/competitions/hhp","completed","intermediate","8","","","2011-04-04","2013-04-04","2023-06-23 00:00:00","2023-10-14 05:40:00" +"134","pf2012","Practice Fusion Analyze This! 2012 - Prediction Challenge","Start digging into electronic health records and submit your ideas for the m...","Start digging into electronic health records and submit your ideas for the most promising, impactful or interesting predictive modeling competitions","","https://www.kaggle.com/competitions/pf2012","completed","intermediate","8","","","2012-06-07","2012-06-30","2023-06-23 00:00:00","2023-10-16 18:14:24" +"135","pf2012-at","Practice Fusion Analyze This! 2012 - Open Challenge","Start digging into electronic health records and submit your creative, insig...","Start digging into electronic health records and submit your creative, insightful, and visually striking analyses.","","https://www.kaggle.com/competitions/pf2012-at","completed","intermediate","8","","","2012-06-07","2012-09-10","2023-06-23 00:00:00","2023-10-16 18:14:26" +"136","seizure-detection","UPenn and Mayo Clinic's Seizure Detection Challenge","Detect seizures in intracranial EEG recordings","Detect seizures in intracranial EEG recordings","","https://www.kaggle.com/competitions/seizure-detection","completed","intermediate","8","","","2014-05-19","2014-08-19","2023-06-23 00:00:00","2023-10-14 05:40:02" +"137","seizure-prediction","American Epilepsy Society Seizure Prediction Challenge","Predict seizures in intracranial EEG recordings","Predict seizures in intracranial EEG recordings","","https://www.kaggle.com/competitions/seizure-prediction","completed","intermediate","8","","","2014-08-25","2014-11-17","2023-06-23 00:00:00","2023-10-14 05:40:03" +"138","deephealth-1","Deep Health - alcohol","Find Correlations and patterns with Medical data","Find Correlations and patterns with Medical data","","https://www.kaggle.com/competitions/deephealth-1","completed","intermediate","8","","","2017-02-13","2017-02-19","2023-06-23 00:00:00","2023-10-16 18:14:48" +"139","deep-health-3","Deep Health - Diabetes 2","This competition is for those attending the Deep Health Hackathon. Predic...","This competition is for those attending the Deep Health Hackathon. Predict the next occurrence of diabetes","","https://www.kaggle.com/competitions/deep-health-3","completed","intermediate","8","","","2017-02-15","2017-02-19","2023-06-23 00:00:00","2023-10-16 18:14:50" +"140","d012554-2021","D012554 - 2021","Classify the health of a fetus using CTG data","Classify the health of a fetus using CTG data","","https://www.kaggle.com/competitions/d012554-2021","completed","intermediate","8","","","2021-04-11","2021-05-09","2023-06-23 00:00:00","2023-10-16 18:15:04" +"141","idao-2022-bootcamp-insomnia","IDAO 2022. ML Bootcamp - Insomnia","Predict sleep disorder on given human health data","Predict sleep disorder on given human health data","","https://www.kaggle.com/competitions/idao-2022-bootcamp-insomnia","completed","intermediate","8","","","2021-12-04","2021-12-05","2023-06-23 00:00:00","2023-10-16 18:15:12" +"142","tweet-mental-health-classification","Tweet Mental Health Classification","Build Models to classify tweets to determine mental health","Build Models to classify tweets to determine mental health","","https://www.kaggle.com/competitions/tweet-mental-health-classification","completed","intermediate","8","","","2021-12-27","2022-01-31","2023-06-23 00:00:00","2023-10-14 05:40:07" +"143","ml-olympiad-good-health-and-well-being","ML Olympiad - GOOD HEALTH AND WELL BEING","Use your ML expertise to classify if a patient has heart disease or not","Use your ML expertise to classify if a patient has heart disease or not","","https://www.kaggle.com/competitions/ml-olympiad-good-health-and-well-being","completed","intermediate","8","","","2022-02-03","2022-03-01","2023-06-23 00:00:00","2023-10-16 18:15:20" +"144","rsna-breast-cancer-detection","RSNA Screening Mammography Breast Cancer Detection","Find breast cancers in screening mammograms","Find breast cancers in screening mammograms","","https://www.kaggle.com/competitions/rsna-breast-cancer-detection","completed","intermediate","8","","","2022-11-28","2023-02-27","2023-06-23 00:00:00","2023-10-14 05:40:12" +"145","biocreative-vii-text-mining-drug-and-chemical-protein-interactions-drugprot","BioCreative VII: Text mining drug and chemical-protein interactions (DrugProt)","","With the rapid accumulation of biomedical literature, it is getting increasingly challenging to exploit efficiently drug-related information described in the scientific literature. One of the most relevant aspects of drugs and chemical compounds are their relationships with certain biomedical entities, in particular genes and proteins. The aim of the DrugProt track (similar to the previous CHEMPROT task of BioCreative VI) is to promote the development and evaluation of systems that are able to automatically detect in relations between chemical compounds/drug and genes/proteins. There are a range of different types of drug-gene/protein interactions, and their systematic extraction and characterization is essential to analyze, predict and explore key biomedical properties underlying high impact biomedical applications. These application scenarios include use cases related to drug discovery, drug repurposing, drug design, metabolic engineering, modeling drug response, pharmacogenet...","","https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/","completed","intermediate","7","","","2021-06-15","2021-09-22","2023-06-23 00:00:00","2023-10-16 18:15:36" +"146","extended-literature-ai-for-drug-induced-liver-injury","Extended Literature AI for Drug Induced Liver Injury","","Unexpected Drug-Induced Liver Injury (DILI) still is one of the main killers of promising novel drug candidates. It is a clinically significant disease that can lead to severe outcomes such as acute liver failure and even death. It remains one of the primary liabilities in drug development and regulatory clearance due to the limited performance of mandated preclinical models even today. The free text of scientific publications is still the main medium carrying DILI results from clinical practice or experimental studies. The textual data still has to be analysed manually. This process, however, is tedious and prone to human mistakes or omissions, as results are very rarely available in a standardized form or organized form. There is thus great hope that modern techniques from machine learning or natural language processing could provide powerful tools to better process and derive the underlying knowledge within free form texts. The pressing need to faster process potential drug can...","","http://camda2022.bioinf.jku.at/contest_dataset#extended_literature_ai_for_drug_induced_liver_injury","completed","intermediate","7","","","\N","2022-05-20","2023-06-23 00:00:00","2023-10-14 05:40:14" +"147","anti-microbial-resistance-forensics","Anti-Microbial Resistance Forensics","","Bacteriophages, being the re-occuring mystery in the history of science are believed to be they key for understanding of microbial evolution and the transfer of AMR genes. Recent studies show that there is a significant correlation between occurence of Phages and AMR genes, indicating that they are indeed taking part in the spread of them. While taking part in AMR dissemination the phages are also considered as the potential alternative to antibiotics. In such contradictory world there is a huge potential as well as urgent need for precise classification, description and analysis of capabilities. Due to pandemic of SARS-CoV-2, advance in phylogenetic algorithms and k-mer based methods have been extremely rapid and those improvements are witing to be adapted to different branches of life sciences.","","http://camda2022.bioinf.jku.at/contest_dataset#anti-microbial_resistance_forensics","completed","intermediate","7","","","\N","2022-05-20","2023-06-23 00:00:00","2023-10-14 05:40:14" +"148","disease-maps-to-modelling-covid-19","Disease Maps to Modelling COVID-19","Use the COVID-19 disease map to suggest drugs candidate for repurposing, tha...","The Disease Maps to modeling COVID-19 Challenge provides highly detailed expert-curated molecular mechanistic maps for COVID-19. Combine them with available omic data to expand the current biological knowledge on COVID-19 mechanism of infection and downstream consequences. The main topic for this year's challenge is drug repurposing with the possibility of Real World Data based validation of the most promising candidates suggested.","","http://camda2022.bioinf.jku.at/contest_dataset#disease_maps_to_modelling_covid-19","completed","intermediate","7","","","\N","2022-05-20","2023-06-23 00:00:00","2023-10-14 05:40:15" +"149","crowdsourced-evaluation-of-inchi-based-tautomer-identification","Crowdsourced Evaluation of InChI-based Tautomer Identification","Calling on scientists from industry, government, and academia dealing with c...","This challenge focuses on the International Chemical Identifier (InChI), which was developed and is maintained under the auspices of the International Union of Pure and Applied Chemistry (IUPAC) and the InChI Trust. The InChI Trust, the IUPAC Working Group on Tautomers, and the U.S. Food and Drug Administration (FDA) call on the scientific community dealing with chemical repositories/data sets and analytics of compounds to test the recently modified InChI algorithm, which was designed for advanced recognition of tautomers. Participants will evaluate this algorithm against real chemical samples in this Crowdsourced Evaluation of InChI-based Tautomer Identification.","","https://precision.fda.gov/challenges/29","completed","intermediate","6","","","2022-11-01","2023-03-01","2023-06-23 00:00:00","2023-10-14 05:40:15" +"150","nctr-indel-calling-from-oncopanel-sequencing-challenge-phase-2","NCTR Indel Calling from Oncopanel Sequencing Challenge Phase 2","In Phase 2, participants who completed in Phase 1 of the challenge have the ...","The high value of clinically actionable information obtained by oncopanel sequencing makes it a crucial tool for precision oncology[1,2]. With the surge in availability of oncopanels, it is critical to ensure that they have been thoroughly tested and are properly used. FDA has initiated the Sequencing Quality Control phase II (SEQC2) project[3] to develop standard analysis protocols and quality control metrics for fit-for-purpose use of Next Generation Sequencing (NGS) data including oncopanel sequencing to inform regulatory science research and precision medicine. The Oncopanel Sequencing Working Group of FDA-led SEQC2 has developed a reference sample[4] suitable for benchmarking oncopanels and comprehensively assessed the analytical performance of several oncopanels[1,2]. The genomic deoxyribonucleic acid (gDNA) reference sample was derived from ten Universal Human Reference RNA (UHRR, Agilent Technologies, Inc) cell-lines and made publicly available by Agilent. Substantial gen...","","https://precision.fda.gov/challenges/22","completed","intermediate","6","","","2022-07-11","2022-07-26","2023-06-23 00:00:00","2023-10-17 23:18:17" +"151","nctr-indel-calling-from-oncopanel-sequencing-data-challenge-phase-1","NCTR Indel Calling from Oncopanel Sequencing Data Challenge Phase 1","Genetic variation involving indels (insertions and deletions) in the cancer ...","The high value of clinically actionable information obtained by oncopanel sequencing makes it a crucial tool for precision oncology[1,2]. With the surge in availability of oncopanels, it is critical to ensure that they have been thoroughly tested and are properly used. FDA has initiated the Sequencing Quality Control phase II (SEQC2) project[3] to develop standard analysis protocols and quality control metrics for fit-for-purpose use of Next Generation Sequencing (NGS) data including oncopanel sequencing to inform regulatory science research and precision medicine. The Oncopanel Sequencing Working Group of FDA-led SEQC2 has developed a reference sample[4] suitable for benchmarking oncopanels and comprehensively assessed the analytical performance of several oncopanels[1,2]. The genomic deoxyribonucleic acid (gDNA) reference sample was derived from ten Universal Human Reference RNA (UHRR, Agilent Technologies, Inc) cell-lines and made publicly available by Agilent. Substantial gen...","","https://precision.fda.gov/challenges/21","completed","intermediate","6","","","2022-05-02","2022-07-08","2023-06-23 00:00:00","2023-10-17 23:18:21" +"152","vha-innovation-ecosystem-and-precisionfda-covid-19-risk-factor-modeling-challenge-phase-2","VHA Innovation Ecosystem and precisionFDA COVID-19 Risk Factor Modeling Challenge Phase 2","The focus of Phase 2 was to validate the top performing models on two additi...","The novel coronavirus disease 2019 (COVID-19) is a respiratory disease caused by a new type of coronavirus, known as “severe acute respiratory syndrome coronavirus 2,” or SARS-CoV-2. On March 11, 2020, the World Health Organization (WHO) declared the outbreak a global pandemic. As of January 22nd, 2022, the Johns Hopkins University COVID-19 dashboard reports over 338 million total confirmed cases worldwide. Although most people have mild to moderate symptoms, the disease can cause severe medical complications leading to death in some people. The Centers for Disease Control and Prevention (CDC) have identified several risk factors for severe COVID-19 illness, including people 65 years and older, individuals living in nursing homes or long-term care facilities, and those with serious underlying medical conditions. The Veteran population has a higher prevalence of several of the known risk factors for severe COVID-19 illness, such as advanced age, heart disease, and diabetes. Identif...","","https://precision.fda.gov/challenges/20","completed","intermediate","6","","","2021-04-14","2022-01-28","2023-06-23 00:00:00","2023-10-14 05:40:19" +"153","tumor-mutational-burden-tmb-challenge-phase-2","Tumor Mutational Burden (TMB) Challenge Phase 2","The goal of the Friends of Cancer Research and precisionFDA Tumor Mutational...","Tumor mutational burden (TMB) is generally defined as the number of mutations detected in a patient's tumor sample per megabase of DNA sequenced. However different algorithms use different methods for calculating TMB. Mutations in genes in tumor cells may lead to the creation of neoantigens, which have the potential to activate an immune system response against the tumor, and the likelihood of an immune system response may increase with the number of mutations. Thus, TMB is a biomarker for some immunotherapy drugs, called immune checkpoint inhibitors, such as those that target the PD-1 and PD-L1 pathways (Chan et al., 2019). An outstanding problem is the lack of standardization for TMB calculation and reporting between different assays. To address this problem, the Friends of Cancer Research convened a working group of industry and regulatory stakeholders to develop guidance and tools for TMB harmonization. Results from the first phase of this effort were presented at AACR 2020 (s...","","https://precision.fda.gov/challenges/18","completed","intermediate","6","","","2021-07-19","2021-09-12","2023-06-23 00:00:00","2023-10-14 05:40:20" +"154","predicting-gene-expression-using-millions-of-random-promoter-sequences","Predicting Gene Expression Using Millions of Random Promoter Sequences","","Decoding how gene expression is regulated is critical to understanding disease. Regulatory DNA is decoded by the cell in a process termed “cis-regulatory logic”, where proteins called Transcription Factors (TFs) bind to specific DNA sequences within the genome and work together to produce as output a level of gene expression for downstream adjacent genes. This process is exceedingly complex to model as a large number of parameters is needed to fully describe the process (see Rationale, de Boer et al. 2020; Zeitingler J. 2020). Understanding the cis-regulatory logic of the human genome is an important goal and would provide insight into the origins of many diseases. However, learning models from human data is challenging due to limitations in the diversity of sequences present within the human genome (e.g. extensive repetitive DNA), the vast number of cell types that differ in how they interpret regulatory DNA, limited reporter assay data, and substantial technical biases present i...","","https://www.synapse.org/#!Synapse:syn28469146/wiki/617075","completed","intermediate","1","","","2022-06-15","2022-08-07","2023-06-23 00:00:00","2023-10-14 05:40:21" +"155","brats-2023","BraTS 2023","","The International Brain Tumor Segmentation (BraTS) challenge. BraTS, since 2012, has focused on the generation of a benchmarking environment and dataset for the delineation of adult brain gliomas. The focus of this year’s challenge remains the generation of a common benchmarking environment, but its dataset is substantially expanded to ~4,500 cases towards addressing additional i) populations (e.g., sub-Saharan Africa patients), ii) tumors (e.g., meningioma), iii) clinical concerns (e.g., missing data), and iv) technical considerations (e.g., augmentations). Specifically, the focus of BraTS 2023 is to identify the current state-of-the-art algorithms for addressing (Task 1) the same adult glioma population as in the RSNA-ANSR-MICCAI BraTS challenge, as well as (Task 2) the underserved sub-Saharan African brain glioma patient population, (Task 3) intracranial meningioma, (Task 4) brain metastasis, (Task 5) pediatric brain tumor patients, (Task 6) global & local missing data, (Task 7...","","https://www.synapse.org/brats","active","advanced","1","","","2023-06-01","2023-08-25","2023-06-23 00:00:00","2023-10-14 05:40:21" +"156","cagi7","CAGI7","The seventh round of CAGI","There have been six editions of CAGI experiments, held between 2010 and 2022. The seventh round of CAGI is planned to take place over the Summer of 2024.","","https://genomeinterpretation.org/challenges.html","upcoming","intermediate","1","","","\N","\N","2023-08-04 21:47:38","2023-10-14 05:40:32" +"157","casp15","CASP15","Establish the state-of-art in modeling proteins and protein complexes","CASP14 (2020) saw an enormous jump in the accuracy of single protein and domain models such that many are competitive with experiment. That advance is largely the result of the successful application of deep learning methods, particularly by the AlphaFold and, since that CASP, RosettaFold. As a consequence, computed protein structures are becoming much more widely used in a broadening range of applications. CASP has responded to this new landscape with a revised set of modeling categories. Some old categories have been dropped (refinement, contact prediction, and aspects of model accuracy estimation) and new ones have been added (RNA structures, protein ligand complexes, protein ensembles, and accuracy estimation for protein complexes). We are also strengthening our interactions with our partners CAPRI and CAMEO. We hope that these changes will maximize the insight that CASP15 provides, particularly in new applications of deep learning.","","https://predictioncenter.org/casp15/index.cgi","completed","intermediate","14","","","2022-04-18","\N","2023-08-04 21:52:12","2023-09-28 23:09:59" +"158","synthrad2023","SynthRAD2023","Synthesizing computed tomography for radiotherapy","This challenge aims to provide the first platform offering public data evaluation metrics to compare the latest developments in sCT generation methods. The accepted challenge design approved by MICCAI can be found at https://doi.org/10.5281/zenodo.7746019. A type 2 challenge will be run, where the participant needs to submit their algorithm packaged in a docker both for validation and test.","","https://synthrad2023.grand-challenge.org/","active","intermediate","5","","","2023-04-01","2023-08-22","2023-08-04 21:54:31","2023-09-28 23:12:01" +"159","synthetic-data-for-instrument-segmentation-in-surgery-syn-iss","Synthetic Data for Instrument Segmentation in Surgery (Syn-ISS)","","A common limitation noted by the surgical data science community is the size of datasets and the resources needed to generate training data at scale for building reliable and high-performing machine learning models. Beyond unsupervised and self-supervised approaches another solution within the broader machine learning community has been a growing volume of literature in the use of synthetic data (simulation) for training algorithms than can be applied to real world data. Synthetic data has multiple benefits like free groundtruth at large scale, possibility to collect larger sample of rare events, include anatomical variations, etc. A first step towards proving the validity of using synthetic data for real world applications is to demonstrate the feasibility within the simulation world itself. Our proposed challenge is to train machine learning methods for instrument segmentation using synthetic datasets and test their performance on synthetic datasets. That is, the challenge parti...","","https://www.synapse.org/#!Synapse:syn50908388/wiki/620516","active","intermediate","1","","","2023-07-19","2023-09-07","2023-08-04 23:49:44","2023-10-10 19:52:16" +"160","pitvis","PitVis","Surgical workflow and instrument recognition in endonasal surgery","The pituitary gland, found just off the base of the brain, is commonly known as “the master gland”, performing essential functions required for sustaining human life. Clinically relevant tumours that have grown on the pituitary gland have an estimated prevalence of 1 in 1000 of the population, and if left untreated can be life-limiting. The “gold standard” treatment is endoscopic pituitary surgery, where the tumour is directly removed by entering through a nostril. This surgery is particularly challenging due to the small working space which limits both vision and instrument manoeuvrability and thus can lead to poor surgical technique causing adverse outcomes for the patient. Computer-assisted intervention can help overcome these challenges by providing guidance for senior surgeons and operative staff during surgery, and for junior surgeons during training.","","https://www.synapse.org/#!Synapse:syn51232283/wiki/","active","intermediate","1","","","2023-06-29","2023-09-10","2023-08-04 23:58:01","2023-09-28 23:12:09" +"161","mvseg2023","MVSEG2023","Automatically segment mitral valve leaflets from single frame 3D trans-esoph...","Mitral valve (MV) disease is a common pathologic problem occurring in approximately 2 % of the general population but climbing to 10 % in those over the age of 75. The preferred intervention for mitral regurgitation is valve repair, due to superior patient outcomes compared to those following valve replacement. Mitral valve interventions are technically challenging due to the functional and anatomical complexity of mitral pathologies. Repair must be tailored to the patient-specific anatomy and pathology, which requires considerable expert training and experience. Automatic segmentation of the mitral valve leaflets from 3D transesophageal echocardiography (TEE) may play an important role in treatment planning, as well as physical and computational modelling of patient-specific valve pathologies and potential repair approaches. This may have important implications in the drive towards personalized care and has the potential to impact clinical outcomes for those undergoing mitral val...","","https://www.synapse.org/#!Synapse:syn51186045/wiki/621356","completed","intermediate","1","","","2023-05-29","2023-08-07","2023-08-05 0-04-36","2023-09-28 23:12:19" +"162","crossmoda23","crossMoDA23","This challenge proposes is the third edition of the first medical imaging be...","Domain Adaptation (DA) has recently raised strong interest in the medical imaging community. By encouraging algorithms to be robust to unseen situations or different input data domains, Domain Adaptation improves the applicability of machine learning approaches to various clinical settings. While a large variety of DA techniques has been proposed, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly address single-class problems. To tackle these limitations, the crossMoDA challenge introduced the first large and multi-class dataset for unsupervised cross-modality Domain Adaptation. From an application perspective, crossMoDA focuses on MRI segmentation for Vestibular Schwannoma. Compared to the previous crossMoDA instance, which made use of multi-institutional data acquired in controlled conditions for radiosurgery planning and focused on a 2 class segmentation task (tumour and cochlea), the...","","https://www.synapse.org/#!Synapse:syn51236108/wiki/621615","completed","intermediate","1","","","2023-04-15","2023-07-10","2023-08-05 0-13-23","2023-10-12 18:10:18" +"163","icr-identify-age-related-conditions","ICR - Identifying Age-Related Conditions","Use Machine Learning to detect conditions with measurements of anonymous cha...","The goal of this competition is to predict if a person has any of three medical conditions. You are being asked to predict if the person has one or more of any of the three medical conditions (Class 1), or none of the three medical conditions (Class 0). You will create a model trained on measurements of health characteristics. To determine if someone has these medical conditions requires a long and intrusive process to collect information from patients. With predictive models, we can shorten this process and keep patient details private by collecting key characteristics relative to the conditions, then encoding these characteristics.","","https://www.kaggle.com/competitions/icr-identify-age-related-conditions","completed","intermediate","8","","","2023-05-11","2023-08-10","2023-08-05 0-32-01","2023-10-12 18:15:08" +"164","cafa-5-protein-function-prediction","CAFA 5: Protein Function Prediction","Predict the biological function of a protein","The goal of this competition is to predict the function of a set of proteins. You will develop a model trained on the amino-acid sequences of the proteins and on other data. Your work will help ​​researchers better understand the function of proteins, which is important for discovering how cells, tissues, and organs work. This may also aid in the development of new drugs and therapies for various diseases.","","https://www.kaggle.com/competitions/cafa-5-protein-function-prediction","completed","intermediate","8","","operation_1777","2023-04-18","2023-08-21","2023-08-05 5-18-40","2023-10-19 00:13:14" +"165","rsna-2023-abdominal-trauma-detection","RSNA 2023 Abdominal Trauma Detection","Detect and classify traumatic abdominal injuries","Traumatic injury is the most common cause of death in the first four decades of life and a major public health problem around the world. There are estimated to be more than 5 million annual deaths worldwide from traumatic injury. Prompt and accurate diagnosis of traumatic injuries is crucial for initiating appropriate and timely interventions, which can significantly improve patient outcomes and survival rates. Computed tomography (CT) has become an indispensable tool in evaluating patients with suspected abdominal injuries due to its ability to provide detailed cross-sectional images of the abdomen. Interpreting CT scans for abdominal trauma, however, can be a complex and time-consuming task, especially when multiple injuries or areas of subtle active bleeding are present. This challenge seeks to harness the power of artificial intelligence and machine learning to assist medical professionals in rapidly and precisely detecting injuries and grading their severity. The development...","","https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection","completed","intermediate","8","","","2023-07-26","2023-10-13","2023-08-05 5-24-09","2023-09-28 23:14:12" +"166","hubmap-hacking-the-human-vasculature","HuBMAP: Hacking the Human Vasculature","Segment instances of microvascular structures from healthy human kidney tiss...","The goal of this competition is to segment instances of microvascular structures, including capillaries, arterioles, and venules. You'll create a model trained on 2D PAS-stained histology images from healthy human kidney tissue slides. Your help in automating the segmentation of microvasculature structures will improve researchers' understanding of how the blood vessels are arranged in human tissues.","","https://www.kaggle.com/competitions/hubmap-hacking-the-human-vasculature","completed","intermediate","8","","","2023-05-22","2023-07-31","2023-08-05 5-31-12","2023-10-12 18:15:00" +"167","amp-parkinsons-disease-progression-prediction","AMP(R)-Parkinson's Disease Progression Prediction","Use protein and peptide data measurements from Parkinson's Disease patients ...","The goal of this competition is to predict MDS-UPDR scores, which measure progression in patients with Parkinson's disease. The Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is a comprehensive assessment of both motor and non-motor symptoms associated with Parkinson's. You will develop a model trained on data of protein and peptide levels over time in subjects with Parkinson’s disease versus normal age-matched control subjects. Your work could help provide important breakthrough information about which molecules change as Parkinson’s disease progresses.","","https://www.kaggle.com/competitions/amp-parkinsons-disease-progression-prediction","completed","intermediate","8","","","2023-02-16","2023-05-18","2023-08-05 5-37-12","2023-10-10 19:52:34" +"168","open-problems-multimodal","Open Problems -Multimodal Single-Cell Integration","Predict how DNA, RNA & protein measurements co-vary in single cells","The goal of this competition is to predict how DNA, RNA, and protein measurements co-vary in single cells as bone marrow stem cells develop into more mature blood cells. You will develop a model trained on a subset of 300,000-cell time course dataset of CD34+ hematopoietic stem and progenitor cells (HSPC) from four human donors at five time points generated for this competition by Cellarity, a cell-centric drug creation company. In the test set, taken from an unseen later time point in the dataset, competitors will be provided with one modality and be tasked with predicting a paired modality measured in the same cell. The added challenge of this competition is that the test data will be from a later time point than any time point in the training data. Your work will help accelerate innovation in methods of mapping genetic information across layers of cellular state. If we can predict one modality from another, we may expand our understanding of the rules governing these complex re...","","https://www.kaggle.com/competitions/open-problems-multimodal","completed","intermediate","8","","","2022-08-15","2022-11-15","2023-08-05 5-43-25","2023-10-10 19:52:41" +"169","multi-atlas-labeling-beyond-the-cranial-vault","Multi-Atlas Labeling Beyond the Cranial Vault","","Multi-atlas labeling has proven to be an effective paradigm for creating segmentation algorithms from training data. These approaches have been extraordinarily successful for brain and cranial structures (e.g., our prior MICCAI workshops-MLSF’11, MAL’12, SATA’13). After the original challenges closed, the data continue to drive scientific innovation; 144 groups have registered for the 2012 challenge (brain only) and 115 groups for the 2013 challenge (brain/heart/canine leg). However, innovation in application outside of the head and to soft tissues has been more limited. This workshop will provide a snapshot of the current progress in the field through extended discussions and provide researchers an opportunity to characterize their methods on a newly created and released standardized dataset of abdominal anatomy on clinically acquired CT. The datasets will be freely available both during and after the challenge. We have two separate new challenges-abdomen and cervix on routinely ...","","https://www.synapse.org/#!Synapse:syn3193805/wiki/89480","active","intermediate","1","","","2015-04-15","\N","2023-08-07 20:21:22","2023-10-10 19:52:39" +"170","hubmap-organ-segmentation","HuBMAP + HPA: Hacking the Human Body","Segment multi-organ functional tissue units","In this competition, you’ll identify and segment functional tissue units (FTUs) across five human organs. You'll build your model using a dataset of tissue section images, with the best submissions segmenting FTUs as accurately as possible. If successful, you'll help accelerate the world’s understanding of the relationships between cell and tissue organization. With a better idea of the relationship of cells, researchers will have more insight into the function of cells that impact human health. Further, the Human Reference Atlas constructed by HuBMAP will be freely available for use by researchers and pharmaceutical companies alike, potentially improving and prolonging human life.","","https://www.kaggle.com/competitions/hubmap-organ-segmentation","completed","intermediate","8","","","2022-06-22","2022-09-22","2023-08-08 16:30:22","2023-10-12 18:14:20" +"171","hubmap-kidney-segmentation","HuBMAP: Hacking the Kidney","Identify glomeruli in human kidney tissue images","This competition, “Hacking the Kidney, starts by mapping the human kidney at single cell resolution. Your challenge is to detect functional tissue units (FTUs) across different tissue preparation pipelines. An FTU is defined as a “three-dimensional block of cells centered around a capillary, such that each cell in this block is within diffusion distance from any other cell in the same block” ([de Bono, 2013](https://www.ncbi.nlm.nih.gov/pubmed/24103658)). The goal of this competition is the implementation of a successful and robust glomeruli FTU detector. You will also have the opportunity to present your findings to a panel of judges for additional consideration. Successful submissions will construct the tools, resources, and cell atlases needed to determine how the relationships between cells can affect the health of an individual. Advancements in HuBMAP will accelerate the world’s understanding of the relationships between cell and tissue organization and function and human health.","","https://www.kaggle.com/competitions/hubmap-kidney-segmentation","completed","intermediate","8","","","2020-11-16","2021-05-10","2023-08-08 17:31:46","2023-10-12 18:14:16" +"172","ventilator-pressure-prediction","Google Brain: Ventilator Pressure Prediction","Simulate a ventilator connected to a sedated patient's lung","In this competition, you’ll simulate a ventilator connected to a sedated patient's lung. The best submissions will take lung attributes compliance and resistance into account. If successful, you'll help overcome the cost barrier of developing new methods for controlling mechanical ventilators. This will pave the way for algorithms that adapt to patients and reduce the burden on clinicians during these novel times and beyond. As a result, ventilator treatments may become more widely available to help patients breathe.","","https://www.kaggle.com/competitions/ventilator-pressure-prediction","completed","intermediate","8","","","2021-09-22","2021-11-03","2023-08-08 17:53:33","2023-10-12 18:14:11" +"173","stanford-covid-vaccine","OpenVaccine - COVID-19 mRNA Vaccine Degradation Prediction","Urgent need to bring the COVID-19 vaccine to mass production","In this competition, we are looking to leverage the data science expertise of the Kaggle community to develop models and design rules for RNA degradation. Your model will predict likely degradation rates at each base of an RNA molecule, trained on a subset of an Eterna dataset comprising over 3000 RNA molecules (which span a panoply of sequences and structures) and their degradation rates at each position. We will then score your models on a second generation of RNA sequences that have just been devised by Eterna players for COVID-19 mRNA vaccines. These final test sequences are currently being synthesized and experimentally characterized at Stanford University in parallel to your modeling efforts--Nature will score your models!","","https://www.kaggle.com/competitions/stanford-covid-vaccine","completed","intermediate","8","","","2020-09-10","2020-10-06","2023-08-08 18:06:17","2023-10-12 18:14:27" +"174","openvaccine","OpenVaccine","To develop mRNA vaccines stable enough to be deployed to everyone in the wor...","mRNA vaccines are a relatively new technology that have come into the limelight with the onset of COVID-19. They were the first COVID-19 vaccines to start clinical trials (initially formulated in a matter of days) and the first to be approved and distributed. mRNA vaccines have the potential to transform immunization, being significantly faster to formulate and produce, cheaper, and more effective-including against mutant strains. However, there is one key bottleneck to their widespread viability and our ability to immunize the entire world-poor refrigerator stability in prefilled syringes. The OpenVaccine challenge aims to allow a worldwide community of game players to create an enhanced vaccine to be injected into millions of people. The challenge-design an mRNA that codes for the same amino acid sequence of the spike protein, but is 2x-10x+ more stable. Through a number of academic partnerships and the launch of a Kaggle machine learning challenge to create best-in-class algori...","","https://eternagame.org/challenges/10845741","completed","intermediate","13","https://doi.org/10.1038/s41467-022-28776-w","","\N","2021-12-12","2023-08-08 18:22:49","2023-09-28 23:17:02" +"175","opentb","OpenTB","What if we could use RNA to detect a gene sequence found to be present only ...","OpenTB used a recently reported gene signature for active tuberculosis based on three RNAs in the blood. This signature could form the basis for a fast, color-based test for TB, similar to an over-the-counter pregnancy test. What was needed was a sensor that could detect the concentrations of three RNAs, carry out the needed calculation, and report the result by binding another molecule. Over four rounds, players designed RNA sensors that can do the math on these 3 genes. Through experimental feedback, they honed their skills and techniques, which resulted in the creation of multiple designs that have been shown to be successful. These findings are being prepared to be published, and future work will be done to develop diagnostic devices integrating these designs","","https://eternagame.org/challenges/10845742","completed","intermediate","13","","","2016-05-04","2018-04-15","2023-08-08 18:43:09","2023-09-28 23:17:09" +"176","opencrispr","OpenCRISPR","A project to discover design patterns for guide RNAs to make gene editing mo...","CRISPR gene editing is a RNA-based method that can target essentially any gene in a living organism for genetic changes. Since its first demonstration, CRISPR has been revolutionizing biology and promises to change how we tackle numerous human diseases from malaria to cancer. Stanford's Center for Personal Dynamic Regulomes and UC Berkeley's Innovative Genomics Institute have challenged Eterna players to solve a remaining hurdle in making this technology safe for use. Scientists want the power to turn on and off CRISPR on demand with small molecules. This is almost a perfect match to the small-molecule switches that the Eterna community has worked on. In fact, the MS2 RNA hairpin often used in Eterna is routinely used to recruit new functionality to CRISPR complexes through other molecules tethered to the MS2 protein. The puzzles began with OpenCRISPR Controls, looking for solutions to lock in or lock out the MS2 RNA hairpin within a special loop in the CRISPR RNA. We hope the res...","","https://eternagame.org/challenges/10845743","completed","intermediate","13","https://doi.org/10.1021/acssynbio.9b00142","","2017-08-26","\N","2023-08-08 18:43:14","2023-10-10 19:57:07" +"177","openknot","OpenKnot","Many important biological processes depend on RNAs that form pseudoknots, an...","RNA pseudoknots have significant biological importance in various processes. They participate in gene regulation by influencing translation initiation or termination in mRNA molecules. Pseudoknots also play a role in programmed ribosomal frameshifting, leading to the production of different protein products from a single mRNA. RNA viruses, including SARS-CoV-2 and Dengue virus, utilize pseudoknots to regulate their replication and control the synthesis of viral proteins. Additionally, certain RNA molecules with pseudoknot structures exhibit enzymatic activity, acting as ribozymes and catalyzing biochemical reactions. These functions highlight the crucial role of RNA pseudoknots in gene expression, proteomic diversity, viral replication, and enzymatic processes. Several unanswered scientific questions surround RNA pseudoknots. One key area of inquiry is understanding the folding pathways of pseudoknots and how they form from linear RNA sequences. Elucidating the structural dynamics...","","https://eternagame.org/challenges/11843006","active","intermediate","13","","","2022-06-17","\N","2023-08-08 18:43:22","2023-10-10 19:52:53" +"178","openaso","OpenASO","A research initiative aimed at developing innovative design principles for R...","The DNA genome is the blueprint for building and operating cells, but this information must be decoded into RNA molecules to be useful. Transcription is the process of decoding DNA genomic information into RNA, resulting in RNA transcripts. Genes are specific sequences of DNA that contain information to produce a specific RNA transcript. The fate of most mRNA molecules in the cell is to be translated by ribosomes into protein molecules. However, mRNA splicing is a crucial step that occurs between the formation of an RNA transcript and protein translation. This step is essential because genes contain non-protein coding introns and protein-coding exons. Splicing removes introns and joins exons to produce a mature mRNA molecule that can be decoded into the correct protein molecule. When the splicing process is corrupted due to genetic mutations, the resulting RNA can become toxic, leading to the synthesis of non-functional proteins or no protein at all, causing various human diseases...","","https://eternagame.org/challenges/11546273","active","intermediate","13","","","2023-02-20","\N","2023-08-08 18:43:25","2023-10-10 19:52:57" +"179","openribosome","OpenRibosome","We aim to 1) gain fundamental insights into the ribosome's RNA sequence-fold...","Our modern world has many challenges-challenges like climate change, increasing waste production, and human health. Imagine-we could replace petrochemistry with biology, single-use plastics with selectively degradable polymers, broad chemotherapeutics with targeted medicines for fighting specific cancer cells, and complex health equipment with point-of-care diagnostics. These innovations and many more can empower us to confront the challenges affecting humanity, our world, and beyond. But how do we actually create these smart materials and medicines? Is it possible to do so by repurposing one of Nature's molecular machines? We think we can. The answer? Customized ribosomes. In Nature, ribosomes are the catalysts for protein assembly. And proteins are more or less similar, chemically, to the smart materials and medicines we want to synthesize. If we could modify ribosomes to build polymers with diverse components-beyond the canonical amino acids us","","https://eternagame.org/challenges/11043833","active","intermediate","13","https://doi.org/10.1038/s41467-023-35827-3","","2019-01-31","\N","2023-08-08 18:43:27","2023-10-10 19:53:01" +"180","lish-moa","Mechanisms of Action (MoA) Prediction","Can you improve the algorithm that classifies drugs based on their biologica...","Can you improve the algorithm that classifies drugs based on their biological activity?","","https://www.kaggle.com/competitions/lish-moa","completed","intermediate","8","","","2020-09-03","2020-11-30","2023-08-08 19:09:31","2023-09-28 23:18:04" +"181","recursion-cellular-image-classification","Recursion Cellular Image Classification","CellSignal-Disentangling biological signal from experimental noise in cellul...","This competition will have you disentangling experimental noise from real biological signals. Your entry will classify images of cells under one of 1,108 different genetic perturbations. You can help eliminate the noise introduced by technical execution and environmental variation between experiments. If successful, you could dramatically improve the industry’s ability to model cellular images according to their relevant biology. In turn, applying AI could greatly decrease the cost of treatments, and ensure these treatments get to patients faster.","","https://www.kaggle.com/competitions/recursion-cellular-image-classification","completed","intermediate","8","","","2019-06-27","2019-09-26","2023-08-08 19:38:42","2023-10-10 19:53:05" +"182","tlvmc-parkinsons-freezing-gait-prediction","Parkinson's Freezing of Gait Prediction","Event detection from wearable sensor data","The goal of this competition is to detect freezing of gait (FOG), a debilitating symptom that afflicts many people with Parkinson’s disease. You will develop a machine learning model trained on data collected from a wearable 3D lower back sensor. Your work will help researchers better understand when and why FOG episodes occur. This will improve the ability of medical professionals to optimally evaluate, monitor, and ultimately, prevent FOG events.","","https://www.kaggle.com/competitions/tlvmc-parkinsons-freezing-gait-prediction","completed","intermediate","8","","","2023-03-09","2023-06-08","2023-08-08 19:47:54","2023-10-10 19:53:08" +"183","chaimeleon","CHAIMELEON Open Challenges","","The CHAIMELEON Open Challenges is a competition designed to train and refine AI models to answer clinical questions about five types of cancer-prostate, lung, breast, colon, and rectal. Participants are challenged to collaborate and develop innovative AI-powered solutions that can significantly impact cancer diagnosis, management, and treatment. They will be evaluated considering a balance between the performance of their AI algorithms to predict different clinical endpoints such as disease staging, treatment response or progression free survival and their trustworthiness. The challenges are open to the whole scientific and tech community interested in AI. They are a unique opportunity to showcase how AI can be used to advance medical research and improve patient outcomes within the CHAIMELEON project.","","https://chaimeleon.grand-challenge.org/","upcoming","intermediate","5","","","\N","2023-12-31","2023-08-09 17:13:09","2023-10-10 19:53:10" +"184","topcow23","Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA","","The aim of the challenge is to extract the CoW angio-architecture from 3D angiographic imaging by segmentation of the vessel components. There are two sub-tasks-binary segmentation of CoW vessels, and multi-class CoW anatomical segmentation. We release a new dataset of joint-modalities, CTA and MRA of the same patient cohort, both with annotations of the anatomy of CoW. Our challenge has two tracks for the same segmentation task, namely CTA track and MRA track. We made use of the clinical information from both modalities during our annotation. And participants can pick whichever modality they want, both CTA and MRA, and choose to tackle the task for either modality.","","https://topcow23.grand-challenge.org/","completed","intermediate","5","","","2023-08-20","2023-09-25","2023-08-09 17:16:22","2023-09-28 23:24:41" +"185","circle-of-willis-intracranial-artery-classification-and-quantification-challenge-2023","Circle of Willis Intracranial Artery Classification and Quantification Challenge 2023","","The purpose of this challenge is to compare automatic methods for classification of the circle of Willis (CoW) configuration and quantification of the CoW major artery diameters and bifurcation angles.","","https://crown.isi.uu.nl/","completed","intermediate","14","","","2023-05-01","2023-08-15","2023-08-09 22:13:24","2023-09-28 23:24:54" +"186","making-sense-of-electronic-health-record-ehr-race-and-ethnicity-data","Making Sense of Electronic Health Record (EHR) Race and Ethnicity Data","The US Food and Drug Administration (FDA) calls on stakeholders, including t...","The urgency of the coronavirus disease 2019 (COVID-19) pandemic has heightened interest in the use of real-world data (RWD) to obtain timely information about patients and populations and has focused attention on EHRs. The pandemic has also heightened awareness of long-standing racial and ethnic health disparities along a continuum from underlying social determinants of health, exposure to risk, access to insurance and care, quality of care, and responses to treatments. This highlighted the potential that EHRs can be used to describe and contribute to our understanding of racial and ethnic health disparities and their solutions. The OMB Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity provides minimum standards for maintaining, collecting, and presenting data on race and ethnicity for all Federal reporting purposes, and defines the two separate constructs of race and ethnicity.","","https://precision.fda.gov/challenges/30","completed","intermediate","6","","","2023-05-31","2023-06-23","2023-08-10 18:28:06","2023-10-10 19:53:12" +"187","the-veterans-cardiac-health-and-ai-model-predictions-v-champs","The Veterans Cardiac Health and AI Model Predictions (V-CHAMPS)","The Veterans Health Administration Innovation Ecosystem, the Digital Health ...","To better understand the risk and protective factors in the Veteran population, the VHA IE and its collaborating partners are calling upon the public to develop AI/ML models to predict cardiovascular health outcomes, including readmission and mortality, using synthetically generated Veteran health records. The Challenge consists of two Phases-Phase 1 is focused on synthetic data. In this Phase of the Challenge, AI/ML models will be developed by Challenge participants and trained and tested on the synthetic data sets provided to them, with a view towards predicting outcome variables for Veterans who have been diagnosed with chronic heart failure (please note that in Phase 1, the data is synthetic Veteran health records). Phase 2 will focus on validating and further exploring the limits of the AI/ML models. During this Phase, high-performing AI/ML models from Phase 1 will be brought into the VA system and validated on the real-world Veterans health data within the VHA. These models...","","https://precision.fda.gov/challenges/31","completed","intermediate","6","","","2023-05-25","2023-08-02","2023-08-10 21:41:10","2023-09-28 23:25:45" +"188","predicting-high-risk-breast-cancer-phase-1","Predicting High Risk Breast Cancer - Phase 1","Predicting High Risk Breast Cancer-a Nightingale OS & AHLI data challenge","Every year, 40 million women get a mammogram; some go on to have an invasive biopsy to better examine a concerning area. Underneath these routine tests lies a deep—and disturbing—mystery. Since the 1990s, we have found far more ‘cancers’, which has in turn prompted vastly more surgical procedures and chemotherapy. But death rates from metastatic breast cancer have hardly changed. When a pathologist looks at a biopsy slide, she is looking for known signs of cancer-tubules, cells with atypical looking nuclei, evidence of rapid cell division. These features, first identified in 1928, still underlie critical decisions today-which women must receive urgent treatment with surgery and chemotherapy? And which can be prescribed “watchful waiting”, sparing them invasive procedures for cancers that would not harm them? There is already evidence that algorithms can predict which cancers will metastasize and harm patients on the basis of the biopsy image. Fascinatingly, these algorithms also h...","","https://app.nightingalescience.org/contests/3jmp2y128nxd","completed","intermediate","15","","","2022-06-01","2023-01-12","2023-08-22 17:07:00","2023-10-12 17:55:10" +"189","predicting-high-risk-breast-cancer-phase-2","Predicting High Risk Breast Cancer - Phase 2","Predicting High Risk Breast Cancer-a Nightingale OS & AHLI data challenge","Every year, 40 million women get a mammogram; some go on to have an invasive biopsy to better examine a concerning area. Underneath these routine tests lies a deep—and disturbing—mystery. Since the 1990s, we have found far more ‘cancers’, which has in turn prompted vastly more surgical procedures and chemotherapy. But death rates from metastatic breast cancer have hardly changed. When a pathologist looks at a biopsy slide, she is looking for known signs of cancer-tubules, cells with atypical looking nuclei, evidence of rapid cell division. These features, first identified in 1928, still underlie critical decisions today-which women must receive urgent treatment with surgery and chemotherapy? And which can be prescribed “watchful waiting”, sparing them invasive procedures for cancers that would not harm them? There is already evidence that algorithms can predict which cancers will metastasize and harm patients on the basis of the biopsy image. Fascinatingly, these algorithms also...","","https://app.nightingalescience.org/contests/vd8g98zv9w0p","completed","intermediate","15","","","2023-02-03","2023-05-13","2023-08-22 17:07:01","2023-10-12 17:55:08" +"190","dream-2-in-silico-network-inference","DREAM 2 - In Silico Network Inference","Predicting the connectivity and properties of in-silico networks.","Three in-silico networks were created and endowed with a dynamics that simulate biological interactions. The challenge consists of predicting the connectivity and some of the properties of one or more of these three networks.","","https://www.synapse.org/#!Synapse:syn2825394/wiki/71150","completed","intermediate","1","","","2007-03-25","\N","2023-08-24 18:54:05","2023-10-12 17:55:03" +"191","dream-3-in-silico-network-challenge","DREAM 3 - In Silico Network Challenge","The goal of the in silico challenges is the reverse engineering of gene netw...","The goal of the in silico challenges is the reverse engineering of gene networks from steady state and time series data. Participants are challenged to predict the directed unsigned network topology from the given in silico generated gene topic_3170sets.","","https://www.synapse.org/#!Synapse:syn2853594/wiki/71567","completed","intermediate","1","https://doi.org/10.1089/cmb.2008.09TT","","2008-06-09","\N","2023-08-25 16:43:41","2023-10-12 17:55:02" +"192","dream-4-in-silico-network-challenge","DREAM 4 - In Silico Network Challenge","The goal of the in silico network challenge is to reverse engineer gene regu...","The goal of the in silico network challenge is to reverse engineer gene regulation networks from simulated steady-state and time-series data. Participants are challenged to infer the network structure from the given in silico gene topic_3170sets. Optionally, participants may also predict the response of the networks to a set of novel perturbations that were not included in the provided datasets.","","https://www.synapse.org/#!Synapse:syn3049712/wiki/74628","completed","intermediate","1","https://doi.org/10.1073/pnas.0913357107","","2009-06-09","\N","2023-08-25 16:43:42","2023-10-12 17:55:00" +"193","dream-5-network-inference-challenge","DREAM 5 - Network Inference Challenge","The goal of this Network Inference Challenge is to reverse engineer gene reg...","The goal of this Network Inference Challenge is to reverse engineer gene regulatory networks from gene topic_3170sets. Participants are given four microarray compendia and are challenged to infer the structure of the underlying transcriptional regulatory networks. Three of the four compendia were obtained from microorganisms, some of which are pathogens of clinical relevance. The fourth compendium is based on an in-silico (i.e., simulated) network. Each compendium consists of hundreds of microarray experiments, which include a wide range of genetic, drug, and environmental perturbations (or in the in-silico network case, simulations thereof). Network predictions will be evaluated on a subset of known interactions for each organism, or on the known network for the in-silico case.","","https://www.synapse.org/#!Synapse:syn2787209/wiki/70349","completed","intermediate","1","https://doi.org/10.1038/nmeth.2016","","2010-06-09","2010-10-31","2023-08-25 16:43:43","2023-10-12 17:54:57" +"194","nlp-sandbox-date-annotation","NLP Sandbox Date Annotation","Identify dates in clinical notes.","An NLP Sandbox Date Annotator takes as input a clinical note and outputs a list of predicted date annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","intermediate","1","https://doi.org/10.7303/syn22277123","","2021-06-04","2023-09-01","2023-08-25 16:45:22","2023-09-28 23:59:02" +"195","nlp-sandbox-person-name-annotation","NLP Sandbox Person Name Annotation","Identify person names in clinical notes.","An NLP Sandbox Person Name Annotator takes as input a clinical note and outputs a list of predicted person name annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","intermediate","1","https://doi.org/10.7303/syn22277123","","2021-06-04","2023-09-01","2023-09-08 16:44:20","2023-09-28 23:59:20" +"196","nlp-sandbox-location-annotation","NLP Sandbox Location Annotation","Identify location information in clinical notes.","An NLP Sandbox Location Annotator takes as input a clinical note and outputs a list of predicted location annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","intermediate","1","https://doi.org/10.7303/syn22277123","","2021-06-04","2023-09-01","2023-09-08 16:44:21","2023-09-28 23:59:21" +"197","nlp-sandbox-contact-annotation","NLP Sandbox Contact Annotation","Identify contact information in clinical notes.","An NLP Sandbox contact annotator takes as input a clinical note and outputs a list of predicted contact annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","intermediate","1","https://doi.org/10.7303/syn22277123","","2021-06-04","2023-09-01","2023-09-08 16:44:22","2023-09-28 23:59:21" +"198","nlp-sandbox-id-annotation","NLP Sandbox ID Annotation","Identify identifiers in clinical notes.","An NLP Sandbox ID annotator takes as input a clinical note and outputs a list of predicted ID annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","intermediate","1","https://doi.org/10.7303/syn22277123","","2021-06-04","2023-09-01","2023-09-08 16:44:22","2023-09-28 23:59:22" +"199","dream-2-bcl6-transcriptomic-target-prediction","DREAM 2 - BCL6 Transcriptomic Target Prediction","","A number of potential transcriptional targets of BCL6, a gene that encodes for a transcription factor active in B cells, have been identified with ChIP-on-chip data and functionally validated by perturbing the BCL6 pathway with CD40 and anti-IgM, and by over-expressing exogenous BCL6 in Ramos cell. We subselected a number of targets found in this way (the gold standard positive set), and added a number decoys (genes that have no evidence of being BCL6 targets, named the gold standard negative set), compiling a list of 200 genes in total. Given this list of 200 genes, the challenge consists of identifying which ones are the true targets and which ones are the decoys, using an independent panel of gene topic_3170.","","https://www.synapse.org/#!Synapse:syn3034857/wiki/","completed","intermediate","1","https://doi.org/10.1073/pnas.0437996100","","2007-04-19","\N","2023-09-12 21:26:22","2023-10-12 17:53:55" +"200","dream-2-protein-protein-interaction-network-inference","DREAM 2 - Protein-Protein Interaction Network Inference","Predict a PPI network of 47 proteins","For many pairs of bait and prey genes, yeast protein-protein interactions were tested in an unbiased fashion using a high saturation, high-stringency variant of the yeast two-hybrid (Y2H) method. A high confidence subset of gene pairs that were found to interact in at least three repetitions of the experiment but that hadn’t been reported in the literature was extracted. There were 47 yeast genes involved in these pairs. Including self interactions, there are a total of 47*48/2 possible pairs of genes that can be formed with these 47 genes. As mentioned above some of these gene pairs were seen to consistently interact in at least three repetitions of the Y2H experiments-these gene pairs form the gold standard positive set. A second set among these gene pairs were seen never to interact in repeated experiments and were not reported as interacting in the literature; we call this the gold standard negative set. Finally in a third set of gene pairs, which we shall call the undecided s...","","https://www.synapse.org/#!Synapse:syn2825374/wiki/","completed","intermediate","1","https://doi.org/10.1126/science.1158684","","2007-05-24","\N","2023-09-12 21:26:28","2023-10-12 17:54:00" +"201","dream-2-genome-scale-network-inference","DREAM 2 - Genome-Scale Network Inference","","A panel of single-channel microarrays was collected for a particular microorganism, including some already published and some in-print data. The data was appropriately normalized (to the logarithmic scale). The challenge consists of reconstructing a genome-scale transcriptional network for this organism. The accuracy of network inference will be judged using chromatin precipitation and otherwise experimentally verified Transcription Factor (TF)-target interactions.","","https://www.synapse.org/#!Synapse:syn3034894/wiki/74418","completed","intermediate","1","https://doi.org/10.1371/journal.pbio.0050008","","2007-06-05","2007-10-31","2023-09-12 21:26:34","2023-10-12 17:54:03" +"202","dream-2-synthetic-five-gene-network-inference","DREAM 2 - Synthetic Five-Gene Network Inference","","A synthetic-biology network consisting of 5 interacting genes was created and transfected to an in-vivo model organism. The challenge consists of predicting the connectivity of the five-gene network from in-vivo measurements.","","https://www.synapse.org/#!Synapse:syn3034869/wiki/74411","completed","intermediate","1","https://doi.org/10.1016/j.cell.2009.01.055","","2007-06-20","2007-10-31","2023-09-12 21:26:56","2023-10-12 17:54:05" +"203","dream-3-signaling-cascade-identification","DREAM 3 - Signaling Cascade Identification","","The concentration of four intracellular proteins or phospho-proteins (X1, X2, X3 and X4) participating in a signaling cascade were measured in about 104 cells by antibody staining and flow cytometry. The idea of this challenge is to explore what key aspects of the dynamics and topology of interactions of a signaling cascade can be inferred from incomplete flow cytometry data.","","https://www.synapse.org/#!Synapse:syn3033068/wiki/74362","completed","intermediate","1","","","2008-06-01","2008-10-31","2023-09-12 21:27:04","2023-10-12 17:54:08" +"204","dream-3-gene-expression-prediction","DREAM 3 - Gene Expression Prediction","","Gene expression time course data is provided for four different strains of yeast (S. Cerevisiae), after perturbation of the cells. The challenge is to predict the rank order of induction/repression of a small subset of genes (the prediction targets) in one of the four strains, given complete data for three of the strains, and data for all genes except the prediction targets in the other strain. You are also allowed to use any information that is in the public domain and are expected to be forthcoming about what information was used.","","https://www.synapse.org/#!Synapse:syn3033083/wiki/74369","completed","intermediate","1","","","2008-06-01","2008-10-31","2023-09-12 21:27:12","2023-10-12 17:54:10" +"205","dream-4-predictive-signaling-network-modelling","DREAM 4 - Predictive Signaling Network Modelling","Cell-type specific high-throughput experimental data","This challenge explores the extent to which our current knowledge of signaling pathways, collected from a variety of cell types, agrees with cell-type specific high-throughput experimental data. Specifically, we ask the challenge participants to create a cell-type specific model of signal transduction using the measured activity levels of signaling proteins in HepG2 cell lines. The model, which can leverage prior information encoded in a generic signaling pathway provided in the challenge, should be biologically interpretable as a network, and capable of predicting the outcome of new experiments.","","https://www.synapse.org/#!Synapse:syn2825304/wiki/71129","completed","intermediate","1","","","2009-03-09","\N","2023-09-12 21:27:14","2023-10-12 17:54:30" +"206","dream-3-signaling-response-prediction","DREAM 3 - Signaling Response Prediction","Predict missing protein concentrations from a large corpus of measurements","Approximately 10,000 intracellular measurements (fluorescence signals proportional to the concentrations of phosphorylated proteins) and extracellular measurements (concentrations of cytokines released in response to cell stimulation) were acquired in human normal hepatocytes and the hepatocellular carcinoma cell line HepG2 cells. The datasets consist of measurements of 17 phospho-proteins (at 0 min, 30 min, and 3 hrs) and 20 cytokines (at 0 min, 3 hrs, and 24 hrs) in two cell types (normal and cancer) after perturbations to the pathway induced by the combinatorial treatment of 7 stimuli and 7 selective inhibitors.","","https://www.synapse.org/#!Synapse:syn2825325/wiki/","completed","intermediate","1","https://doi.org/10.1126%2Fscisignal.2002212","","2009-03-09","\N","2023-09-12 21:27:20","2023-10-12 17:54:33" +"207","dream-4-peptide-recognition-domain-prd-specificity-prediction","DREAM 4 - Peptide Recognition Domain (PRD) Specificity Prediction","","Many important protein-protein interactions are mediated by peptide recognition domains (PRD), which bind short linear sequence motifs in other proteins. For example, SH3 domains typically recognize proline-rich motifs, PDZ domains recognize hydrophobic C-terminal tails, and kinases recognize short sequence regions around a phosphorylatable residue (Pawson, 2003). Given the sequence of the domains, the challenge consists of predicting a position weight matrix (PWM) that describes the specificity profile of each of the given domains to their target peptides. Any publicly accessible peptide specificity information available for the domain may be used.","","https://www.synapse.org/#!Synapse:syn2925957/wiki/72976","completed","intermediate","1","","","2009-06-01","2009-10-31","2023-09-12 21:27:35","2023-10-12 17:54:35" +"208","dream-5-transcription-factor-dna-motif-recognition-challenge","DREAM 5 - Transcription-Factor, DNA-Motif Recognition Challenge","","Transcription factors (TFs) control the expression of genes through sequence-specific interactions with genomic DNA. Different TFs bind preferentially to different sequences, with the majority recognizing short (6-12 base), degenerate ‘motifs’. Modeling the sequence specificities of TFs is a central problem in understanding the function and evolution of the genome, because many types of genomic analyses involve scanning for potential TF binding sites. Models of TF binding specificity are also important for understanding the function and evolution of the TFs themselves. The challenge consists of predicting the signal intensities for the remaining TFs.","","https://www.synapse.org/#!Synapse:syn2887863/wiki/72185","completed","intermediate","1","https://doi.org/10.1038/nbt.2486","","2011-06-01","2011-09-30","2023-09-12 21:27:41","2023-10-12 17:54:36" +"209","dream-5-epitope-antibody-recognition-ear-challenge","DREAM 5 - Epitope-Antibody Recognition (EAR) Challenge","Predict the binding specificity of peptide-antibody interactions.","Humoral immune responses are mediated through antibodies. About 1010 to 1012 different antigen binding sites called paratopes are generated by genomic recombination. These antibodies are capable to bind to a variety of structures ranging from small molecules to protein complexes, including any posttranslational modification thereof. When studying protein-antibody interactions, two types of epitopes (the region paratopes interact with) are to be distinguished from each other-i) conformational and ii) linear epitopes. All potential linear epitopes of a protein can be represented by short peptides derived from the primary amino acid sequence. These peptides can be synthesized and arrayed on solid supports, e.g. glass slides (see Lorenz et al., 2009 [1]). By incubating these peptide arrays with antibody mixtures such as human serum or plasma, peptides can be determined that interact with antibodies in a specific fashion.","","https://www.synapse.org/#!Synapse:syn2820433/wiki/71017","completed","intermediate","1","","","2010-06-09","\N","2023-09-12 21:27:44","2023-10-12 17:54:39" +"210","dream-gene-expression-prediction-challenge","DREAM Gene Expression Prediction Challenge","Predict gene expression levels from promoter sequences in eukaryotes","The level by which genes are transcribed is determined in large part by the DNA sequence upstream to the gene, known as the promoter region. Although widely studied, we are still far from a quantitative and predictive understanding of how transcriptional regulation is encoded in gene promoters. One obstacle in the field is obtaining accurate measurements of transcription derived by different promoters. To address this, an experimental system was designed to measure the transcription derived by different promoters, all of which are inserted into the same genomic location upstream to a reporter gene -a yellow florescence protein gene (YFP). The challenge consists of the prediction of the promoter activity given a promoter sequence and a specific experimental condition. To study a set of promoters that share many elements of the regulatory program, and thus are suitable for computational learning, the data pertains to promoters of most of the ribosomal protein genes (RP) of yeast (S....","","https://www.synapse.org/#!Synapse:syn2820426/wiki/71010","completed","intermediate","1","","","2010-07-09","\N","2023-09-12 21:28:00","2023-10-19 23:32:10" +"211","dream-5-systems-genetics-challenge","DREAM 5 - Systems Genetics Challenge","Predict disease phenotypes and infer Gene Networks from Systems Genetics data","The central goal of systems biology is to gain a predictive, system-level understanding of biological networks. This can be done, for example, by inferring causal networks from observations on a perturbed biological system. An ideal experimental design for causal inference is randomized, multifactorial perturbation. The recognition that the genetic variation in a segregating population represents randomized, multifactorial perturbations (Jansen and Nap (2001), Jansen (2003)) gave rise to Systems Genetics (SG), where a segregating or genetically randomized population is genotyped for many DNA variants, and profiled for phenotypes of interest (e.g. disease phenotypes), gene expression, and potentially other ‘omics’ variables (protein expression, metabolomics, DNA methylation, etc.; Figure 1. Figure 1 was taken from Jansen and Nap (2001)). In this challenge we explore the use of Systems Genetics data for elucidating causal network models among genes, i.e. Gene Networks (DREAM5 SYSGEN...","","https://www.synapse.org/#!Synapse:syn2820440/wiki/","completed","intermediate","1","","","2010-07-09","\N","2023-09-12 21:28:10","2023-10-12 17:54:42" +"212","dream-6-estimation-of-model-parameters-challenge","DREAM 6 - Estimation of Model Parameters Challenge","","Given the complete model structures (including expressions for the kinetic rate laws) for three gene regulatory networks, participants are asked to develop and/or apply optimization methods, including the selection of the most informative experiments, to accurately estimate parameters and predict outcomes of perturbations in Systems Biology models.","","https://www.synapse.org/#!Synapse:syn2841366/wiki/71372","completed","intermediate","1","","","2011-06-01","2011-10-31","2023-09-12 21:28:12","2023-10-12 17:54:45" +"213","dream-6-flowcap2-molecular-classification-of-acute-myeloid-leukemia-challenge","DREAM 6 - FlowCAP2 Molecular Classification of Acute Myeloid Leukemia Challenge","The goal of this challenge is to diagnose Acute Myeloid Leukaemia from patie...","Flow cytometry (FCM) has been widely used by immunologists and cancer biologists for more than 30 years as a biomedical research tool to distinguish different cell types in mixed populations, based on the expression of cellular markers. It has also become a widely used diagnostic tool for clinicians to identify abnormal cell populations associated with disease. In the last decade, advances in instrumentation and reagent technologies have enabled simultaneous single-cell measurement of tens of surface and intracellular markers, as well as tens of signaling molecules, positioning FCM to play an even bigger role in medicine and systems biology [1,2]. However, the rapid expansion of FCM applications has outpaced the functionality of traditional analysis tools used to interpret FCM data such that scientists are faced with the daunting prospect of manually identifying interesting cell populations in 20 dimensional data from a collection of millions of cells. For these reasons a reliable...","","https://www.synapse.org/#!Synapse:syn2887788/wiki/72178","completed","intermediate","1","https://doi.org/10.1038/nmeth.2365","","2011-06-01","2011-09-30","2023-09-12 21:28:19","2023-10-12 17:54:47" +"214","dream-6-alternative-splicing-challenge","DREAM 6 - Alternative Splicing Challenge","","The goal of the mRNA-seq alternative splicing challenge is to assess the accuracy of the reconstruction of alternatively spliced mRNA transcripts from Illumina short-read mRNA-seq. Reconstructed transcripts will be scored against Pacific Biosciences long-read mRNA-seq. The ensuing analysis of the transcriptomes from mandrill and rhinoceros fibroblasts and their derived induced pluripotent stem cells (iPSC), as well as the transcriptome for human Embrionic Stem Cells (hESC) is an opportunity to discover novel biology as well as investigate species-bias of different methods.","","https://www.synapse.org/#!Synapse:syn2817724/wiki/","completed","intermediate","1","","","2011-08-09","\N","2023-09-12 21:28:25","2023-10-12 17:54:50" +"215","causalbench-challenge","CausalBench Challenge","A machine learning contest for gene network inference from single-cell pertu...","Mapping gene-gene interactions in cellular systems is a fundamental step in early-stage drug discovery that helps generate hypotheses on what molecular mechanisms may effectively be targeted by potential future medicines. In the CausalBench Challenge, we invite the machine-learning community to advance the state-of-the-art in deriving gene-gene networks from large-scale real-world perturbational single-cell datasets to improve our ability to glean causal insights into disease-relevant biology.","","https://www.gsk.ai/causalbench-challenge/","completed","intermediate","16","https://doi.org/10.48550/arXiv.2308.15395","","2023-03-01","2023-04-21","2023-09-12 21:28:25","2023-10-19 23:32:34" +"216","iclr-computational-geometry-and-topology-challenge-2022","ICLR Computational Geometry & Topology Challenge 2022","","The purpose of this challenge is to foster reproducible research in geometric (deep) learning, by crowdsourcing the open-source implementation of learning algorithms on manifolds. Participants are asked to contribute code for a published/unpublished algorithm, following Scikit-Learn/Geomstats' or pytorch's APIs and computational primitives, benchmark it, and demonstrate its use in real-world scenarios.","","https://github.com/geomstats/challenge-iclr-2022","completed","intermediate","14","","","\N","2022-04-04","2023-09-13 16:54:06","2023-10-19 23:28:44" +"217","iclr-computational-geometry-and-topology-challenge-2021","ICLR Computational Geometry & Topology Challenge 2021","","The purpose of this challenge is to push forward the fields of computational differential geometry and topology, by creating the best data analysis, computational method, or numerical experiment relying on state-of-the-art geometric and topological Python packages.","","https://github.com/geomstats/challenge-iclr-2021","completed","intermediate","14","https://doi.org/10.48550/arXiv.2108.09810","","\N","2021-05-02","2023-09-13 17:02:12","2023-10-19 23:28:44" +"218","genedisco-challenge","GeneDisco Challenge","","The GeneDisco challenge is a machine learning community challenge for evaluating batch active learning algorithms for exploring the vast experimental design space in genetic perturbation experiments. Genetic perturbation experiments, using for example CRISPR technologies to perturb the genome, are a vital component of early-stage drug discovery, including target discovery and target validation. The GeneDisco challenge is organized in conjunction with the Machine Learning for Drug Discovery workshop at ICLR-22.","","https://www.gsk.ai/genedisco-challenge/","completed","intermediate","16","https://doi.org/10.48550/arXiv.2110.11875","","2022-01-31","2022-03-31","2023-09-13 17:20:30","2023-10-19 23:32:43" +"219","hidden-treasures-warm-up","Hidden Treasures: Warm Up","","In the context of human genome sequencing, software pipelines typically involve a wide range of processing elements, including aligning sequencing reads to a reference genome and subsequently identifying variants (differences). One way of assessing the performance of such pipelines is by using well-characterized datasets such as Genome in a Bottle’s NA12878. However, because the existing NGS reference datasets are very limited and have been widely used to train/develop software pipelines, benchmarking of pipeline performance would ideally be done on samples with unknown variants. This challenge will provide a unique opportunity for participants to investigate the accuracy of their pipelines by testing the ability to find in silico injected variants in FASTQ files from exome sequencing of reference cell lines. It will be a warm up for the community ahead of a more difficult in silico challenge to come in the fall. This challenge will provide users with a FASTQ file of a NA12878 se...","","https://precision.fda.gov/challenges/1","completed","intermediate","6","","","2017-07-17","2017-09-13","2023-09-13 23:31:39","2023-10-12 17:55:23" +"220","data-management-and-graph-extraction-for-large-models-in-the-biomedical-space","Data management and graph extraction for large models in the biomedical space","Collaborative hackathon on the topic of data management and graph extraction...","This fall, CMU Libraries is hosting a hackathon in partnership with DNAnexus on the topic of data management and graph extraction for large models in the biomedical space. The hackathon will be held in person at CMU, October 19-21, 2023. The hackathon is a collaborative, rather than competitive, event, with each team working on a dedicated part of the problem. The teams will be focused on the following topics-1) Knowledge graph-based validation for variant (genomic) assertions; 2) Continuous monitoring for RLHF and flexible infrastructure for layering assertions with rollback; 3) Flexible tokenization of complex data types; 4) Assertion tracking in large models; 5) Column headers for data harmonization. The outputs are often published as preprints or on the F1000 hackathon channel. Contact Ben Busby (bbusby@dnanexus.com) with any questions about the hackathon or serving as a team lead.","","https://library.cmu.edu/about/news/2023-08/hackathon-2023","active","intermediate","14","","","2023-10-19","2023-10-21","2023-09-13 23:32:59","2023-09-27 21:08:26" +"221","cagi2-asthma-twins","CAGI2: Asthma discordant monozygotic twins","With the provided whole genome and RNA sequencing data, identify which two i...","The dataset includes whole genomes of 8 pairs of discordant monozygotic twins (randomly numbered from 1 to 16) that is, in each pair identical twins one has asthma and one does not. In addition, RNA sequencing data for each individual is provided. One of the twins in each pair suffers from asthma while the other twin is healthy.","","https://genomeinterpretation.org/CAGI2-asthma-twins.html","completed","intermediate","2","","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 18:11:42" +"222","cagi4-bipolar","CAGI4: Bipolar disorder","With the provided exome data, identify which individuals have BD and which i...","Bipolar disorder (BD) is a serious mental illness characterized by recurrent episodes of manias and depression, which are syndromes of abnormal mood, thinking and behavior. It affects 1.0-4.5% of the population [1], and it is among the major causes of disability worldwide. This challenge involved the prediction of which of a set of individuals have been diagnosed with bipolar disorder, given exome data. 500 of the 1000 exome samples were provided for training.","","https://genomeinterpretation.org/CAGI4-bipolar.html","completed","intermediate","2","","","\N","2016-04-04","2023-09-28 18:19:48","2023-09-28 18:25:17" +"223","cagi3-brca","CAGI3: BRCA1 & BRCA2","For each variant, provide the probability that Myriad Genetics has classifie...","In normal cells, the BRCA1 and BRCA2 genes are involved in homologous recombination for double strand break repair and ensure the stability of a cell's genetic material. Mutations in these genes have been linked to development of breast and ovarian cancer. Myriad Genetics created the BRACAnalysis test in order to assess a woman’s risk of developing hereditary breast or ovarian cancer based on detection of mutations in the BRCA1 and BRCA2 genes. This test has become the standard of care in identification of individuals with hereditary breast and ovarian cancer (HBOC) syndrome. It is based on proprietary methods.","","https://genomeinterpretation.org/CAGI3-brca.html","completed","intermediate","2","","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-19 23:32:48" +"224","cagi2-breast-cancer-pkg","CAGI2: Breast cancer pharmacogenomics","Cancer tissues are specifically responsive to different drugs. For this expe...","Cell-cycle-checkpoint kinase 2 (CHEK2; OMIM #604373) is a protein that plays an important role in the maintenance of genome integrity and in the regulation of the G2/M cell cycle checkpoint. CHEK2 has been shown to interact with other proteins involved in DNA repair processes such as BRCA1 and TP53. These findings render CHEK2 an 23 attractive candidate susceptibility gene for a variety of cancers.","","https://genomeinterpretation.org/CAGI2-breast-cancer-pkg.html","completed","intermediate","2","","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 17:46:22" +"225","cagi4-2eqtl","CAGI4: eQTL causal SNPs","Participants are asked to submit predictions of the regulatory sequences tha...","Identifying the causal alleles responsible for variation in expression of human genes has been particularly difficult. This is an important problem, as genome-wide association studies (GWAS) suggest that much of the variation underlying common traits and diseases maps within regions of the genome that do not encode protein. A massively parallel reporter assay (MPRA) has been applied to thousands of single nucleotide polymorphisms (SNPs) and small insertion/deletion polymorphisms in linkage disequilibrium (LD) with cis-expression quantitative trait loci (eQTLs). The results identify variants showing differential expression between alleles. The challenge is to identify the regulatory sequences and the expression-modulating variants (emVars) underlying each eQTL and estimate their effects in the assay.","","https://genomeinterpretation.org/CAGI4-2eqtl.html","completed","intermediate","2","","","\N","2016-04-04","2023-09-28 18:19:48","2023-09-29 3-58-33" +"226","cagi1-cbs","CAGI1: CBS","Participants were asked to submit predictions for the effect of the variants...","CBS is a vitamin-dependent enzyme involved in cysteine biosynthesis. The human CBS requires two cofactors for function, vitamin B6 and heme. Homocystinuria due to CBS deficiency (OMIM #236200) is a recessive inborn error of sulfur amino acid metabolism.","","https://genomeinterpretation.org/CAGI1-cbs.html","completed","intermediate","2","","","\N","2010-12-10","2023-09-28 18:19:48","2023-10-12 17:46:07" +"227","cagi2-cbs","CAGI2: CBS","Participants were asked to submit predictions for the effect of the variants...","CBS is a vitamin-dependent enzyme involved in cysteine biosynthesis. The human CBS requires two cofactors for function, vitamin B6 and heme. Homocystinuria due to CBS deficiency (OMIM #236200) is a recessive inborn error of sulfur amino acid metabolism.","","https://genomeinterpretation.org/CAGI2-cbs.html","completed","intermediate","2","","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 17:45:56" +"228","cagi1-chek2","CAGI1: CHEK2","Variants in the ATM & CHEK2 genes are associated with breast cancer.","Predictors will be provided with 41 rare missense, nonsense, splicing, and indel variants in CHEK2.","","https://genomeinterpretation.org/CAGI1-chek2.html","completed","intermediate","2","","","\N","2010-12-10","2023-09-28 18:19:48","2023-10-19 23:32:57" +"229","cagi3-fch","CAGI3: FCH","The challenge involved exome sequencing data for 5 subjects in an FCH family...","Familial combined hyperlipidemia (FCH; OMIM 14380) the most prevalent hyperlipidemia, is a complex metabolic disorder characterized by variable occurrence of elevated low-density lipoprotein cholesterol (LDL-C) level and high triglycerides (TG)—a condition that is commonly associated with coronary artery disease (CAD).","","https://genomeinterpretation.org/CAGI3-fch.html","completed","intermediate","2","","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-12 17:45:43" +"230","cagi3-ha","CAGI3: HA","The dataset for this challenge comprises of exome sequencing data for 4 subj...","Hypoalphalipoproteinemia (HA; OMIM #604091) is characterized by severely decreased serum high-density lipoprotein cholesterol (HDL-C) levels and low apolipoprotein A1 (APOA1). Low HDL-C is a risk factor for coronary artery disease.","","https://genomeinterpretation.org/CAGI3-ha.html","completed","intermediate","2","","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-12 17:45:41" +"231","cagi2-croshn-s","CAGI2: Crohn's disease","With the provided exome data, identify which individuals have Crohn's diseas...","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/CAGI2-croshn-s.html","completed","intermediate","2","","","\N","2011-10-06","2023-09-28 18:19:48","2023-09-27 21:09:04" +"232","cagi3-crohn-s","CAGI3: Crohn's disease","With the provided exome data, identify which individuals have Crohn's diseas...","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/CAGI3-crohn-s.html","completed","intermediate","2","","","\N","2013-04-25","2023-09-28 18:19:48","2023-09-28 18:25:20" +"233","cagi4-chron-s-exome","CAGI4: Crohn's exomes","With the provided exome data, identify which individuals have Crohn's diseas...","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/cagi4-chron-s-exome.html","completed","intermediate","2","","","\N","2016-04-04","2023-09-28 18:19:48","2023-10-16 18:29:11" +"234","cagi4-hopkins","CAGI4: Hopkins clinical panel","Participants were tasked with identifying the disease class for each of 106 ...","The Johns Hopkins challenge, provided by the Johns Hopkins DNA Diagnostic Laboratory (http://www.hopkinsmedicine.org/dnadiagnostic), comprised of exonic sequence for 83 genes associated with one of 14 disease classes, including 5 decoys","","https://genomeinterpretation.org/CAGI4-hopkins.html","completed","intermediate","2","","","\N","2016-04-04","2023-09-28 18:19:48","2023-10-12 17:45:27" +"235","cagi2-mouse-exomes","CAGI2: Mouse exomes","The challenge involved identifying the causative variants leading to one of ...","Predictors were given SNVs and indels found from exome sequencing. Causative variants had been identified for the L11Jus74 and Sofa phenotypes by the use of traditional breeding crosses,47 and the predictions were compared to these results, which were unpublished at the time of the CAGI submissions. The L11Jus74 phenotype is caused by two SNVs (chr11-102258914A> and chr11-77984176A>T), whereas a 15-nucleotide deletion in the Pfas gene is responsible for the Sofa phenotype. The predictions for Frg and Stn phenotypes could not be compared to experimental data, as the causative variants could not successfully be mapped by linkage","","https://genomeinterpretation.org/CAGI2-mouse-exomes.html","completed","intermediate","2","","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 17:45:19" +"236","cagi3-mrn-mre11","CAGI3: MRE11","Genomes are subject to constant threat by damaging agents that generate DNA ...","Predict probability of pathogenicity (a number between 0 and 1) for individual rare variants of MRE11 and NBS1.","","https://genomeinterpretation.org/CAGI3-mrn.html","completed","intermediate","2","","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-16 18:38:44" +"237","cagi4-naglu","CAGI4: NAGLU","Participants are asked to submit predictions on the effect of the variants o...","NAGLU is a lysosomal glycohydrolyase. Deficiency of NAGLU causes the rare disorder Mucopolysaccharidosis IIIB or Sanfilippo B disease. Naturally occurring NAGLU mutants have been assayed for enzymatic activity in transfected cell lysates. The challenge is to predict the fractional activity of each mutant protein compared to the wild-type enzyme.","","https://genomeinterpretation.org/CAGI4-naglu.html","completed","intermediate","2","","","\N","2016-04-04","2023-09-28 18:19:48","2023-10-20 23:28:52" +"238","cagi4-npm-alk","CAGI4: NPM: ALK","Participants are asked to submit predictions of both the kinase activity and...","NPM-ALK is a fusion protein in which the ALK tyrosine kinase is constitutively activated, contributing to cancer. NPM-ALK constructs with mutations in the kinase domain have been assayed in extracts of transfected cells. The challenge is to predict the kinase activity and the Hsp90 binding affinity of the mutant proteins relative to the reference NPM-ALK fusion protein.","","https://genomeinterpretation.org/CAGI4-npm-alk.html","completed","intermediate","2","","","\N","2016-04-04","2023-09-28 18:19:48","2023-10-20 23:28:53" +"239","cagi3-mrn-nbs1","CAGI3: NBS1","Genomes are subject to constant threat by damaging agents that generate DNA ...","Predict probability of pathogenicity (a number between 0 and 1) for individual rare variants of MRE11 and NBS1.","","https://genomeinterpretation.org/CAGI3-mrn.html","completed","intermediate","2","","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-16 18:38:55" +"240","cagi3-p16","CAGI3: p16","CDKN2A is the most common, high penetrance, susceptibility gene identified t...","Evaluate how different variants of p16 protein impact its ability to block cell proliferation.","","https://genomeinterpretation.org/CAGI3-p16.html","completed","intermediate","2","","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-20 23:28:57" +"241","cagi2-p53","CAGI2: p53 reactivation","Predictors are asked to submit predictions on the effect of the cancer rescu...","The transcription factor p53 is a central tumor suppressor protein that controls DNA repair, cell cycle arrest, and apoptosis (programmed cell death). About half of human cancers have p53 mutations that inactivate p53. Over 250,000 US deaths yearly are due to tumors that express full-length p53 that has been inactivated by a single point mutation. For the past several years, the group of Rick Lathrop at University of California, Irvine, has been engaged in a complete functional census of p53 second-site suppressor (“cancer rescue”) mutations. These cancer rescue mutations are additional amino acids changes (to otherwise cancerous p53 mutations), which have been found to rescue p53 tumor suppressor function, reactivating otherwise inactive p53. These intragenic rescue mutations reactivate cancer mutant p53 in yeast and human cell assays by providing structural changes that compensate for the cancer mutation.","","https://genomeinterpretation.org/CAGI2-p53.html","completed","intermediate","2","","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-20 23:28:58" +"242","cagi1-pgp","CAGI1: PGP","PGP challenge requires matching of full genome sequences to extensive phenot...","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/CAGI1-pgp.html","completed","intermediate","2","","","\N","2010-12-10","2023-09-28 18:19:48","2023-09-27 21:05:22" +"243","cagi2-pgp","CAGI2: PGP","PGP challenge requires matching of full genome sequences to extensive phenot...","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/CAGI2-pgp.html","completed","intermediate","2","","","\N","2011-10-06","2023-09-28 18:19:48","2023-09-27 21:05:23" +"244","cagi3-pgp","CAGI3: PGP","PGP challenge requires matching of full genome sequences to extensive phenot...","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/CAGI3-pgp.html","completed","intermediate","2","","","\N","2013-04-25","2023-09-28 18:19:48","2023-09-27 21:05:23" +"245","cagi4-pgp","CAGI4: PGP","PGP challenge requires matching of full genome sequences to extensive phenot...","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/CAGI4-pgp.html","completed","intermediate","2","","","\N","2016-04-04","2023-09-28 18:19:48","2023-09-27 21:05:24" +"246","cagi4-pyruvate-kinase","CAGI4: Pyruvate kinase","Participants are asked to submit predictions on the effect of the mutations ...","Pyruvate kinase catalyzes the last step in glycolysis and is regulated by allosteric effectors. Variants in the gene encoding the isozymes expressed in red blood cells and liver, including missense variants mapping near the effector binding sites, cause PK deficiency. A large set of single amino acid mutations in the liver enzyme has been assayed in E. coli extracts for the effect on allosteric regulation of enzyme activity. The challenge is to predict the impacts of mutations on enzyme activity and allosteric regulation.","","https://genomeinterpretation.org/CAGI4-pyruvate-kinase.html","completed","intermediate","2","","","\N","2015-01-11","2023-09-28 18:19:48","2023-09-29 22:06:22" +"247","cagi2-rad50","CAGI2: RAD50","Predict the probability of the variant occurring in a case individual.","RAD50 is a candidate intermediate-risk breast cancer susceptibility gene. The RAD50 data provided for CAGI challenge include a list of potentially interesting sequence variants observed from sequencing RAD50 gene in about 1,400 breast cancer cases and 1,200 ethnically matched controls. Variants in the list were observed between 1 and 20 times.","","https://genomeinterpretation.org/CAGI2-rad50.html","completed","intermediate","2","","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-19 23:33:11" +"248","cagi2-risksnps","CAGI2: riskSNPs","The goal of these challenges is to investigate the community’s ability to id...","The goal of this experiment is to explore current understanding of the molecular level mechanisms underlying associations between SNPs and disease risk, incorporating expertise in each of the known mechanism areas, and as far as possible assigning possible mechanisms for each association locus. The correct mechanisms are unknown, so there can be no ranking of accuracy-that is not the point of the experiment. Rather, the goal is to ascertain which mechanisms appear most relevant, how confidently they can be assigned, and what fraction of loci can currently be assigned plausible mechanisms.","","https://genomeinterpretation.org/CAGI2-risksnps.html","completed","intermediate","2","","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-19 23:33:11" +"249","cagi3-risksnps","CAGI3: riskSNPs","The goal of these challenges is to investigate the community’s ability to id...","The goal of this experiment is to explore current understanding of the molecular level mechanisms underlying associations between SNPs and disease risk, incorporating expertise in each of the known mechanism areas, and as far as possible assigning possible mechanisms for each association locus. The correct mechanisms are unknown, so there can be no ranking of accuracy-that is not the point of the experiment. Rather, the goal is to ascertain which mechanisms appear most relevant, how confidently they can be assigned, and what fraction of loci can currently be assigned plausible mechanisms.","","https://genomeinterpretation.org/CAGI3-risksnps.html","completed","intermediate","2","","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-19 23:33:13" +"250","cagi2-nav1-5","CAGI2: SCN5A","Predictors are asked to submit predictions on the effect of the mutants on t...","The cardiac action potential (AP) is the sum of a number of distinct ionic currents. It can be divided into five phases (phase 0‐4). From pacemaker cells of the SA node the initial depolarizing wave front will spread throughout the cardiomyocytes via gap junctions. If the depolarization is sufficient voltage‐dependent sodium channels (Nav1.5) are activated and allow Na+ influx. This results in a further depolarization of the membrane which will lead to opening of even more Nav channels. This positive feedback mechanism is seen as the rapid upstroke in the initial phase (phase 0) of the action potential. Nav1.5 is encoded by SCN5A and mutations in this gene have been associated with various diseases such as Atrial fibrillation, Long QT syndrome, Cardiac Conduction Defect, Sick Sinus Disease, and Brugada Syndrome (BrS).","","https://genomeinterpretation.org/CAGI2-nav1.5.html","completed","intermediate","2","","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-16 18:32:16" +"251","cagi2-mr-1","CAGI2: Shewanella oneidensis strain MR-1","Shewanella oneidensis strain MR-1 (formerly known as S. putrefaciens) is a m...","Predictors are asked to submit predictions on how insertions in the given gene of MR-1 affect the fitness of that gene in a given condition (stressor).","","https://genomeinterpretation.org/CAGI2-mr-1.html","completed","intermediate","2","","","\N","2011-10-06","2023-09-28 18:19:55","2023-10-16 18:32:21" +"252","cagi3-mr-1","CAGI3: Shewanella oneidensis strain MR-1","Shewanella oneidensis strain MR-1 (formerly known as S. putrefaciens) is a m...","Predictors are asked to submit predictions on how insertions in the given gene of MR-1 affect the fitness of that gene in a given condition (stressor).","","https://genomeinterpretation.org/CAGI3-mr-1.html","completed","intermediate","2","","","\N","2013-04-25","2023-09-28 18:20:01","2023-10-16 18:18:07" +"253","cagi4-sickkids","CAGI4: SickKids","The challenge presented here is to use computational methods to match each g...","Realizing the promise of precision medicine will require developing methods for interpreting genome sequence data to infer individuals’ phenotypic traits and predispositions to disease. This challenge involves 25 children with suspected genetic disorders who were referred for clinical genome sequencing. Predictors are given their genome sequences and their clinical phenotypic descriptions, as provided to the diagnostic laboratory, and asked to predict which genome corresponds to which clinical description. Additionally, identify the diagnostic variants underlying the predictions. Optionally, identify predictive secondary variants conferring high risk of other diseases whose phenotypes are not reported in the clinical descriptions.","","https://genomeinterpretation.org/CAGI4-sickkids.html","completed","intermediate","2","","","\N","2016-04-04","2023-09-28 18:19:48","2023-10-06 20:48:13" +"254","cagi4-sumo-ligase","CAGI4: SUMO ligase","Participants are asked to submit predictions of the effect of the variants o...","SUMO ligase identifies target proteins and covalently attaches SUMO to them, thereby modulating the functions of hundreds of proteins including proteins implicated in cancer, neurodegeneration, and other diseases. A large library of missense mutations in human SUMO ligase has been assessed for competitive growth in a high-throughput yeast-based complementation assay. The challenge is to predict the effect of mutations on function, as measured by the change in fractional representation of each mutant SUMO ligase clone, relative to wild-type clones, in a competitive yeast growth assay.","","https://genomeinterpretation.org/CAGI4-sumo-ligase.html","completed","intermediate","2","","","\N","2016-04-04","2023-09-28 18:19:48","2023-10-19 23:31:57" +"255","cagi3-splicing","CAGI3: TP53 splicing","With the provided data determine which disease-causing mutations in the TP53...","The function of exonic splicing regulatory elements can be undermined by DNA sequence variation and in some cases can contribute to pathogenesis. Thousands of disease-causing mutations disrupt exonic splicing regulatory elements. These data suggest that >25 percent of missense mutations may impact pre-mRNA splicing rather than mRNA translation. Using minigene constructs derived from a fragment of the TP53 gene, we have experimentally determined if each mutation influences splicing fidelity in HEK293T cells. We hope that CAGI participants will be able to predict the outcome of our experiments. A long-term goal will be the computational prioritization of disease-causing mutations prior to experimental validation. This contribution is expected to have major impacts in understanding the pathogenic basis of disease-causing mutations.","","https://genomeinterpretation.org/CAGI3-splicing.html","completed","intermediate","2","","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-10 19:48:10" +"256","cagi4-warfarin","CAGI4: Warfarin exomes","With the provided exome data and clinical covariates, predict the therapeuti...","With over 33 million prescriptions in 2011, warfarin is the most commonly used anticoagulant for preventing thromboembolic events. Warfarin has a twenty-fold inter-individual dose variability and a narrow therapeutic index, and it is responsible for a third of adverse drug event hospitalizations in older Americans [2]. Alternatives to warfarin, such as direct thrombin inhibitors and factor Xa inhibitors, are now available. However, these are more expensive, irreversible, and may cause a higher rate of acute coronary events compared to warfarin [3,4]. Thus, warfarin remains a mainstay of anticoagulant therapy, and better methods of dosing warfarin will lead to fewer adverse events due to overcoagulation.","","https://genomeinterpretation.org/CAGI4-warfarin.html","completed","intermediate","2","","","\N","2016-04-04","2023-09-28 18:19:48","2023-09-28 21:19:03" +"257","cagi6-calmodulin","CAGI6: Calmodulin","participants were asked to submit predictions for the competitive growth sco...","Calmodulin (CaM) is a ubiquitous calcium (Ca2+) sensor protein interacting with more than 200 molecular partners, thereby regulating a variety of biological processes. Missense point mutations in the genes encoding CaM have been associated with ventricular tachycardia and sudden cardiac death. A library encompassing up to 17 point mutations was assessed by far-UV circular dichroism (CD) by measuring melting temperature (Tm) and percentage of unfolding (%unfold) upon thermal denaturation at pH and salt concentration that mimic the physiological conditions. The challenge is to predict- the Tm and %unfold values for isolated CaM variants under Ca2+-saturating conditions (Ca2+-CaM) and in the Ca2+-free (apo) state; whether the point mutation stabilizes or destabilizes the protein (based on Tm and %unfold).","","https://genomeinterpretation.org/CAGI6-cam.html","completed","intermediate","1","","","\N","2021-12-31","2023-09-28 18:19:48","2023-10-19 23:33:19" +"258","cagi2-splicing","CAGI2: splicing","Predictors are asked to compare exons from wild type and disease-associated ...","Accurate precursor mRNA (pre-mRNA) splicing is required for the expression of protein coding genes from the human genome. In this process, intervening sequences (introns) are removed from pre-mRNA and coding/regulatory sequences (exons) are ligated together generating a mature mRNA. A large ribonucleoprotein machine called the spliceosome assembles de novo upon every nascent intron and catalyzes the chemical steps of splicing.","","https://genomeinterpretation.org/CAGI2-splicing.html","completed","intermediate","2","","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-18 15:32:55" +"259","cagi6-arsa","CAGI6: ARSA","Predicting the effect of naturally occurring missense mutations on enzymatic...","Metachromatic Leukodystrophy (MLD) is an autosomal recessive, lysosomal-storage disease caused by mutations in Arylsulfatase A (ARSA) and toxic accumulation of sulfatide substrate. Genome sequencing has revealed hundreds of protein-altering, ARSA missense variants, but the functional effect of most variants remains unknown. ARSA enzyme activity using a high-throughput cellular assay was measured for a large set of variants of known significance and variants of unknown significance. The challenge is to predict the fractional enzyme activity of each mutant protein compared to the wildtype protein.","","https://genomeinterpretation.org/CAGI6-lc-arsa.html","completed","intermediate","1","","","\N","2022-11-16","2023-09-28 18:20:23","2023-10-12 18:11:51" +"260","predict-hits-for-the-wdr-domain-of-lrrk2","CACHE1: PREDICT HITS FOR THE WDR DOMAIN OF LRRK2","Finding ligands targeting the central cavity of the WD-40 repeat (WDR) domai...","The first CACHE Challenge target is LRRK2, the most commonly mutated gene in familial Parkinson's Disease. Participants are asked to find hits for the WD40 repeat (WDR) domain of LRRK2. Read more under Details below.","","https://cache-challenge.org/challenges/predict-hits-for-the-wdr-domain-of-lrrk2","completed","intermediate","17","","","2021-12-01","2022-01-31","2023-09-27 19:01:55","2023-10-16 19:03:47" +"261","finding-ligands-targeting-the-conserved-rna-binding-site-of-sars-cov-2-nsp13","CACHE2: FINDING LIGANDS TARGETING THE CONSERVED RNA BINDING SITE OF SARS-CoV-2 NSP13","Finding ligands targeting the conserved RNA binding site of SARS-CoV-2 NSP13.","Predicted compounds will be procured and tested at CACHE using both enzymatic and binding assays","","https://cache-challenge.org/challenges/finding-ligands-targeting-the-conserved-rna-binding-site-of-sars-cov-2-nsp13","completed","intermediate","17","","","2022-06-22","2022-09-04","2023-09-27 19:02:43","2023-10-16 19:01:17" +"262","finding-ligands-targeting-the-macrodomain-of-sars-cov-2-nsp3","CACHE3: Finding ligands targeting the macrodomain of SARS-CoV-2 Nsp3","Severe acute respiratory syndrome coronavirus 2","To predict ligands that bind to the ADPr site of SARS-CoV-2 Nsp3 macrodomain (Mac1).","","https://cache-challenge.org/challenges/finding-ligands-targeting-the-macrodomain-of-sars-cov-2-nsp3","completed","intermediate","17","","","2022-11-02","2023-01-01","2023-09-27 19:03:13","2023-10-16 19:01:19" +"263","finding-ligands-targeting-the-tkb-domain-of-cblb","CACHE4: Finding ligands targeting the TKB domain of CBLB","Several cancers (PMID-33306199), potential immunotherapy (PMID-24875217), in...","Predict compounds that bind to the closed conformation of the CBLB TKB domain with novel chemical templates and KD below 30 micromolar.","","https://cache-challenge.org/challenges/finding-ligands-targeting-the-tkb-domain-of-cblb","completed","intermediate","17","","","2023-03-09","2023-05-09","2023-09-27 19:03:14","2023-10-16 19:01:22" +"264","jan2024-rare-disease-ai-hackathon","Jan2024: Rare Disease AI Hackathon","Researchers and medical experts are invited to collaborate on our patient ca...","Bring AI and medical experts together to build open source models for rare diseases. Create zero-barrier access to rare disease expertise for patients, researchers and physicians. Use AI to Uncover novel links between rare diseases. Establish validation methods for medical AI models. Jumpstart an open source community for rare disease AI models. Launch models for Beta testing on Hypophosphatasia.ai and EhlersDanlos.ai.","","https://www.rarediseaseaihackathon.org/","active","intermediate","14","","","2023-09-30","2024-01-15","2023-09-27 19:10:40","2023-10-12 18:13:38" +"265","cometh-benchmark","COMETH Benchmark","Quantify tumor heterogeneity-how many cell types are present and in which pr...","Successful treatment of cancer is still a challenge and this is partly due to a wide heterogeneity of cancer composition across patient population. Unfortunately, accounting for such heterogeneity is very difficult. Clinical evaluation of tumor heterogeneity often requires the expertise of anatomical pathologists and radiologists.This benchmark is dedicated to the quantification of intra-tumor heterogeneity using appropriate statistical methods on cancer omics data.In particular, it focuses on estimating cell types and proportion in biological samples based on methylation and methylome data sets. The goal is to explore various statistical methods for source separation/deconvolution analysis (Non-negative Matrix Factorization, Surrogate Variable Analysis, Principal component Analysis, Latent Factor Models, ...) using both RNA-seq and methylome data.","","https://www.codabench.org/competitions/218/","completed","intermediate","10","","","2020-06-14","2020-12-29","2023-09-28 23:25:52","2023-10-10 19:47:14" +"266","the-miccai-2014-machine-learning-challenge","The MICCAI 2014 Machine Learning Challenge","Predicting Binary and Continuous Phenotypes from Structural Brain MRI Data","Machine learning tools have been increasingly applied to structural brain magnetic resonance imaging (MRI) scans, largely for developing models to predict clinical phenotypes at the individual level. Despite significant methodological developments and novel application domains, there has been little effort to conduct benchmark studies with standardized datasets, which researchers can use to validate new tools, and more importantly conduct an objective comparison with state-of-the-art algorithms. The MICCAI 2014 Machine Learning Challenge (MLC) will take a significant step in this direction, where we will employ four separate, carefully compiled, and curated large-scale (each N > 70) structural brain MRI datasets with accompanying clinically relevant phenotypes. Our goal is to provide a snapshot of the current state of the art in the field of neuroimage-based prediction, and attract machine-learning practitioners to the MICCAI community and the field of medical image computing in g...","","https://competitions.codalab.org/competitions/1471","completed","intermediate","9","","","2014-04-16","2014-06-14","2023-09-28 23:36:12","2023-10-19 23:31:50" +"267","cagi6-annotate-all-missense","CAGI6: Annotate All Missense","Predictors are asked to predict the functional effect predict each coding SNV.","dbNSFP currently describes 81,782,923 possible protein-altering variants in the human genome. The challenge is to predict the functional effect of every such variant. For the vast majority of these missense and nonsense variants, the functional impact is not currently known, but experimental and clinical evidence is accruing rapidly. Rather than drawing upon a single discrete dataset as typical with CAGI, predictions will be assessed by comparing with experimental or clinical annotations made available after the prediction submission date, on an ongoing basis. If predictors assent, predictions will also be incorporated into dbNSFP.","","https://genomeinterpretation.org/CAGI6-annotate-all-missense.html","completed","intermediate","1","","","2021-06-01","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:13:42" +"268","cagi6-hmbs","CAGI6: HMBS","Participants are asked to submit predictions of the fitness score for each o...","Hydroxymethylbilane synthase (HMBS), also known as porphobilinogen deaminase (PBGD) or uroporphyrinogen I synthase, is an enzyme involved in heme production. In humans, variants that affect HMBS function result in acute intermittent porphyria (AIP), an autosomal dominant genetic disorder caused by a build-up of porphobilinogen in the cytoplasm. A large library of HMBS missense variants was assessed with respect to their effects on protein function using a high-throughput yeast complementation assay. The challenge is to predict the functional effects of these variants.","","https://genomeinterpretation.org/CAGI6-hmbs.html","completed","intermediate","1","","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:05" +"269","cagi6-intellectual-disability-panel","CAGI6: Intellectual Disability Panel","In this challenge predictors are asked to analyze the sequence data for the ...","The objective in this challenge is to predict a patient's clinical phenotype and the causal variant(s) based on their gene panel sequences. Sequence data for 74 genes from a cohort of 500 patients with a range of neurodevelopmental presentations (intellectual disability, autistic spectrum disorder, epilepsy, microcephaly, macrocephaly, hypotonia, ataxia) has been made available for this challenge. Additional data from 150 patients from the same clinical study is available for training and validation.","","https://genomeinterpretation.org/CAGI6-id-panel.html","completed","intermediate","1","","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:09" +"270","cagi6-mapk1","CAGI6: MAPK1","For each variant, participants are asked to predict the ΔΔGH20 value for the...","MAPK1 (ERK2) is active as serine/threonine kinase in the Ras-Raf-MEK-ERK signal transduction cascade that regulates cell proliferation, transcription, differentiation, and cell cycle progression. MAPK1 is activated by phosphorylation which occurs with strict specificity by MEK1/2 on Thr185 and Tyr187, and may also act as a transcriptional repressor independent of its kinase activity. A library of eleven missense variants selected from the COSMIC database was assessed by near and far-UV circular dichroism and intrinsic fluorescence spectra to determine thermodynamic stability at different concentrations of denaturant. These data were used to calculate a ΔΔGH20 value; i.e., the difference in unfolding free energy ΔGH20 between each variant and the wildtype protein, both in phosphorylated and unphosphorylated forms. The challenge is to predict these two ΔΔGH20 values and the catalytic efficiency (kcat/km)mut/(kcat/km)wt, as determined by a fluorescence assay, of the phosphorylated fo...","","https://genomeinterpretation.org/CAGI6-mapk1.html","completed","intermediate","1","","","2021-07-08","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:13" +"271","cagi6-mapk3","CAGI6: MAPK3","For each variant, participants are asked to predict the ΔΔGH20 value for the...","MAPK3 (ERK1) is active as serine/threonine kinase in the Ras-Raf-MEK-ERK signal transduction cascade that regulates cell proliferation, transcription, differentiation, and cell cycle progression. MAPK3 is activated by phosphorylation which occurs with strict specificity by MEK1/2 on Thr202 and Tyr204, and may also act as a transcriptional repressor independent of its kinase activity. A library of twelve missense variants selected from the COSMIC database was assessed by near and far-UV circular dichroism and intrinsic fluorescence spectra to determine thermodynamic stability at different concentrations of denaturant. These data were used to calculate a ΔΔGH20 value; i.e., the difference in unfolding free energy ΔGH20 between each variant and the wildtype protein, both in phosphorylated and unphosphorylated forms. The challenge is to predict these two ΔΔGH20 values and the catalytic efficiency (kcat/km)mut/(kcat/km)wt, as determined by a fluorescence assay, of the phosphorylated fo...","","https://genomeinterpretation.org/CAGI6-mapk3.html","completed","intermediate","1","","","2021-08-04","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:15" +"272","cagi6-mthfr","CAGI6: MTHFR","Participants are asked to submit predictions of the fitness score for each m...","Methylenetetrahydrofolate reductase (MTHFR) catalyzes the production of 5-methyltetrahydrofolate, which is needed for conversion of homocysteine to methionine. Humans with variants affecting MTHFR function present with a wide range of phenotypes, including homocystinuria, homocysteinemia, developmental delay, severe mental retardation, psychiatric disturbances, and late-onset neurodegenerative disorders. A further complication to interpretation of variants in this gene is a common variant, Ala222Val, carried by a large fraction of the human population. A large library of MTHFR missense variants was assessed with respect to their effects on protein function using a high-throughput yeast complementation assay. The challenge is to predict the functional effects of these variants in two different settings- for the wildtype protein, and for the protein with the common Ala222Val variant.","","https://genomeinterpretation.org/CAGI6-mthfr.html","completed","intermediate","1","","","2021-05-03","2021-06-30","2023-06-23 00:00:00","2023-10-12 18:12:18" +"273","cagi6-polygenic-risk-scores","CAGI6: Polygenic Risk Scores","Participants will be expected to provide a fully trained prediction model th...","Polygenic risk scores (PRS) have potential clinical utility for risk surveillance, prevention and personalized medicine. Participants will be provided with datasets of four real phenotypes (Type 2 Diabetes, Breast Cancer, Inflammatory Bowel Disease and Coronary Artery Disease) and of thirty simulated phenotypes representing a range of genetic architectures of common polygenic diseases. The challenge is to predict the disease outcomes of individuals in held-out validation cohorts.","","https://genomeinterpretation.org/CAGI6-prs.html","completed","intermediate","1","","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:23" +"274","cagi6-rare-genomes-project","CAGI6: Rare Genomes Project","The prediction challenge involves approximately 30 families.The prediction s...","The Rare Genomes Project (RGP) is a direct-to-participant research study on the utility of genome sequencing for rare disease diagnosis and gene discovery. The study is led by genomics experts and clinicians at the Broad Institute of MIT and Harvard. Research subjects are consented for genomic sequencing and the sharing of their sequence and phenotype information with researchers working to understand the molecular causes of rare disease. When a candidate disease variant believed to be related to the phenotype is identified, the variant is confirmed with Sanger sequencing in a clinical setting and returned to the participant via his or her local physician. In this challenge, whole genome sequence data and phenotype data from a subset of the solved and unsolved RGP families will be provided. Participants in the challenge will try to identify the causative variant(s) in each case. For the unsolved cases, prioritized variants from the participating teams will be examined to see if ad...","","https://genomeinterpretation.org/CAGI6-rgp.html","completed","intermediate","1","","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:27" +"275","cagi6-sherloc-clinical-classification","CAGI6: Sherloc clinical classification","Over 122,000 coding (missense, silent, frameshift, stop gained, in-frame cod...","Invitae is a genetic testing company that publishes their variant interpretations to ClinVar. In this challenge, over 122,000 previously uncharacterized variants are provided, spanning the range of effects seen in the clinic. Following the close of this challenge, Invitae will submit their interpretations for these variants to ClinVar. Predictors are asked to interpret the pathogenicity of these variants, and the clinical utility of predictions will be assessed across multiple categories by Invitae.","","https://genomeinterpretation.org/CAGI6-invitae.html","completed","intermediate","1","","","2021-07-08","2021-12-01","2023-06-23 00:00:00","2023-10-12 18:12:31" +"276","cagi6-splicing-vus","CAGI6: Splicing VUS","Predict whether the experimentally validated variants of unknown significanc...","Variants causing aberrant splicing have been implicated in a range of common and rare disorders, including retinitis pigmentosa, autism spectrum disorder, amyotrophic lateral sclerosis, and a variety of cancers. However, such variants are frequently overlooked by diagnostic sequencing pipelines, leading to missed diagnoses for patients. Clinically ascertained variants of unknown significance underwent whole-blood based RT-PCR to test for impact on splicing. The challenge is to predict which of the tested variants disrupt splicing.","","https://genomeinterpretation.org/CAGI6-splicing-vus.html","completed","intermediate","1","","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:34" +"277","cagi6-stk11","CAGI6: STK11","Participants are asked to submit predictions on the impact of the variants l...","Serine/Threonine Kinase 11 (STK11) is considered a master kinase that functions as a tumor suppressor and nutrient sensor within a heterotrimeric complex with pseudo-kinase STRAD-alpha and structural protein MO25. Germline variants resulting in loss of STK11 define Peutz-Jaghers Syndrome, an autosomal dominant cancer predisposition syndrome marked by gastrointestinal hamartomas and freckling of the oral mucosa. Somatic loss of function variants, both nonsense and missense, occur in 15-30% of non-small cell lung adenocarcinomas, where they correlate clinically with insensitivity to anti-PD1 monoclonal antibody therapy. The challenge is to predict the impact on STK11 function for each missense variant in relation to wildtype STK11.","","https://genomeinterpretation.org/CAGI6-stk11.html","completed","intermediate","1","","","2021-06-08","2021-09-01","2023-06-23 00:00:00","2023-10-12 18:12:38" +"278","qbi-hackathon","QBI hackathon","A 48-hour event connecting the Bay Area developer community with scientists ...","The QBI hackathon is a 48-hour event connecting the vibrant Bay Area developer community with the scientists from UCSF, UCB and UCSC, during which we work together on the cutting edge biomedical problems. Advances in computer vision, AI, and machine learning have enabled computers to pick out cat videos, recognize people’s faces from photos, play video games and drive cars. More recently, application of deep neural nets to protein structure prediction completely revolutionized the field. We look forward to seeing how far we can push science ahead when we apply these latest algorithms to biomedically relevant light microscopy, electron microscopy, and proteomics data. If you love FFTs, transformers, language models, topological data processing, or simply writing code, this is your chance to apply your skills to make an impact on global healthcare. Beyond the actual event, we hope to establish a better connection between talented developers and scientists in the Bay Area, so that we...","","https://www.eventbrite.com/e/qbi-hackathon-2023-tickets-633794304827?aff=oddtdtcreator","upcoming","intermediate","14","","","20231104","20231105","2023-10-06 21:22:51","2023-10-19 23:49:11" +"279","niddk-central-repository-data-centric-challenge","NIDDK Central Repository Data-Centric Challenge","Enhancing NIDDK datasets for future Artificial Intelligence (AI) applications.","The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repository (https://repository.niddk.nih.gov/home/) is conducting a Data Centric Challenge aimed at augmenting existing Repository data for future secondary research including data-driven discovery by artificial intelligence (AI) researchers. The NIDDK Central Repository (NIDDK-CR) program strives to increase the utilization and impact of the resources under its guardianship. However, lack of standardization and consistent metadata within and across studies limit the ability of secondary researchers to easily combine datasets from related studies to generate new insights using data science methods. In the fall of 2021, the NIDDK-CR began implementing approaches to augment data quality to improve AI-readiness by making research data FAIR (findable, accessible, interoperable, and reusable) via a small pilot project utilizing Natural Language Processing (NLP) to tag study variables. In 2022, the NIDD...","","https://www.challenge.gov/?challenge=niddk-central-repository-data-centric-challenge","active","intermediate","14","","","2023-09-20","2023-11-03","2023-10-18 16:58:17","2023-10-18 20:52:49" diff --git a/apps/openchallenges/challenge-service/src/main/resources/db/migration/V1.0.0__create_tables.sql b/apps/openchallenges/challenge-service/src/main/resources/db/migration/V1.0.0__create_tables.sql index fb88dc8ce3..be3106d090 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/db/migration/V1.0.0__create_tables.sql +++ b/apps/openchallenges/challenge-service/src/main/resources/db/migration/V1.0.0__create_tables.sql @@ -39,6 +39,7 @@ CREATE TABLE `challenge` `difficulty` ENUM('good_for_beginners', 'intermediate', 'advanced'), `platform_id` int, `doi` varchar(80), + `operation` varchar(80), `start_date` DATE, `end_date` DATE, -- `email` varchar(255) DEFAULT NULL, From 64d4d3edda03c566134df9b0a3dc47a2b987d9f2 Mon Sep 17 00:00:00 2001 From: verena <9377970+vpchung@users.noreply.github.com> Date: Sat, 21 Oct 2023 03:16:08 +0000 Subject: [PATCH 02/14] add `operation` to the API specs --- .../api-description/build/challenge.openapi.yaml | 8 ++++++++ libs/openchallenges/api-description/build/openapi.yaml | 8 ++++++++ .../api-description/src/components/schemas/Challenge.yaml | 2 ++ .../src/components/schemas/ChallengeOperation.yaml | 5 +++++ 4 files changed, 23 insertions(+) create mode 100644 libs/openchallenges/api-description/src/components/schemas/ChallengeOperation.yaml diff --git a/libs/openchallenges/api-description/build/challenge.openapi.yaml b/libs/openchallenges/api-description/build/challenge.openapi.yaml index c41ed7d7c9..246650afdb 100644 --- a/libs/openchallenges/api-description/build/challenge.openapi.yaml +++ b/libs/openchallenges/api-description/build/challenge.openapi.yaml @@ -392,6 +392,12 @@ components: minLength: 0 maxLength: 280 example: This is an example description of the challenge. + ChallengeOperation: + description: The EDAM operation class of the challenge. + type: string + maxLength: 80 + pattern: ^$|^operation_\d+$ + example: operation_0004 ChallengePlatformId: description: The unique identifier of a challenge platform. type: integer @@ -473,6 +479,8 @@ components: $ref: '#/components/schemas/ChallengeDescription' doi: type: string + operation: + $ref: '#/components/schemas/ChallengeOperation' status: $ref: '#/components/schemas/ChallengeStatus' difficulty: diff --git a/libs/openchallenges/api-description/build/openapi.yaml b/libs/openchallenges/api-description/build/openapi.yaml index ebfaab86de..d67cc9e919 100644 --- a/libs/openchallenges/api-description/build/openapi.yaml +++ b/libs/openchallenges/api-description/build/openapi.yaml @@ -544,6 +544,12 @@ components: minLength: 0 maxLength: 280 example: This is an example description of the challenge. + ChallengeOperation: + description: The EDAM operation class of the challenge. + type: string + maxLength: 80 + pattern: ^$|^operation_\d+$ + example: operation_0004 ChallengePlatformId: description: The unique identifier of a challenge platform. type: integer @@ -625,6 +631,8 @@ components: $ref: '#/components/schemas/ChallengeDescription' doi: type: string + operation: + $ref: '#/components/schemas/ChallengeOperation' status: $ref: '#/components/schemas/ChallengeStatus' difficulty: diff --git a/libs/openchallenges/api-description/src/components/schemas/Challenge.yaml b/libs/openchallenges/api-description/src/components/schemas/Challenge.yaml index f3d41b1d78..e07950e922 100644 --- a/libs/openchallenges/api-description/src/components/schemas/Challenge.yaml +++ b/libs/openchallenges/api-description/src/components/schemas/Challenge.yaml @@ -13,6 +13,8 @@ properties: $ref: ChallengeDescription.yaml doi: type: string + operation: + $ref: ChallengeOperation.yaml status: $ref: ChallengeStatus.yaml difficulty: diff --git a/libs/openchallenges/api-description/src/components/schemas/ChallengeOperation.yaml b/libs/openchallenges/api-description/src/components/schemas/ChallengeOperation.yaml new file mode 100644 index 0000000000..434b04a2b9 --- /dev/null +++ b/libs/openchallenges/api-description/src/components/schemas/ChallengeOperation.yaml @@ -0,0 +1,5 @@ +description: The EDAM operation class of the challenge. +type: string +maxLength: 80 +pattern: '^$|^operation_\d+$' +example: operation_0004 From 2e75b66f3adfb88e3fe03bbfe9a4c054a591507f Mon Sep 17 00:00:00 2001 From: verena <9377970+vpchung@users.noreply.github.com> Date: Wed, 25 Oct 2023 22:54:32 +0000 Subject: [PATCH 03/14] reduce max char to 16 --- .../src/main/resources/db/migration/V1.0.0__create_tables.sql | 2 +- .../src/components/schemas/ChallengeOperation.yaml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/apps/openchallenges/challenge-service/src/main/resources/db/migration/V1.0.0__create_tables.sql b/apps/openchallenges/challenge-service/src/main/resources/db/migration/V1.0.0__create_tables.sql index be3106d090..4a011db46e 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/db/migration/V1.0.0__create_tables.sql +++ b/apps/openchallenges/challenge-service/src/main/resources/db/migration/V1.0.0__create_tables.sql @@ -39,7 +39,7 @@ CREATE TABLE `challenge` `difficulty` ENUM('good_for_beginners', 'intermediate', 'advanced'), `platform_id` int, `doi` varchar(80), - `operation` varchar(80), + `operation` varchar(16), `start_date` DATE, `end_date` DATE, -- `email` varchar(255) DEFAULT NULL, diff --git a/libs/openchallenges/api-description/src/components/schemas/ChallengeOperation.yaml b/libs/openchallenges/api-description/src/components/schemas/ChallengeOperation.yaml index 434b04a2b9..3455e9a0ad 100644 --- a/libs/openchallenges/api-description/src/components/schemas/ChallengeOperation.yaml +++ b/libs/openchallenges/api-description/src/components/schemas/ChallengeOperation.yaml @@ -1,5 +1,5 @@ description: The EDAM operation class of the challenge. type: string -maxLength: 80 +maxLength: 16 pattern: '^$|^operation_\d+$' example: operation_0004 From 142e5f5cfb19a984487742a50794073fb5fc7ac7 Mon Sep 17 00:00:00 2001 From: verena <9377970+vpchung@users.noreply.github.com> Date: Wed, 25 Oct 2023 22:55:40 +0000 Subject: [PATCH 04/14] accept NULL as possible value --- .../src/components/schemas/ChallengeOperation.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/libs/openchallenges/api-description/src/components/schemas/ChallengeOperation.yaml b/libs/openchallenges/api-description/src/components/schemas/ChallengeOperation.yaml index 3455e9a0ad..e5ccb7444d 100644 --- a/libs/openchallenges/api-description/src/components/schemas/ChallengeOperation.yaml +++ b/libs/openchallenges/api-description/src/components/schemas/ChallengeOperation.yaml @@ -2,4 +2,5 @@ description: The EDAM operation class of the challenge. type: string maxLength: 16 pattern: '^$|^operation_\d+$' +nullable: true example: operation_0004 From 667e3b95fceb9a9002633924b740e61c5c1c5ab8 Mon Sep 17 00:00:00 2001 From: verena <9377970+vpchung@users.noreply.github.com> Date: Wed, 25 Oct 2023 22:58:04 +0000 Subject: [PATCH 05/14] update openapi specs --- .../api-description/build/challenge.openapi.yaml | 3 ++- libs/openchallenges/api-description/build/openapi.yaml | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/libs/openchallenges/api-description/build/challenge.openapi.yaml b/libs/openchallenges/api-description/build/challenge.openapi.yaml index ac40ff32a3..6e56788f6e 100644 --- a/libs/openchallenges/api-description/build/challenge.openapi.yaml +++ b/libs/openchallenges/api-description/build/challenge.openapi.yaml @@ -395,8 +395,9 @@ components: ChallengeOperation: description: The EDAM operation class of the challenge. type: string - maxLength: 80 + maxLength: 16 pattern: ^$|^operation_\d+$ + nullable: true example: operation_0004 ChallengePlatformId: description: The unique identifier of a challenge platform. diff --git a/libs/openchallenges/api-description/build/openapi.yaml b/libs/openchallenges/api-description/build/openapi.yaml index acfef2fedd..4bf367f548 100644 --- a/libs/openchallenges/api-description/build/openapi.yaml +++ b/libs/openchallenges/api-description/build/openapi.yaml @@ -547,8 +547,9 @@ components: ChallengeOperation: description: The EDAM operation class of the challenge. type: string - maxLength: 80 + maxLength: 16 pattern: ^$|^operation_\d+$ + nullable: true example: operation_0004 ChallengePlatformId: description: The unique identifier of a challenge platform. From 5174865637961c5c0b5f52ed4154dd5656412553 Mon Sep 17 00:00:00 2001 From: verena <9377970+vpchung@users.noreply.github.com> Date: Sat, 17 Feb 2024 00:28:57 +0000 Subject: [PATCH 06/14] add latest challenge data with `operation` column --- .../src/main/resources/db/challenges.csv | 998 +++++++++--------- 1 file changed, 499 insertions(+), 499 deletions(-) diff --git a/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv b/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv index 3d179ac0d0..443279a94c 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv +++ b/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv @@ -1,499 +1,499 @@ -"id","slug","name","headline","description","avatar_url","website_url","status","platform","doi","start_date","end_date","created_at","updated_at" -"1","network-topology-and-parameter-inference","Network Topology and Parameter Inference","Optimize methods to estimate biology model parameters","Participants are asked to develop and/or apply optimization methods, including the selection of the most informative experiments, to accurately estimate parameters and predict outcomes of perturbations in Systems Biology models.","","https://www.synapse.org/#!Synapse:syn2821735","completed","1","","2012-06-01","2012-10-01","2023-11-15 22:40:15","2023-11-16 18:31:42" -"2","breast-cancer-prognosis","Breast Cancer Prognosis","Predict breast cancer survival from clinical and genomic data","The goal of the breast cancer prognosis Challenge is to assess the accuracy of computational models designed to predict breast cancer survival, based on clinical information about the patient's tumor as well as genome-wide molecular profiling data including gene expression and copy number profiles.","","https://www.synapse.org/#!Synapse:syn2813426","completed","1","","2012-07-12","2012-10-15","2023-11-14 20:36:32","2023-11-14 20:17:33" -"3","phil-bowen-als-prediction-prize4life","Phil Bowen ALS Prediction Prize4Life","Seeking treatment to halt ALS's fatal loss of motor function","Amyotrophic Lateral Sclerosis (ALS), or Lou Gehrig's disease, is a fatal neurological condition causing the death of nerve cells in the brain and spinal cord, resulting in a progressive loss of motor function while cognitive functions persist. Typically emerging around age 50, it affects about five in 100,000 people worldwide, with familial hereditary forms as the only known risk factors (5-10% of cases). There is currently no cure for ALS. The FDA-approved drug Riluzole extends life by a few months. ALS patients, on average, have a life expectancy of 2-5 years, with 10% experiencing slower disease progression. Astrophysicist Stephen Hawking, living with ALS for 49 years, is an exceptional case. The DREAM-Phil Bowen ALS Prediction Prize4Life, or ""ALS Prediction Prize,"" utilizes the PRO-ACT database with clinical data from over 7,500 ALS patients. This collaboration with DREAM aims to expedite ALS treatment discovery. Prize4Life, a non-profit, collaborates with NEALS and ALS Ther...","","https://www.synapse.org/#!Synapse:syn2826267","completed","1","","2012-06-01","2012-10-01","2023-11-01 22:09:02","2023-11-13 17:16:16" -"4","drug-sensitivity-and-drug-synergy-prediction","Drug Sensitivity and Drug Synergy Prediction","Predicting drug sensitivity in human cell lines","Development of new cancer therapeutics currently requires a long and protracted process of experimentation and testing. Human cancer cell lines represent a good model to help identify associations between molecular subtypes, pathways, and drug response. In recent years there have been several efforts to generate genomic profiles of collections of cell lines and to determine their response to panels of candidate therapeutic compounds. These data provide the basis for the development of in silico models of sensitivity based either on the unperturbed genetic potential of a cancer cell, or by using perturbation data to incorporate knowledge of actual cell response. Making predictions from either of these data profiles will be beneficial in identifying single and combinatorial chemotherapeutic response in patients. To that end, the present challenge seeks computational methods, derived from the molecular profiling of cell lines both in a static state and in response to perturbation of ...","","https://www.synapse.org/#!Synapse:syn2785778","completed","1","","2012-06-01","2012-10-01","2023-11-01 22:08:36","2023-11-16 17:58:39" -"5","niehs-ncats-unc-toxicogenetics","NIEHS-NCATS-UNC Toxicogenetics","Predicting cytotoxicity from genomic and chemical data","This challenge is designed to build predictive models of cytotoxicity as mediated by exposure to environmental toxicants and drugs. To approach this question, we will provide a dataset containing cytotoxicity estimates as measured in lymphoblastoid cell lines derived from 884 individuals following in vitro exposure to 156 chemical compounds. In subchallenge 1, participants will be asked to model interindividual variability in cytotoxicity based on genomic profiles in order to predict cytotoxicity in unknown individuals. In subchallenge 2, participants will be asked to predict population-level parameters of cytotoxicity across chemicals based on structural attributes of compounds in order to predict median cytotoxicity and mean variance in toxicity for unknown compounds.","","https://www.synapse.org/#!Synapse:syn1761567","completed","1","","2013-06-10","2013-09-15","2023-11-01 22:08:45","2023-11-01 22:06:01" -"6","whole-cell-parameter-estimation","Whole-Cell Parameter Estimation","Seeking innovative parameter estimation methods for large models","The goal of this challenge is to explore and compare innovative approaches to parameter estimation of large, heterogeneous computational models. Participants are encouraged to develop and/or apply optimization methods, including the selection of the most informative experiments. The organizers encourage participants to form teams to collaboratively solve the challenge.","","https://www.synapse.org/#!Synapse:syn1876068","completed","1","","2013-06-10","2013-09-23","2023-06-23 00:00:00","2023-11-01 22:06:23" -"7","hpn-dream-breast-cancer-network-inference","HPN-DREAM Breast Cancer Network Inference","Inferring causal signaling networks in breast cancer","The overall goal of the Heritage-DREAM breast cancer network inference challenge is to quickly and effectively advance our ability to infer causal signaling networks and predict protein phosphorylation dynamics in cancer. We provide extensive training data from experiments on four breast cancer cell lines stimulated with various ligands. The data comprise protein abundance time-courses under inhibitor perturbations.","","https://www.synapse.org/#!Synapse:syn1720047","completed","1","","2013-06-10","2013-09-16","2023-06-23 00:00:00","2023-11-13 17:15:59" -"8","rheumatoid-arthritis-responder","Rheumatoid Arthritis Responder","Unlocking Anti-TNF response predictors in RA therapy","The goal of this project is to use a crowd-based competition framework to develop a validated molecular predictor of anti-TNF response in RA. There is an increasing need for predictors of response to therapy in inflammatory disease driven by the observation that most clinically defined diseases show variable response and the growing availability of alternative therapies. Anti-TNF drugs in Rheumatoid Arthritis represent a prototypical example of this opportunity. A number of studies have tried, over the past decade, to develop a robust predictor of response. We believe the time is right to try a different approach to developing such a biomarker with a crowd-sourced collaborative competition. This is based on DREAM and Sage Bionetworks' experience with running competitions and the availability of new unpublished large-scale data relating to RA treatment response.THIS CHALLENGE RAN FROM FEBRUARY TO OCTOBER 2014 AND IS NOW CLOSED.","","https://www.synapse.org/#!Synapse:syn1734172","completed","1","","2014-02-10","2014-06-04","2023-06-23 00:00:00","2023-11-15 22:42:17" -"9","icgc-tcga-dream-mutation-calling","ICGC-TCGA DREAM Mutation Calling","Crowdsourcing challenge to improve cancer mutation detection","The ICGC-TCGA DREAM Genomic Mutation Calling Challenge (herein, The Challenge) is an international effort to improve standard methods for identifying cancer-associated mutations and rearrangements in whole-genome sequencing (WGS) data. Leaders of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cancer genomics projects are joining with Sage Bionetworks and IBM-DREAM to initiate this innovative open crowd-sourced Challenge [1-3].","","https://www.synapse.org/#!Synapse:syn312572","completed","1","","2013-12-14","2016-04-22","2023-06-23 00:00:00","2023-10-14 05:38:15" -"10","acute-myeloid-leukemia-outcome-prediction","Acute Myeloid Leukemia Outcome Prediction","Uncover drivers of AML using clinical and proteomic data","The AML Outcome Prediction Challenge provides a unique opportunity to access and interpret a rich dataset for AML patients that includes clinical covariates, select gene mutation status and proteomic data. Capitalizing on a unique AML reverse phase protein array (RPPA) dataset obtained at M.D. Anderson Cancer Center that captures 271 measurements for each patient, participants of the DREAM 9 Challenge will help uncover what drives AML. Outcomes of this Challenge have the potential to be used immediately to tailor therapies for newly diagnosed leukemia patients and to accelerate the development of new drugs for leukemia.","","https://www.synapse.org/#!Synapse:syn2455683","completed","1","","2014-06-02","2014-09-15","2023-06-23 00:00:00","2023-10-14 05:38:16" -"11","broad-dream-gene-essentiality-prediction","Broad-DREAM Gene Essentiality Prediction","Crowdsourcing models to predict cancer cell gene dependencies","The goal of this project is to use a crowd-based competition to develop predictive models that can infer gene dependency scores in cancer cells (genes that are essential to cancer cell viability when suppressed) using features of those cell lines. An additional goal is to find a small set of biomarkers (gene expression, copy number, and mutation features) that can best predict a single gene or set of genes.","","https://www.synapse.org/#!Synapse:syn2384331","completed","1","","2014-06-02","2014-09-29","2023-06-23 00:00:00","2023-10-14 05:38:16" -"12","alzheimers-disease-big-data","Alzheimer's Disease Big Data","Seeking accurate predictive biomarkers","The goal of the Alzheimer's Disease Big Data DREAM Challenge #1 (AD#1) was to apply an open science approach to rapidly identify accurate predictive AD biomarkers that can be used by the scientific, industrial and regulatory communities to improve AD diagnosis and treatment. AD#1 will be the first in a series of AD Data Challenges to leverage genetics and brain imaging in combination with cognitive assessments, biomarkers and demographic information from cohorts ranging from cognitively normal to mild cognitively impaired to individuals with AD.","","https://www.synapse.org/#!Synapse:syn2290704","completed","1","","2014-06-02","2014-10-17","2023-06-23 00:00:00","2023-10-14 05:38:17" -"13","olfaction-prediction","Olfaction Prediction","Predicting smell from molecule features","The goal of the DREAM Olfaction Prediction Challenge is to find models that can predict how a molecule smells from its physical and chemical features. A model that allows us to predict a smell from a molecule will provide fundamental insights into how odor chemicals are transformed into a smell percept in the brain. Further, being able to predict how a chemical smells will greatly accelerate the design of new molecules to be used as fragrances. Currently, fragrance chemists synthesize many molecules to obtain a new ingredient, but most of these will not have the desired qualities.","","https://www.synapse.org/#!Synapse:syn2811262","completed","1","","2015-01-15","2015-05-01","2023-11-01 22:11:08","2023-10-14 05:38:17" -"14","prostate-cancer","Prostate Cancer","Predict survival of docetaxel treatment in mCRPC patients","This challenge will attempt to improve the prediction of survival and toxicity of docetaxel treatment in patients with metastatic castration-resistant prostate cancer (mCRPC). The primary benefit of this Challenge will be to establish new quantitative benchmarks for prognostic modeling in mCRPC, with a potential impact for clinical decision making and ultimately understanding the mechanism of disease progression. Participating teams will be asked to submit predictive models based on clinical variables from the comparator arms of four phase III clinical trials with over 2,000 mCRPC patients treated with first-line docetaxel. The comparator arm of a clinical trial represents the patients that receive a treatment that is considered to be effective. This arm of the clinical trial is used to evaluate the effectiveness of the new therapy being tested.","","https://www.synapse.org/#!Synapse:syn2813558","completed","1","","2015-03-16","2015-07-27","2023-06-23 00:00:00","2023-10-14 05:38:18" -"15","als-stratification-prize4life","ALS Stratification Prize4Life","Predicting ALS progression and survival with data","As illustrated by the overview figure below, (a) Challenge Data includes data from ALS clinical trials and ALS registries. ALS clinical trials consist of patients from clinical trials available open access on the PRO-ACT database and patients from 6 clinical trials not yet added into the database. Data from ALS registries was collected from patients in national ALS registries. (b) Data is divided into three subsets-training data provided to solvers in full, leaderboard, and validation data that is available only to the organizers and is reserved for the scoring of the challenge. (c) The goal of this challenge is then to predict the Clinical Targets, i.e. the disease progression as ALSFRS slope as well as survival. (d) For Building the Models, participants create two algorithms-one that selects features and one that predicts outcomes. To perform predictions, data from a given patient is fed into the selector . The selector selects 6 features and a cluster/model ID (3), e.g. from a...","","https://www.synapse.org/#!Synapse:syn2873386","completed","1","","2015-06-22","2015-10-04","2023-06-23 00:00:00","2023-10-14 05:38:19" -"16","astrazeneca-sanger-drug-combination-prediction","AstraZeneca-Sanger Drug Combination Prediction","Predict effective drug combinations using genomic data","To accelerate the understanding of drug synergy, AstraZeneca has partnered with the European Bioinformatic Institute, the Sanger Institute, Sage Bionetworks, and the distributed DREAM community to launch the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. This Challenge is designed to explore fundamental traits that underlie effective combination treatments and synergistic drug behavior using baseline genomic data, i.e. data collected pretreatment. As the basis of the Challenge, AstraZeneca is releasing ~11.5k experimentally tested drug combinations measuring cell viability over 118 drugs and 85 cancer cell lines (primarily colon, lung, and breast), and monotherapy drug response data for each drug and cell line. Moreover, in coordination with the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Sanger Institute, genomic data including gene expression, mutations (whole exome), copy-number alterations, and methylation data will be released into the publ...","","https://www.synapse.org/#!Synapse:syn4231880","completed","1","","2015-09-03","2016-03-14","2023-06-23 00:00:00","2023-10-14 05:38:19" -"17","smc-dna-meta","SMC-DNA Meta","Seeking most accurate somatic mutation detection pipeline","The goal of this Challenge is to identify the most accurate meta-pipeline for somatic mutation detection, and establish the state-of-the-art. The algorithms in this Challenge must use as input mutations predicted by one or more variant callers and output mutation calls associated with cancer. An additional goal is to highlight the complementarity of the calling algorithms and help understand their individual advantages/deficiencies.","","https://www.synapse.org/#!Synapse:syn4588939","completed","1","","2015-08-17","2016-04-10","2023-06-23 00:00:00","2023-10-14 05:38:20" -"18","smc-het","SMC-Het","Crowdsourcing challenge to improve tumor subclonal reconstruction","The ICGC-TCGA DREAM Somatic Mutation Calling-Tumour Heterogeneity Challenge (SMC-Het) is an international effort to improve standard methods for subclonal reconstruction-to quantify and genotype each individual cell population present within a tumor. Leaders of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cancer genomics projects are joining with Sage Bionetworks and IBM-DREAM to initiate this innovative open crowd-sourced Challenge [1-3].","","https://www.synapse.org/#!Synapse:syn2813581","completed","1","","2015-11-16","2016-06-30","2023-11-01 22:21:29","2023-10-14 05:38:21" -"19","respiratory-viral","Respiratory Viral","Early predictors of respiratory infection and contagiousness","Respiratory viruses are highly infectious and cause acute illness in millions of people every year. However, there is wide variation in the physiologic response to exposure at the individual level. Some people that are exposed to virus are able to completely avoid infection. Others contract virus but are able to fight it off without exhibiting any symptoms of illness such as coughing, sneezing, sore throat or fever. It is not well understood what characteristics may protect individuals from respiratory viral infection. These individual responses are likely influenced by multiple processes including both the basal state of the human host upon exposure and the dynamics of host immune response in the early hours immediately following exposure. Many of these processes play out in the peripheral blood through activation and recruitment of circulating immune cells. Global gene expression patterns measured in peripheral blood at the time of symptom onset-several days after viral exposure...","","https://www.synapse.org/#!Synapse:syn5647810","completed","1","","2016-05-16","2016-09-28","2023-06-23 00:00:00","2023-11-14 20:16:42" -"20","disease-module-identification","Disease Module Identification","Crowdsourcing challenge to find disease modules in genomic networks","The Disease Module Identification DREAM Challenge is an open community effort to systematically assess module identification methods on a panel of state-of-the-art genomic networks and leverage the “wisdom of crowds” to discover novel modules and pathways underlying complex diseases.","","https://www.synapse.org/#!Synapse:syn6156761","completed","1","https://doi.org/10.1038/s41592-019-0509-5","2016-06-24","2016-10-01","2023-11-01 22:21:32","2023-10-16 21:17:48" -"21","encode","ENCODE","Predict transcription factor binding sites from limited data","Transcription factors (TFs) are regulatory proteins that bind specific DNA sequence patterns (motifs) in the genome and affect transcription rates of target genes. Binding sites of TFs differ across cell types and experimental conditions. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an experimental method that is commonly used to obtain the genome-wide binding profile of a TF of interest in a specific cell type/condition. However, profiling the binding landscape of every TF in every cell type/condition is infeasible due to constraints on cost, material and effort. Hence, accurate computational prediction of in vivo TF binding sites is critical to complement experimental results.","","https://www.synapse.org/#!Synapse:syn6131484","completed","1","","2016-07-07","2017-01-11","2023-11-01 22:21:32","2023-10-14 05:38:26" -"22","idea","Idea","Fostering collaborative solutions in health: the DREAM IDEA challenge","The DREAM Idea Challenge is designed to collaboratively shape and enable the solution of a question fundamental to improving human health. In the process, all proposals and their evaluation will be made publicly available for the explicit purpose of connecting modelers and experimentalists who want to address the same question. This Wall of Models will enable new collaborations, and help turn every good modeling idea into a success story. It will further serve as a basis for new DREAM challenges.","","https://www.synapse.org/#!Synapse:syn5659209","completed","1","","2016-06-15","2017-04-30","2023-06-23 00:00:00","2023-11-20 20:18:36" -"23","smc-rna","SMC-RNA","Crowdsourcing challenge to improve cancer mutation detection from rna data","The ICGC-TCGA DREAM Somatic Mutation Calling-RNA Challenge (SMC-RNA) is an international effort to improve standard methods for identifying cancer-associated rearrangements in RNA sequencing (RNA-seq) data. Leaders of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cancer genomics projects are joining with Sage Bionetworks and IBM-DREAM to initiate this innovative open crowd-sourced Challenge [1-3].","","https://www.synapse.org/#!Synapse:syn2813589","completed","1","","2016-06-29","2017-05-02","2023-06-23 00:00:00","2023-10-14 05:38:29" -"24","digital-mammography-dream-challenge","Digital Mammography DREAM Challenge","Improve mammography prediction to detect breast cancer early","The Digital Mammography DREAM Challenge will attempt to improve the predictive accuracy of digital mammography for the early detection of breast cancer. The primary benefit of this Challenge will be to establish new quantitative tools-machine learning, deep learning or other-that can help decrease the recall rate of screening mammography, with a potential impact on shifting the balance of routine breast cancer screening towards more benefit and less harm. Participating teams will be asked to submit predictive models based on over 640,000 de-identified digital mammography images from over 86000 subjects, with corresponding clinical variables.","","https://www.synapse.org/#!Synapse:syn4224222","completed","1","https://doi.org/10.1001/jamanetworkopen.2020.0265","2016-11-18","2017-05-16","2023-06-23 00:00:00","2023-10-14 05:38:29" -"25","multiple-myeloma","Multiple Myeloma","Develop precise risk model for myeloma patients","Multiple myeloma (MM) is a cancer of the plasma cells in the bone marrow, with about 25,000 newly diagnosed patients per year in the United States alone. The disease's clinical course depends on a complex interplay of clinical traits and molecular characteristics of the plasma cells.1 Since risk-adapted therapy is becoming standard of care, there is an urgent need for a precise risk stratification model to assist in therapeutic decision-making and research. While progress has been made, there remains a significant opportunity to improve patient stratification to optimize treatment and to develop new therapies for high-risk patients. A DREAM Challenge represents a chance not only to integrate available data and analytical approaches to tackle this important problem, but also provides the ability to benchmark potential methods to identify those with the greatest potential to yield patient care benefits in the future.","","https://www.synapse.org/#!Synapse:syn6187098","completed","1","","2017-06-30","2017-11-08","2023-06-23 00:00:00","2023-10-14 05:38:31" -"26","ga4gh-dream-workflow-execution","GA4GH-DREAM Workflow Execution","Develop technologies to enable distributed genomic data analysis","The highly distributed and disparate nature of genomic and clinical data generated around the world presents an enormous challenge for those scientists who wish to integrate and analyze these data. The sheer volume of data often exceeds the capacity for storage at any one site and prohibits the efficient transfer between sites. To address this challenge, researchers must bring their computation to the data. Numerous groups are now developing technologies and best practice methodologies for running portable and reproducible genomic analysis pipelines as well as tools and APIs for discovering genomic analysis resources. Software development, deployment, and sharing efforts in these groups commonly rely on the use of modular workflow pipelines and virtualization based on Docker containers and related tools.","","https://www.synapse.org/#!Synapse:syn8507133","completed","1","","2017-07-21","2017-12-31","2023-06-23 00:00:00","2023-10-14 05:38:31" -"27","parkinsons-disease-digital-biomarker","Parkinson's Disease Digital Biomarker","Develop Parkinson's digital signatures from sensor data for Parkinson's disease","The Parkinson's Disease Digital Biomarker DREAM Challenge is a first of it's kind challenge, designed to benchmark methods for the processing of sensor data for development of digital signatures reflective of Parkinson's Disease. Participants will be provided with raw sensor (accelerometer, gyroscope, and magnetometer) time series data recorded during the performance of pre-specified motor tasks, and will be asked to extract data features which are predictive of PD pathology. In contrast to traditional DREAM challenges, this one will focus on feature extraction rather than predictive modeling, and submissions will be evaluated based on their ability to predict disease phenotype using an array of standard machine learning algorithms.","","https://www.synapse.org/#!Synapse:syn8717496","completed","1","","2017-07-06","2017-11-10","2023-06-23 00:00:00","2023-11-14 19:10:32" -"28","nci-cptac-proteogenomics","NCI-CPTAC Proteogenomics","Develop tools to extract insights from cancer proteomics data","Cancer is driven by aberrations in the genome [1,2], and these alterations manifest themselves largely in the changes in the structure and abundance of proteins, the main functional gene products. Hence, characterization and analyses of alterations in the proteome has the promise to shed light into cancer development and may improve development of both biomarkers and therapeutics. Measuring the proteome is very challenging, but recent rapid technology developments in mass spectrometry are enabling deep proteomics analysis [3]. Multiple initiatives have been launched to take advantage of this development to characterize the proteome of tumours, such as the Clinical Proteomic Tumor Analysis Consortium (CPTAC). These efforts hold the promise to revolutionize cancer research, but this will only be possible if the community develops computational tools powerful enough to extract the most information from the proteome, and to understand the association between genome, transcriptome and ...","","https://www.synapse.org/#!Synapse:syn8228304","completed","1","","2017-06-26","2017-11-20","2023-11-01 22:21:37","2023-10-14 05:38:33" -"29","multi-targeting-drug","Multi-Targeting Drug","Seeking generalizable methods to predict multi-target compound binding","The objective of this challenge is to incentivize development of methods for predicting compounds that bind to multiple targets. In particular, methods that are generalizable to multiple prediction problems are sought. To achieve this, participants will be asked to predict 2 separate compounds, each having specific targets to which they should bind, and a list of anti-targets to avoid. Participants should use the same methods to produce answers for questions 1 and 2.","","https://www.synapse.org/#!Synapse:syn8404040","completed","1","","2017-10-05","2018-02-26","2023-06-23 00:00:00","2023-10-14 05:38:33" -"30","single-cell-transcriptomics","Single Cell Transcriptomics","Reconstructing cell locations in Drosophila embryo from transcripts","In this Challenge on Single-Cell Transcriptomics, participants will reconstruct the location of single cells in the Drosophila embryo using single-cell transcriptomic data. Data will be made available in late August and participating challenge teams can work on the data and submit their results previous to the DREAM Conference. The best performers will be announced at the DREAM conference on Dec 8.","","https://www.synapse.org/#!Synapse:syn15665609","completed","1","","2018-09-04","2018-11-21","2023-06-23 00:00:00","2023-11-16 18:38:46" -"31","idg-drug-kinase-binding","IDG Drug-Kinase Binding","Drug-kinase binding prediction for IDG drug-kinase binding","This IDG-DREAM Drug-Kinase Binding Prediction Challenge seeks to evaluate the power of statistical and machine learning models as a systematic and cost-effective means for catalyzing compound-target interaction mapping efforts by prioritizing most potent interactions for further experimental evaluation. The Challenge will focus on kinase inhibitors, due to their clinical importance [2], and will be implemented in a screening-based, pre-competitive drug discovery project in collaboration with theIlluminating the Druggable Genome (IDG) Kinase-focused Data and Resource Generation Center, consortium, with the aim to establish kinome-wide target profiles of small-molecule agents, with the goal of extending the druggability of the human kinome space.","","https://www.synapse.org/#!Synapse:syn15667962","completed","1","","2018-10-01","2019-04-18","2023-06-23 00:00:00","2023-11-14 19:07:18" -"32","malaria","Malaria","Predict malaria drug resistance from parasite gene expression for malaria","The Malaria DREAM Challenge is open to anyone interested in contributing to the development of computational models that address important problems in advancing the fight against malaria. The overall goal of the first Malaria DREAM Challenge is to predict Artemisinin (Art) drug resistance level of a test set of malaria parasites using their in vitro transcription data and a training set consisting of published in vivo and unpublished in vitrotranscriptomes. The in vivodataset consists of ~1000 transcription samples from various geographic locations covering a wide range of life cycles and resistance levels, with other accompanying data such as patient age, geographic location, Art combination therapy used, etc [Mok et al (2015) Science]. The in vitro transcription dataset consists of 55 isolates, with transcription collected at two timepoints (6 and 24 hours post-invasion), in the absence or presence of an Art perturbation, for two biological replicates using a custom microarray a...","","https://www.synapse.org/#!Synapse:syn16924919","completed","1","","2019-04-30","2019-08-15","2023-06-23 00:00:00","2023-10-14 05:38:35" -"33","preterm-birth-prediction-transcriptomics","Preterm Birth Prediction - Transcriptomics","Determine gestational age for preterm birth prediction","A basic need in pregnancy care is to establish gestational age, and inaccurate estimates may lead to unnecessary interventions and sub-optimal patient management. Current approaches to establish gestational age rely on patient's recollection of her last menstrual period and/or ultrasound, with the latter being not only costly but also less accurate if not performed during the first trimester of pregnancy. Therefore development of an inexpensive and accurate molecular clock of pregnancy would be of benefit to patients and health care systems. Participants in sub-challenge 1 (Prediction of gestational age) will be given whole blood gene topic_3170 collected from pregnant women to develop prediction models for the gestational age at blood draw. Another challenge in obstetrics, in both low and high-income countries, is identification and treatment of women at risk of developing the ‘great obstetrical syndromes‘. Of these, preterm birth (PTB), defined as giving birth prior to completio...","","https://www.synapse.org/#!Synapse:syn18380862","completed","1","","2019-05-04","2019-12-05","2023-06-23 00:00:00","2023-11-14 19:07:28" -"34","single-cell-signaling-in-breast-cancer","Single-Cell Signaling in Breast Cancer","Exploring heterogeneous signaling in single cancer cells","Signaling underlines nearly every cellular event. Individual cells, even if genetically identical, respond to perturbation in different ways. This underscores the relevance of cellular heterogeneity, in particular in how cells respond to drugs. This is of high relevance since the fact that a subset of cells do not respond (or only weakly) to drugs can render this drug an ineffective treatment. In spite of its relevance to many diseases, comprehensive studies on the heterogeneous signaling in single cells are still lacking. We have generated the, to our knowledge, currently largest single cell signaling dataset on a panel of 67 well-characterized breast cancer cell lines by mass cytometry (3'015 conditions, ~80 mio single cells, 38 markers; Bandura et al. 2009; Bendall et al., 2011; Bodenmiller et al., 2012; Lun et al., 2017; Lun et al., 2019). These cell lines are, among others, also characterized at the genomic, transcriptomic, and proteomic level (Marcotte et al., 2016). We ask ...","","https://www.synapse.org/#!Synapse:syn20366914","completed","1","","2018-08-20","2019-11-15","2023-06-23 00:00:00","2023-10-14 05:38:37" -"35","ehr-dream-challenge-patient-mortality-prediction","EHR DREAM Challenge: Patient Mortality Prediction","New tools to reconstruct cell lineages from CRISPR mutations","The recent advent of new CRISPR-based molecular tools allows the reconstruction of cell lineages based on the phylogenetical analysis of DNA mutations induced by CRISPR during development and promises to solve the lineage of complex model organisms at single-cell resolution (see image from McKenna et al Science 2016). To date, however, no lineage reconstruction algorithms have been rigorously examined for their performance/robustness across diverse molecular tools, datasets, and number of cells/size of lineage trees. It also remains unclear whether new Machine-Learning algorithms that go beyond the classical ones developed for reconstructing phylogenetic trees, could consistently reconstruct cell lineages to a high degree of accuracy. The challenge-a partnership between The Allen Institute and DREAM-will comprise 3 subchallenges that consist of reconstructing cell lineage trees of different sizes and nature. In subchallenge 1, participants will be given experimental molecular data...","","https://www.synapse.org/#!Synapse:syn18405991","completed","1","https://doi.org/10.1093/jamia/ocad159","2019-09-09","2020-01-23","2023-06-23 00:00:00","2023-11-02 18:25:23" -"36","allen-institute-cell-lineage-reconstruction","Allen Institute Cell Lineage Reconstruction","New tools enable reconstructing complex cell lineages at single-cell resolution","The recent advent of new CRISPR-based molecular tools allows the reconstruction of cell lineages based on the phylogenetical analysis of DNA mutations induced by CRISPR during development and promises to solve the lineage of complex model organisms at single-cell resolution. To date, however, no lineage reconstruction algorithms have been rigorously examined for their performance/robustness across diverse molecular tools, datasets, and number of cells/size of lineage trees. It also remains unclear whether new Machine-Learning algorithms that go beyond the classical ones developed for reconstructing phylogenetic trees, could consistently reconstruct cell lineages to a high degree of accuracy. The challenge-a partnership between The Allen Institute and DREAM-will comprise 3 subchallenges that consist of reconstructing cell lineage trees of different sizes and nature. In subchallenge 1, participants will be given experimental molecular data to reconstruct in vitro cell lineages of l...","","https://www.synapse.org/#!Synapse:syn20692755","completed","1","","2019-10-15","2020-02-06","2023-06-23 00:00:00","2023-11-02 18:25:24" -"37","tumor-deconvolution","Tumor Deconvolution","Deconvolve bulk tumor data into immune components","The extent of stromal and immune cell infiltration within solid tumors has prognostic and predictive significance. Unfortunately, expression profiling of tumors has, until very recently, largely been undertaken using bulk techniques (e.g., microarray and RNA-seq). Unlike single-cell methods (e.g., single-cell RNA-seq, FACS, mass cytometry, or immunohistochemistry), bulk approaches average expression across all cells (cancer, stromal, and immune) within the sample and, hence, do not directly quantitate tumor infiltration. This information can be recovered by computational tumor deconvolution methods, which would thus allow interrogation of immune subpopulations across the large collection of public bulk topic_3170sets. The goal of this Challenge is to evaluate the ability of computational methods to deconvolve bulk topic_3170, reflecting a mixture of cell types, into individual immune components. Methods will be assessed based on in vitro and in silico admixtures specifically gener...","","https://www.synapse.org/#!Synapse:syn15589870","completed","1","","2019-06-26","2020-04-30","2023-06-23 00:00:00","2023-11-14 19:07:39" -"38","ctd2-pancancer-drug-activity","CTD2 Pancancer Drug Activity","Benchmark algorithms predicting drug targets from gene data","Over the last two years, the Columbia CTD2 Center developed PANACEA (Pancancer Analysis of Chemical Entity Activity), a comprehensive repertoire of dose response curves and molecular profiles representative of cellular responses to drug perturbations. PANACEA covers a broad spectrum of cellular contexts representative of poor outcome malignancies, including rare ones such as GIST sarcoma and gastroenteropancreatic neuroendocrine tumors (GEP-NETs). PANACEA is uniquely suited to support DREAM Challenges related to the elucidation of drug mechanism of action (MOA), drug sensitivity, and drug synergy. The goal of the CTD2 Pancancer Drug Activity DREAM Challenge is to foster the development and benchmarking of algorithms to predict targets of chemotherapeutic compounds from post-treatment transcriptional data.","","https://www.synapse.org/#!Synapse:syn20968331","completed","1","","2019-12-02","2020-02-13","2023-06-23 00:00:00","2023-10-20 23:11:10" -"39","ctd2-beataml","CTD2 BeatAML","Seeking new drug targets for precision AML treatment","In the era of precision medicine, AML patients have few therapeutic options, with “7 + 3” induction chemotherapy having been the standard for decades (Bertoli et al. 2017). While several agents targeting the myeloid marker CD33 or alterations in FLT3 or IDH2 have demonstrated efficacy in patients (Wei and Tiong 2017), responses are uncertain in some populations (Castaigne et al. 2012) and relapse remains prevalent (Stone et al. 2017). These drugs highlight both the promise of targeted therapies in AML and the urgent need for additional treatment options that are tailored to more refined patient subpopulations in order to achieve durable responses. The BeatAML initiative was launched as a comprehensive study of the relationship between molecular alterations and ex-vivo drug sensitivity in patients with AML. One of the primary goals of this multi-center study was to develop a discovery cohort that could yield new drug target hypotheses and predictive biomarkers of therapeutic respon...","","https://www.synapse.org/#!Synapse:syn20940518","completed","1","","2019-12-19","2020-04-28","2023-06-23 00:00:00","2023-10-14 05:38:42" -"40","metadata-automation","Metadata Automation","Semi-automating metadata annotation for enhanced data sharing in cancer research","The Cancer Research Data Commons (CRDC) will collate data across diverse groups of cancer researchers, each collecting biomedical data in different formats. This means the data must be retrospectively harmonized and transformed to enable this data to be submitted. In addition, to be findable by the broader scientific community, coherent information (metadata) is necessary about the data fields and values. Coherent metadata annotation of the data fields and their values can enable computational data transformation, query, and analysis. Creation of this type of descriptive metadata can require biomedical expertise to determine the best annotations and thus is a time-consuming and manual task which is both an obstacle and a bottleneck in data sharing and submissions. Goal-Using structured biomedical data files, challenge participants will develop tools to semi-automate annotation of metadata fields and values, using available research data annotations (e.g. caDSR CDEs) as well as es...","","https://www.synapse.org/#!Synapse:syn18065891","completed","1","","2020-01-14","2020-06-02","2023-06-23 00:00:00","2023-10-14 05:38:42" -"41","automated-scoring-of-radiographic-joint-damage","Automated Scoring of Radiographic Joint Damage","Develop automated method to quantify rheumatoid arthritis joint damage","The purpose of the RA2-DREAM Challenge is to develop an automated method to quickly and accurately quantify the degree of joint damage associated with rheumatoid arthritis (RA). Based on radiographs of the hands and feet, a novel, automated scoring method could be applied broadly for patient care and research. We challenge participants to develop algorithms to automatically assess joint space narrowing and erosions using a large set of existing radiographs with damage scores generated by visual assessment of images by trained readers using standard protocols. The end result will be a generalizable, publicly available, automated method to generate accurate, reproducible and unbiased RA damage scores to replace the current tedious, expensive, and non-scalable method of scoring by human visual inspection.","","https://www.synapse.org/#!Synapse:syn20545111","completed","1","","2019-11-04","2020-05-21","2023-06-23 00:00:00","2023-10-18 00:38:55" -"42","beat-pd","BEAT-PD","Develop mobile sensors to remotely monitor Parkinson's disease","Recent advances in mobile health have demonstrated great potential to leverage sensor-based technologies for quantitative, remote monitoring of health and disease-particularly for diseases affecting motor function such as Parkinson's disease. Such approaches have been rolled out using research-grade wearable sensors and, increasingly, through the use of smartphones and consumer wearables, such as smart watches and fitness trackers. These devices not only provide the ability to measure much more detailed disease phenotypes but also provide the ability to follow patients longitudinally with much higher frequency than is possible through clinical exams. However, the conversion of sensor-based data streams into digital biomarkers is complex and no methodological standards have yet evolved to guide this process. Parkinson's disease (PD) is a neurodegenerative disease that primarily affects the motor system but also exhibits other symptoms. Typical motor symptoms of the disease include...","","https://www.synapse.org/#!Synapse:syn20825169","completed","1","","2020-01-13","2020-05-13","2023-06-23 00:00:00","2023-10-14 05:38:45" -"43","ctd2-pancancer-chemosensitivity","CTD2 Pancancer Chemosensitivity","Predict drug sensitivity from cell line gene expression","Over the last two years, the Columbia CTD2 Center developed PANACEA (Pancancer Analysis of Chemical Entity Activity), a comprehensive repertoire of dose response curves and molecular profiles representative of cellular responses to drug perturbations. PANACEA covers a broad spectrum of cellular contexts representative of poor outcome malignancies, including rare ones such as GIST sarcoma and gastroenteropancreatic neuroendocrine tumors (GEP-NETs). PANACEA is uniquely suited to support DREAM Challenges related to the elucidation of drug mechanism of action (MOA), drug sensitivity, and drug synergy. The goal of this Challenge is to foster development and benchmarking of algorithms to predict the sensitivity, as measured by the area under the dose-response curve, of a cell line to a compound based on the baseline transcriptional profiles of the cell line. The drug perturbational RNAseq profiles of 11 cell lines for 30 chosen compounds will be provided to challenge participants, with...","","https://www.synapse.org/#!Synapse:syn21763589","completed","1","","2020-04-28","2020-07-27","2023-06-23 00:00:00","2023-10-14 05:38:45" -"44","ehr-dream-challenge-covid-19","EHR DREAM Challenge: COVID-19","Develop tools to predict COVID-19 risk without sharing data","The rapid rise of COVID-19 has challenged healthcare globally. The underlying risks and outcomes of infection are still incompletely characterized even as the world surpasses 4 million infections. Due to the importance and emergent need for better understanding of the condition and the development of patient specific clinical risk scores and early warning tools, we have developed a platform to support testing analytic and machine learning hypotheses on clinical data without data sharing as a platform to rapidly discover and implement approaches for care. We have previously applied this approach in the successful EHR DREAM Challenge focusing on Patient Mortality Prediction with UW Medicine. We have the goal of incorporating machine learning and predictive algorithms into clinical care and COVID-19 is an important and highly urgent challenge. In our first iteration, we will facilitate understanding risk factors that lead to a positive test utilizing electronic health recorded dat...","","https://www.synapse.org/#!Synapse:syn21849255","completed","1","https://doi.org/10.1001/jamanetworkopen.2021.24946","2020-04-30","2021-07-01","2023-06-23 00:00:00","2023-11-01 14:57:29" -"45","anti-pd1-response-prediction","Anti-PD1 Response Prediction","Predicting lung cancer response to immuno-oncology therapy","While durable responses and prolonged survival have been demonstrated in some lung cancer patients treated with immuno-oncology (I-O) anti-PD-1 therapy, there remains a need to improve the ability to predict which patients are more likely to receive benefit from treatment with I-O. The goal of this challenge is to leverage clinical and biomarker data to develop predictive models of response to I-O therapy in lung cancer and ultimately gain insights that may facilitate potential novel monotherapies or combinations with I-O.","","https://www.synapse.org/#!Synapse:syn18404605","completed","1","","2020-11-17","2021-02-25","2023-06-23 00:00:00","2023-11-02 18:25:16" -"46","brats-2021-challenge","BraTS 2021 Challenge","Developing ML methods to analyze brain tumor MRI scans","Glioblastoma, and diffuse astrocytic glioma with molecular features of glioblastoma (WHO Grade 4 astrocytoma), are the most common and aggressive malignant primary tumor of the central nervous system in adults, with extreme intrinsic heterogeneity in appearance, shape, and histology. Glioblastoma patients have very poor prognosis, and the current standard of care treatment comprises surgery, followed by radiotherapy and chemotherapy. The International Brain Tumor Segmentation (BraTS) Challenges —which have been running since 2012— assess state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans.","","https://www.synapse.org/#!Synapse:syn25829067","completed","1","","2021-07-07","2021-10-15","2023-06-23 00:00:00","2023-10-14 05:38:48" -"47","cancer-data-registry-nlp","Cancer Data Registry NLP","Predicting lung cancer response to immuno-oncology therapy","A critical bottleneck in translational and clinical research is access to large volumes of high-quality clinical data. While structured data exist in medical EHR systems, a large portion of patient information including patient status, treatments, and outcomes is contained in unstructured text fields. Research in Natural Language Processing (NLP) aims to unlock this hidden and often inaccessible information. However, numerous challenges exist in developing and evaluating NLP methods, much of it centered on having “gold-standard” metrics for evaluation, and access to data that may contain personal health information (PHI). This DREAM Challenge will focus on the development and evaluation of of NLP algorithms that can improve clinical trial matching and recruitment.","","https://www.synapse.org/#!Synapse:syn18361217","upcoming","1","","\N","\N","2023-06-23 00:00:00","2023-10-14 05:38:49" -"48","barda-community-challenge-pediatric-covid-19-data-challenge","BARDA Community Challenge - Pediatric COVID-19 Data Challenge","Models to predict severe COVID-19 in children sought","While most children with COVID-19 are asymptomatic or have mild symptoms, healthcare providers have difficulty determining which among their pediatric patients will progress to moderate or severe COVID-19 early in the progression. Some of these patients develop multisystem inflammatory syndrome in children (MIS-C), a life-threatening inflammation of organs and tissues. Methods to distinguish children at risk for severe COVID-19 complications, including conditions such as MIS-C, are needed for earlier interventions to improve pediatric patient outcomes. Multiple HHS divisions are coming together for a data challenge competition that will leverage de-identified electronic health record data to develop, train and validate computational models that can predict severe COVID-19 complications in children, equipping healthcare providers with the information and tools they need to identify pediatric patients at risk.","","https://www.synapse.org/#!Synapse:syn25875374/wiki/611225","completed","1","","2021-08-19","2021-12-17","2023-06-23 00:00:00","2023-10-14 05:38:50" -"49","brats-continuous-evaluation","BraTS Continuous Evaluation","Seeking innovations to improve brain tumor diagnosis and treatment","Brain tumors are among the deadliest types of cancer. Specifically, glioblastoma, and diffuse astrocytic glioma with molecular features of glioblastoma (WHO Grade 4 astrocytoma), are the most common and aggressive malignant primary tumor of the central nervous system in adults, with extreme intrinsic heterogeneity in appearance, shape, and histology, with a median survival of approximately 15 months. Brain tumors in general are challenging to diagnose, hard to treat and inherently resistant to conventional therapy because of the challenges in delivering drugs to the brain, as well as the inherent high heterogeneity of these tumors in their radiographic, morphologic, and molecular landscapes. Years of extensive research to improve diagnosis, characterization, and treatment have decreased mortality rates in the U.S by 7% over the past 30 years. Although modest, these research innovations have not translated to improvements in survival for adults and children in low-and middle-income...","","https://www.synapse.org/brats_ce","completed","1","","2022-01-01","\N","2023-06-23 00:00:00","2023-10-14 05:38:51" -"50","fets-2022","FeTS 2022","Federated Learning Challenge 2022: advancing brain tumor segmentation algorithms","FeTS 2022 focuses on benchmarking methods for federated learning (FL), and particularly i) weight aggregation methods for federated training, and ii) algorithmic generalizability on out-of-sample data based on federated evaluation. In line with its last instance (FeTS 2021-the 1st FL challenge ever organized), FeTS 2022 targets the task of brain tumor segmentation and builds upon i) the centralized dataset of >8,000 clinically-acquired multi-institutional MRI scans (from the RSNA-ASNR-MICCAI BraTS 2021 challenge) with their real-world partitioning, and ii) the collaborative network of remote independent institutions included in a real-world federation. Participants are welcome to compete in either of the two challenge tasks- Task 1 (“Federated Training”) seeks effective weight aggregation methods for the creation of a consensus model given a pre-defined segmentation algorithm for training, while also (optionally) accounting for network outages. Task 2 (“Federated Evaluation”) see...","","https://www.synapse.org/#!Synapse:syn28546456/wiki/617093","completed","1","","2022-04-08","2022-08-15","2023-06-23 00:00:00","2023-10-18 00:36:14" -"51","random-promotor","Random Promotor","Deciphering gene regulation: training models to predict gene expression patterns","Decoding how gene expression is regulated is critical to understanding disease. Regulatory DNA is decoded by the cell in a process termed “cis-regulatory logic”, where proteins called Transcription Factors (TFs) bind to specific DNA sequences within the genome and work together to produce as output a level of gene expression for downstream adjacent genes. This process is exceedingly complex to model as a large number of parameters is needed to fully describe the process (see Rationale, de Boer et al. 2020; Zeitingler J. 2020). Understanding the cis-regulatory logic of the human genome is an important goal and would provide insight into the origins of many diseases. However, learning models from human data is challenging due to limitations in the diversity of sequences present within the human genome (e.g. extensive repetitive DNA), the vast number of cell types that differ in how they interpret regulatory DNA, limited reporter assay data, and substantial technical biases present ...","","https://www.synapse.org/#!Synapse:syn28469146/wiki/617075","completed","1","","2022-05-02","2022-08-07","2023-06-23 00:00:00","2023-10-14 05:38:53" -"52","preterm-birth-prediction-microbiome","Preterm Birth Prediction - Microbiome","Seeking innovations to improve brain tumor diagnosis and treatment","Globally, about 11% of infants every year are born preterm, defined as birth prior to 37 weeks of gestation, totaling nearly 15 million births.(5) In addition to the emotional and financial toll on families, preterm births have higher rates of neonatal death, nearly 1 million deaths each year, and long-term health consequences for some children. Infants born preterm are at risk for a variety of adverse outcomes, such as respiratory illnesses, cerebral palsy, infections, and blindness, with infants born very preterm (i.e., before 32 weeks) at increased risk of these conditions.(6) The ability to accurately predict which women are at a higher risk for preterm birth would help healthcare providers to treat in a timely manner those at higher risk of delivering preterm. Currently available treatments for pregnant women at risk of preterm delivery include corticosteroids for fetal maturation and magnesium sulfate provided prior to 32 weeks to prevent cerebral palsy.(7) There are several...","","https://www.synapse.org/#!Synapse:syn26133770/wiki/612541","completed","1","","2022-07-19","2022-09-16","2023-06-23 00:00:00","2023-10-14 05:38:54" -"53","finrisk","FINRISK - Heart Failure and Microbiome","Predict incident risk for heart failure in a finnish adults","Cardiovascular diseases are the leading cause of death both in men and women worldwide. Heart failure (HF) is the most common form of heart disease, characterised by the heart's inability to pump a sufficient supply of blood to meet the needs of the body. The lifetime risk of developing HF is roughly 20%, yet, it remains difficult to diagnose due to its and a lack of agreement of diagnostic criteria. As the diagnosis of HF is dependent on ascertainment of clinical histories and appropriate screening of symptomatic individuals, identifying those at risk of HF is essential. This DREAM challenge focuses on the prediction of HF using a combination of gut microbiome and clinical variables. This challenge is designed to predict incident risk for heart failure in a large human population study of Finnish adults, FINRISK 2002 (Borodulin et al., 2018). The FINRISK study has been conducted in Finland to investigate the risk factors for cardiovascular disease every 5 years since 1972. A rand...","","https://www.synapse.org/#!Synapse:syn27130803/wiki/616705","completed","1","","2022-09-20","2023-01-30","2023-06-23 00:00:00","2023-11-14 19:07:49" -"54","scrna-seq-and-scatac-seq-data-analysis","scRNA-seq and scATAC-seq Data Analysis","Assess computational methods for scrna-seq and scatac-seq analysis","Understanding transcriptional regulation at individual cell resolution is fundamental to understanding complex biological systems such as tissues and organs. Emerging high-throughput sequencing technologies now allow for transcript quantification and chromatin accessibility at the single cell level. These technologies present unique challenges due to inherent data sparsity. Proper signal correction is key to accurate gene expression quantification via scRNA-seq, which propagates into downstream analyses such as differential gene expression analysis and cell-type identification. In the even more sparse scATAC-seq data, the correct identification of informative features is key to assessing cell heterogeneity at the chromatin level. The aims of this challenge will be two-fold- 1) To evaluate computational methods for signal correction and peak identification in scRNA-seq and scATAC-seq, respectively; 2) To assess the impact of these methods on downstream analysis","","https://www.synapse.org/#!Synapse:syn26720920/wiki/615338","completed","1","","2022-11-29","2023-02-08","2023-06-23 00:00:00","2023-10-14 05:38:56" -"55","cough-diagnostic-algorithm-for-tuberculosis","COugh Diagnostic Algorithm for Tuberculosis","Predict TB status using features extracted from audio of elicited coughs","Tuberculosis (TB), a communicable disease caused by Mycobacterium tuberculosis, is a major cause of ill health and one of the leading causes of death worldwide. Until the COVID-19 pandemic, TB was the leading cause of death from a single infectious agent, ranking even above HIV/AIDS. In 2020, an estimated 9.9 million people fell ill with TB and 1.3 million died of TB worldwide. However, approximately 40% of people with TB were not diagnosed or reported to public health authorities because of challenges in accessing health facilities or failure to be tested or treated when they do. The development of low-cost, non-invasive digital screening tools may improve some of the gaps in diagnosis. As cough is a common symptom of TB, it has the potential to be used as a biomarker for diagnosis of disease. Several previous studies have demonstrated the potential for cough sounds to be used to screen for TB[1-3], though these were typically done in small samples or limited settings. Further de...","","https://www.synapse.org/#!Synapse:syn31472953/wiki/617828","active","1","","2022-10-16","\N","2023-06-23 00:00:00","2023-12-06 00:58:30" -"56","nih-long-covid-computational-challenge","NIH Long COVID Computational Challenge","Understanding prevalence and outcomes of post-COVID syndrome","The overall prevalence of post-acute sequelae of SARS-CoV-2 (PASC) is currently unknown, but there is growing evidence that more than half of COVID-19 survivors experience at least one symptom of PASC/Long COVID at six months after recovery of the acute illness. Reports also reflect an underlying heterogeneity of symptoms, multi-organ involvement, and persistence of PASC/Long COVID in some patients. Research is ongoing to understand prevalence, duration, and clinical outcomes of PASC/Long COVID. Symptoms of fatigue, cognitive impairment, shortness of breath, and cardiac damage, among others, have been observed in patients who had only mild initial disease. The breadth and complexity of data created in today's health care encounters require advanced analytics to extract meaning from longitudinal data on symptoms, laboratory results, images, functional tests, genomics, mobile health/wearable devices, written notes, electronic health records (EHR), and other relevant data types. Adva...","","https://www.synapse.org/#!Synapse:syn33576900/wiki/618451","completed","1","","2022-08-25","2022-12-15","2023-06-23 00:00:00","2023-10-18 00:39:03" -"57","bridge2ai","Bridge2AI","What makes a good color palette?","What makes a good color palette?","","","upcoming","1","","\N","\N","2023-06-23 00:00:00","2023-11-20 20:19:26" -"58","rare-x-open-data-science","RARE-X Open Data Science","Unlocking rare disease mysteries through open science collaboration","The Xcelerate RARE-A Rare Disease Open Science Data Challenge is bringing together researchers and data scientists in a collaborative and competitive environment to make the best use of patient-provided data to solve big unknowns in healthcare. The Challenge will launch to researchers in late May 2023, focused on rare pediatric neurodevelopmental diseases.","","https://www.synapse.org/#!Synapse:syn51198355/wiki/621435","completed","1","","2023-05-17","2023-08-16","2023-06-23 00:00:00","2023-10-14 05:38:59" -"59","cagi5-regulation-saturation","CAGI5: Regulation saturation","Predicting effects of variants in disease-linked enhancers and promoters","17,500 single nucleotide variants (SNVs) in 5 human disease associated enhancers (including IRF4, IRF6, MYC, SORT1) and 9 promoters (including TERT, LDLR, F9, HBG1) were assessed in a saturation mutagenesis massively parallel reporter assay. Promoters were cloned into a plasmid upstream of a tagged reporter construct, and reporter expression was measured relative to the plasmid DNA to determine the impact of promoter variants. Enhancers were placed upstream of a minimal promoter and assayed similarly. The challenge is to predict the functional effects of these variants in the regulatory regions as measured from the reporter expression.","","https://genomeinterpretation.org/cagi5-regulation-saturation.html","completed","\N","","2018-01-04","2018-05-03","2023-06-23 00:00:00","2023-12-06 01:09:41" -"60","cagi5-calm1","CAGI5: CALM1","Predicting effects of calmodulin variants on yeast growth","Calmodulin is a calcium-sensing protein that modulates the activity of a large number of proteins in the cell. It is involved in many cellular processes, and is especially important for neuron and muscle cell function. Variants that affect calmodulin function have been found to be causally associated with cardiac arrhythmias. A large library of calmodulin missense variants was assessed with respect to their effects on protein function using a high-throughput yeast complementation assay. The challenge is to predict the functional effects of these calmodulin variants on competitive growth in a high-throughput yeast complementation assay.","","https://genomeinterpretation.org/cagi5-calm1.html","completed","\N","","2017-10-21","2017-12-20","2023-06-23 00:00:00","2023-10-18 15:35:49" -"61","cagi5-pcm1","CAGI5: PCM1","Assessing PCM1 variants' impact on zebrafish ventricle","The PCM1 (Pericentriolar Material 1) gene is a component of centriolar satellites occurring around centrosomes in vertebrate cells. Several studies have implicated PCM1 variants as a risk factor for schizophrenia. Ventricular enlargement is one of the most consistent abnormal structural brain findings in schizophrenia Therefore 38 transgenic human PCM1 missense mutations implicated in schizophrenia were assayed in a zebrafish model to determine their impact on the posterior ventricle area. The challenge is to predict whether variants implicated in schizophrenia impact zebrafish ventricular area.","","https://genomeinterpretation.org/cagi5-pcm1.html","completed","\N","","2017-11-09","2018-04-19","2023-06-23 00:00:00","2023-10-18 15:35:49" -"62","cagi5-frataxin","CAGI5: Frataxin","Predicting ΔΔGH20 for Frataxin Variants","Fraxatin is a highly-conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Humans with a frataxin deficiency have the cardio-and neurodegenerative disorder Friedreich's ataxia. A library of eight missense variants was assessed by near and far-UV circular dichroism and intrinsic fluorescence spectra to determine thermodynamic stability at different concentration of denaturant. These were used to calculate a ΔΔGH20 value, the difference in unfolding free energy (ΔGH20) between the mutant and wild-type proteins for each variant. The challenge is to predict ΔΔGH20 for each frataxin variant.","","https://genomeinterpretation.org/cagi5-frataxin.html","completed","\N","","2017-11-30","2018-04-18","2023-06-23 00:00:00","2023-10-18 15:35:50" -"63","cagi5-tpmt","CAGI5: TPMT and p10","Predicting TPMT and PTEN protein stability variants","The gene p10 encodes for PTEN (Phosphatase and TEnsin Homolog), an important secondary messenger molecule promoting cell growth and survival through signaling cascades including those controlled by AKT and mTOR. Thiopurine S-methyl transferase (TPMT) is a key enzyme involved in the metabolism of thiopurine drugs and functions by catalyzing the S-methylation of aromatic and heterocyclic sulfhydryl groups. A library of thousands of PTEN and TPMT mutations was assessed to measure the stability of the variant protein using a multiplexed variant stability profiling (VSP) assay, which detects the presence of EGFP fused to the mutated PTEN and TPMT protein respectively. The stability of the variant protein dictates the abundance of the fusion protein and thus the EGFP level of the cell. The challenge is to predict the effect of each variant on TPMT and/or PTEN protein stability.","","https://genomeinterpretation.org/cagi5-tpmt.html","completed","\N","","2017-11-30","2017-12-01","2023-06-23 00:00:00","2023-10-14 05:39:03" -"64","cagi5-annotate-all-missense","CAGI5: Annotate all nonsynonymous variants","Annotate all nonsynonymous variants","dbNSFP describes 810,848,49 possible protein-altering variants in the human genome. The challenge is to predict the functional effect of every such variant. For the vast majority of these missense variants, the functional impact is not currently known, but experimental and clinical evidence are accruing rapidly. Rather than drawing upon a single discrete dataset as typical with CAGI, predictions will be assessed by comparing with experimental or clinical annotations made available after the prediction submission date, on an ongoing basis. if predictors assent, predictions will also incorporated into dbNSFP.","","https://genomeinterpretation.org/cagi5-annotate-all-missense.html","completed","\N","","2017-11-30","2018-05-09","2023-06-23 00:00:00","2023-10-14 05:39:04" -"65","cagi5-gaa","CAGI5: GAA","Predict enzyme activity of GAA mutants in Pompe disease","Acid alpha-glucosidase (GAA) is a lysosomal alpha-glucosidase. Some mutations in GAA cause a rare disorder, Pompe disease, (Glycogen Storage Disease II). Rare GAA missense variants found in a human population sample have been assayed for enzymatic activity in transfected cell lysates. The assessment of this challenge will include evaluations that recognize novelty of approach. The challenge is to predict the fractional enzyme activity of each mutant protein compared to the wild-type enzyme.","","https://genomeinterpretation.org/cagi5-gaa.html","completed","\N","","2017-11-09","2018-04-25","2023-06-23 00:00:00","2023-10-14 05:39:04" -"66","cagi5-chek2","CAGI5: CHEK2","Estimate CHEK2 gene variant probabilities in Latino breast cancer cases","Variants in the CHEK2 gene are associated with breast cancer. This challenge includes CHEK2 gene variants from approximately 1200 Latino breast cancer cases and 1200 ethnically matched controls. This challenge is to estimate the probability of each gene variant occurring in an individual from the cancer affected cohort.","","https://genomeinterpretation.org/cagi5-chek2.html","completed","\N","","2017-12-20","2018-04-24","2023-06-23 00:00:00","2023-10-14 05:39:07" -"67","cagi5-enigma","CAGI5: ENIGMA","Predict cancer risk from BRCA1/2 gene variants","Breast cancer is the most prevalent cancer among women worldwide. The association between germline mutations in the BRCA1 and BRCA2 genes and the development of cancer has been well established. The most common high-risk mutations associated with breast cancer are those in the autosomal dominant breast cancer genes 1 and 2 (BRCA1 and BRCA2). Mutations in these genes are found in 1-3% of breast cancer cases. The challenge is to predict which variants are associated with increased risk for breast cancer.","","https://genomeinterpretation.org/cagi5-enigma.html","completed","\N","","2017-12-20","2018-05-01","2023-06-23 00:00:00","2023-10-14 05:39:08" -"68","cagi5-mapsy","CAGI5: MaPSy","Predict the impact of genetic variants on splicing mechanisms","The Massively Parallel Splicing Assay (MaPSy) approach was used to screen 797 reported exonic disease mutations using a mini-gene system, assaying both in vivo via transfection in tissue culture, and in vitro via incubation in cell nuclear extract. The challenge is to predict the degree to which a given variant causes changes in splicing.","","https://genomeinterpretation.org/cagi5-mapsy.html","completed","\N","","2017-11-29","2018-05-07","2023-06-23 00:00:00","2023-10-14 05:39:08" -"69","cagi5-vex-seq","CAGI5: Vex-seq","Predict splicing changes from variants in globin gene","A barcoding approach called Variant exon sequencing (Vex-seq) was applied to assess effect of 2,059 natural single nucleotide variants and short indels on splicing of a globin mini-gene construct transfected into HepG2 cells. This is reported as ΔΨ (delta PSI, or Percent Spliced In), between the variant Ψand the reference Ψ. The challenge is to predict ΔΨ for each variant.","","https://genomeinterpretation.org/cagi5-vex-seq.html","completed","\N","","2017-12-14","2018-05-02","2023-06-23 00:00:00","2023-10-16 17:51:58" -"70","cagi5-sickkids5","CAGI5: SickKids clinical genomes","Predict genetic disorders from 30 child genomes and phenotypes","This challenge involves 30 children with suspected genetic disorders who were referred for clinical genome sequencing. Predictors are given the 30 genome sequences, and are also provided with the phenotypic descriptions as shared with the diagnostic laboratory. The challenge is to predict what class of disease is associated with each genome, and which genome corresponds to which clinical description. Predictors may additionally identify the diagnostic variant(s) underlying the predictions, and identify predictive secondary variants conferring high risk of other diseases whose phenotypes are not reported in the clinical descriptions.","","https://genomeinterpretation.org/cagi5-sickkids5.html","completed","\N","","2017-12-22","2018-04-26","2023-06-23 00:00:00","2023-10-14 05:39:10" -"71","cagi5-intellectual-disability","CAGI5: ID Panel","Predict phenotypes and variants from gene panel sequences","The challenge presented here is to use computational methods to predict a patient's clinical phenotype and the causal variant(s) based on analysis of their gene panel sequence data. Sequence data for 74 genes associated with intellectual disability (ID) and/or Autism spectrum disorders (ASD) from a cohort of 150 patients with a range of neurodevelopmental presentations (ID, autism, epilepsy, etc..) have been made available for this challenge. For each patient, predictors must report the causative variants and which of seven phenotypes are present.","","https://genomeinterpretation.org/cagi5-intellectual-disability.html","completed","\N","","2017-12-22","2018-04-30","2023-06-23 00:00:00","2023-10-18 15:28:06" -"72","cagi5-clotting-disease","CAGI5: Clotting disease exomes","Predict venous thromboembolism risk in African Americans","African Americans have a higher incidence of developing venous thromboembolisms (VTE), which includes deep vein thrombosis (DVT) and pulmonary embolism (PE), than people of European ancestry. Participants are provided with exome data and clinical covariates for a cohort of African Americans who have been prescribed Warfarin either because they had experienced a VTE event or had been diagnosed with atrial fibrillation (which predisposes to clotting). The challenge is to distinguish between these conditions. At present, in contrast to European ancestry, there are no genetic methods for anticipating which African Americans are most at risk of a venous thromboembolism, and the results of this challenge may contribute to the development of such tools.","","https://genomeinterpretation.org/cagi5-clotting-disease.html","completed","\N","","2017-11-23","2018-04-28","2023-06-23 00:00:00","2023-10-18 15:30:55" -"73","cagi6-sickkids","CAGI6: SickKids clinical genomes and transcriptomes","Identify genes causing rare diseases using transcriptomics","This challenge involves data from 79 children who were referred to The Hospital for Sick Children's (SickKids) Genome Clinic for genome sequencing because of suspected but undiagnosed genetic disorders. Research subjects are consented for sharing of their sequence data and phenotype information with researchers working to understand the molecular causes of rare disease. When a candidate disease variant believed to be related to the phenotype is identified, the variant is adjudicated and confirmed in a clinical setting. In this challenge, transcriptomic and phenotype data from a subset of the “solved” (diagnosed) and “unsolved” SickKids patients will be provided, along with corresponding genomic sequence data. The challenge is to use a transcriptome-driven approach to identify the gene(s) and molecular mechanisms underlying the phenotypic descriptions in each case. For the unsolved cases, prioritized variants from the participating teams will be examined to see if additional diagno...","","https://genomeinterpretation.org/cagi6-sickkids.html","completed","1","","2021-08-04","2021-12-31","2023-06-23 00:00:00","2023-11-02 18:02:23" -"74","cagi6-cam","CAGI6: CaM","Predict the impact of point mutations on calmodulin stability","Calmodulin (CaM) is a ubiquitous calcium (Ca2+) sensor protein interacting with more than 200 molecular partners, thereby regulating a variety of biological processes. Missense point mutations in the genes encoding CaM have been associated with ventricular tachycardia and sudden cardiac death. A library encompassing up to 17 point mutations was assessed by far-UV circular dichroism (CD) by measuring melting temperature (Tm) and percentage of unfolding (%unfold) upon thermal denaturation at pH and salt concentration that mimic the physiological conditions. The challenge is to predict: the Tm and %unfold values for isolated CaM variants under Ca2+-saturating conditions (Ca2+-CaM) and in the Ca2+-free (apo) state; whether the point mutation stabilizes or destabilizes the protein (based on Tm and %unfold).","","https://genomeinterpretation.org/cagi6-cam.html","completed","1","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-18 15:32:37" -"75","cami-ii","CAMI II","Assemble and classify microbial genomes in complex samples","CAMI II offers several challenges-an assembly, a genome binning, a taxonomic binning and a taxonomic profiling challenge, on several multi-sample data sets from different environments, including long and short read data. This includes a marine data set and a high-strain diversity data set, with a third data set to follow later. A pathogen detection challenge on a clinical sample is also provided.","","https://www.microbiome-cosi.org/cami/cami/cami2","completed","3","","2019-01-14","2021-01-31","2023-06-23 00:00:00","2023-10-17 23:15:00" -"76","camda18-metasub-forensics","CAMDA18-MetaSUB Forensics","Build a metagenomic map of mass-transit systems globally","The MetaSUB International Consortium is building a longitudinal metagenomic map of mass-transit systems and other public spaces across the globe. The consortium maintains a strategic partnership with CAMDA and this year provides data from global City Sampling Days for the first-ever multi-city forensic analyses.","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#metasub_forensics_challenge","completed","\N","","\N","\N","2023-06-23 00:00:00","2023-11-01 20:37:34" -"77","camda18-cmap-drug-safety","CAMDA18-CMap Drug Safety","Predict drug toxicity using cell-based gene expression data","Attrition in drug discovery and development due to safety / toxicity issues remains a significant concern, and there are strong efforts to identify and mitigate risk as early as possible. Drug-induced liver injury (DILI) is one of the primary problems in drug development and regulatory clearance due to the poor performance of existing preclinical models. There is a pressing need to evaluate alternative methods for predicting DILI, with great hopes being placed in modern approaches from statistics and machine learning applied to genome scale profiling data. A critical question thus is if we can better integrate, understand, and exploit information from cell-based screens like the Broad Institute Connectivity Map (CMap, Science 313, Nature Reviews Cancer 7). This CAMDA challenge focuses on understanding or predicting drug induced liver injury in humans from cell-based screens, specifically the CMap gene expression responses of two different cancer cell lines (MCF7 and PC3) to 276 d...","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#cmap_drug_safety_challenge","completed","\N","","\N","\N","2023-06-23 00:00:00","2023-11-01 20:37:35" -"78","camda18-cancer-data-integration","CAMDA18-Cancer Data Integration","Unify data integration approaches for breast cancer and neuroblastoma","Examine the power of data integration in a real-world clinical settings. Many approaches work well on some data-sets yet not on others. We here challenge you to demonstrate a unified single approach to data-integration that matches or outperforms the current state of the art on two different diseases, breast cancer and neuroblastoma. Breast cancer affects about 3 million women every year (McGuire et al, Cancers 7), and this number is growing fast, especially in developed countries. Can you improve on the large Metabric study (Curtis et al., Nature 486, and Dream Challenge, Margolin et al, Sci Transl Med 5)? The cohort is biologically heterogeneous with all five distinct PAM50 breast cancer subtypes represented. Matched profiles for microarray and copy number data as well as clinical information (survival times, multiple prognostic markers, therapy data) are available for about 2,000 patients. Neuroblastoma is the most common extracranial solid tumor in children. The base study com...","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#cancer_data_integration_challenge","completed","\N","","\N","\N","2023-06-23 00:00:00","2023-11-01 20:37:36" -"79","cafa-4","CAFA 4","Assess algorithms for predicting protein function","The goal of the Critical Assessment of Functional Annotation(CAFA) challenge is to evaluate automated protein function prediction algorithms in the task of predicting Gene Ontology and Human Phenotype Ontology terms for a given set of protein sequences. For the GO-based predictions, the evaluation will be carried out for the Molecular Function Ontology, Biological Process Ontology and Cellular Component Ontology. Participants develop protein function prediction algorithms using training protein sequence data and submit their predictions on target protein sequence data.","","https://www.biofunctionprediction.org/cafa/","completed","1","","2019-10-21","2020-02-12","2023-06-23 00:00:00","2023-10-14 05:39:20" -"80","casp13","CASP13","CASP assesses protein structure prediction methods","CASP (Critical Assessment of Structure Prediction) is a community wide experiment to determine and advance the state of the art in modeling protein structure from amino acid sequence. Every two years, participants are invited to submit models for a set of proteins for which the experimental structures are not yet public. Independent assessors then compare the models with experiment. Assessments and results are published in a special issue of the journal PROTEINS. In the most recent CASP round, CASP12, nearly 100 groups from around the world submitted more than 50,000 models on 82 modeling targets","","https://predictioncenter.org/casp13/index.cgi","completed","\N","","2018-04-18","2018-08-20","2023-06-23 00:00:00","2023-10-17 22:52:29" -"81","casp14","CASP14","Assess progress in protein structure prediction","CASP (Critical Assessment of Structure Prediction) is a community wide experiment to determine and advance the state of the art in modeling protein structure from amino acid sequence. Every two years, participants are invited to submit models for a set of proteins for which the experimental structures are not yet public. Independent assessors then compare the models with experiment. Assessments and results are published in a special issue of the journal PROTEINS. In the most recent CASP round, CASP14, nearly 100 groups from around the world submitted more than 67,000 models on 90 modeling targets.","","https://predictioncenter.org/casp14/index.cgi","completed","\N","","2020-05-04","2020-09-07","2023-06-23 00:00:00","2023-10-17 22:47:26" -"82","cfsan-pathogen-detection","CFSAN Pathogen Detection","Rapidly identify food sources of outbreaks","In the U.S. alone, one in six individuals, an estimated 48 million people, fall prey to foodborne illness, resulting in 128,000 hospitalizations and 3,000 deaths per year. Economic burdens are estimated cumulatively at $152 billion dollars annually, including $39 billion due to contamination of fresh and processed produce. One longstanding problem is the ability to rapidly identify the food-source associated with the outbreak being investigated. The faster an outbreak is identified and the increased certainty that a given source (e.g., papayas from Mexico) and patients are linked, the faster the outbreak can be stopped, limiting morbidity and mortality. In the last few years, the application of next-generation sequencing (NGS) technology for whole genome sequencing (WGS) of foodborne pathogens has revolutionized food pathogen outbreak surveillance. WGS of foodborne pathogens enables high-resolution identification of pathogens isolated from food or environmental samples. These pat...","","https://precision.fda.gov/challenges/2","completed","6","","2018-02-15","2018-04-26","2023-06-23 00:00:00","2023-10-14 05:39:23" -"83","cdrh-biothreat","CDRH Biothreat","Identify infectious diseases from clinical samples using sequencing technology","Many infectious diseases have similar signs and symptoms, making it challenging for healthcare providers to identify the disease-causing agent. Clinical samples are often tested by multiple test methods to help reveal the microbe that is causing the infectious disease. The results of these test methods can help healthcare professionals determine the best treatment for patients. Today, High-Throughput Sequencing (HTS) or Next Generation Sequencing (NGS) technology has the capability, as a single test, to accomplish what might have required several different tests in the past. NGS technology may allow the diagnosis of infections without prior knowledge of disease(s) cause. NGS technology can potentially reveal the presence of all microorganisms in a patient sample. Using infectious disease NGS (ID-NGS) technology, each microbial pathogen may be identified by its unique genomic fingerprint. The vision of ID-NGS technology is to further improve patient care by delivering diagnostics ...","","https://precision.fda.gov/challenges/3","completed","6","","2018-08-03","2018-10-18","2023-06-23 00:00:00","2023-10-14 05:39:24" -"84","multi-omics-enabled-sample-mislabeling-correction","Multi-omics Enabled Sample Mislabeling Correction","Identify and correct sample and data mislabeling events","In biomedical research, sample mislabeling (accidental swapping of patient samples) or data mislabeling (accidental swapping of patient omics data) has been a long-standing problem that contributes to irreproducible results and invalid conclusions. These problems are particularly prevalent in large scale multi-omics studies, in which multiple different omics experiments are carried out at different time periods and/or in different labs. Human errors could arise during sample transferring, sample tracking, large-scale data generation, and data sharing/management. Thus, there is a pressing need to identify and correct sample and data mislabeling events to ensure the right data for the right patient. Simultaneous use of multiple types of omics platforms to characterize a large set of biological samples, as utilized in The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects, has been demonstrated as a powerful approach to understanding the ...","","https://precision.fda.gov/challenges/4","completed","6","https://doi.org/10.1038/s41591-018-0180-x","2018-09-24","2018-12-19","2023-06-23 00:00:00","2023-11-14 19:07:58" -"85","biocompute-object-app-a-thon","BioCompute Object App-a-thon","Seeking standards for reproducible bioinformatics analysis","Like scientific laboratory experiments, bioinformatics analysis results and interpretation are faced with reproducibility challenges due to the variability in multiple computational parameters, including input format, prerequisites, platform dependencies, and more. Even small changes in these computational parameters may have a large impact on the results and carry big implications for their scientific validity. Because there are currently no standardized schemas for reporting computational scientific workflows and parameters together with their results, the ways in which these workflows are communicated is highly variable, incomplete, and difficult or impossible to reproduce. The US Food and Drug Administration (FDA) High Performance Virtual Environment (HIVE) group and George Washington University (GW) have partnered to establish a framework for community-based standards development and harmonization of high-throughput sequencing (HTS) computations and data formats based arou...","","https://precision.fda.gov/challenges/7/","completed","6","https://doi.org/10.1101/2020.11.02.365528","2019-05-14","2019-10-18","2023-06-23 00:00:00","2023-10-14 05:39:25" -"86","brain-cancer-predictive-modeling-and-biomarker-discovery","Brain Cancer Predictive Modeling and Biomarker Discovery","Seeking novel biomarkers to advance precision medicine for brain tumors","An estimated 86,970 new cases of primary brain and other central nervous system tumors are expected to be diagnosed in the US in 2019. Brain tumors comprise a particularly deadly subset of all cancers due to limited treatment options and the high cost of care. Only a few prognostic and predictive markers have been successfully implemented in the clinic so far for gliomas, the most common malignant brain tumor type. These markers include MGMT promoter methylation in high-grade astrocytomas, co-deletion of 1p/19q in oligodendrogliomas, and mutations in IDH1 or IDH2 genes (Staedtke et al. 2016). There remains significant potential for identifying new clinical biomarkers in gliomas. Clinical investigators at Georgetown University are seeking to advance precision medicine techniques for the prognosis and treatment of brain tumors through the identification of novel multi-omics biomarkers. In support of this goal, precisionFDA and the Georgetown Lombardi Comprehensive Cancer Center and ...","","https://precision.fda.gov/challenges/8/","completed","6","","2019-11-01","2020-02-14","2023-06-23 00:00:00","2023-10-14 05:39:25" -"87","gaining-new-insights-by-detecting-adverse-event-anomalies","Gaining New Insights by Detecting Adverse Event Anomalies","Seeking algorithms to detect adverse events in FDA data","The Food and Drug Administration (FDA) calls on the public to develop computational algorithms for automatic detection of adverse event anomalies using publicly available data.","","https://precision.fda.gov/challenges/9/","completed","6","","2020-01-17","2020-05-18","2023-06-23 00:00:00","2023-10-14 05:39:27" -"88","calling-variants-in-difficult-to-map-regions","Calling Variants in Difficult-to-Map Regions","Precision benchmarking: evaluating variant calling in complex genomic regions","This challenge calls on the public to assess variant calling pipeline performance on a common frame of reference, with a focus on benchmarking in difficult-to-map regions, segmental duplications, and the Major Histocompatibility Complex (MHC).","","https://precision.fda.gov/challenges/10/","completed","6","https://doi.org/10.1016/j.xgen.2022.100129","2020-05-01","2020-06-15","2023-06-23 00:00:00","2023-10-14 05:39:28" -"89","vha-innovation-ecosystem-and-covid-19-risk-factor-modeling","VHA Innovation Ecosystem and COVID-19 Risk Factor Modeling","AI for COVID-19: predicting health outcomes in the veteran population","The novel coronavirus disease 2019 (COVID-19) is a respiratory disease caused by a new type of coronavirus, known as “severe acute respiratory syndrome coronavirus 2,” or SARS-CoV-2. On March 11, 2020, the World Health Organization (WHO) declared the outbreak a global pandemic. As of Monday, June 1, the Johns Hopkins University COVID-19 dashboard reports over 6.21 million total confirmed cases worldwide, including over 1.79 million cases in the United States. Although most people have mild to moderate symptoms, the disease can cause severe medical complications leading to death in some people. The Centers for Disease Control and Prevention (CDC) have identified several groups at elevated risk for severe illness, including people 65 years and older, individuals living in nursing homes or long term care facilities, and those with serious underlying medical conditions, such as severe obesity, diabetes, chronic lung disease or moderate to severe asthma, chronic kidney or liver disease...","","https://precision.fda.gov/challenges/11/","completed","6","","2020-06-02","2020-07-03","2023-06-23 00:00:00","2023-10-14 05:39:28" -"90","covid-19-precision-immunology-app-a-thon","COVID-19 Precision Immunology App-a-thon","Seeking insights on COVID-19 pathophysiology to enable effective strategies","The novel coronavirus disease 2019 (COVID-19), a respiratory disease caused by a new type of coronavirus, known as “severe acute respiratory syndrome coronavirus 2” or SARS-CoV-2, was declared a global pandemic by the World Health Organization on March 11, 2020. To date, the Johns Hopkins University COVID-19 dashboard reports over 62 million confirmed cases worldwide, with a wide range of disease severity from asymptomatic to deaths (over 1.46 million). To effectively combat the widespread transmission of COVID-19 infection and save lives especially of those vulnerable individuals, it is imperative to better understand its pathophysiology to enable effective diagnosis, prognosis and treatment strategies using rapidly shared data.","","https://precision.fda.gov/challenges/12/","completed","6","","2020-11-30","2021-01-29","2023-06-23 00:00:00","2023-10-14 05:39:29" -"91","smarter-food-safety-low-cost-tech-enabled-traceability","Smarter Food Safety Low Cost Tech-Enabled Traceability","Seeking affordable tech solutions for food traceability","The motivation is tapping into new technologies and integrating data streams will help to advance the widespread, consistent implementation of traceability systems across the food industry. However, the affordability of such technologies, particularly for smaller companies, can be a barrier to implementing tech-enabled traceability systems. FDA's New Era of Smarter Food Safety initiative strives to work with stakeholders to explore low-cost or no-cost options so that our approaches are inclusive of and viable for human and animal food operations of all sizes. Democratizing the benefits of digitizing data will allow the entire food system to move more rapidly towards digital traceability systems. The primary goal is to encourage stakeholders, including technology providers, public health advocates, entrepreneurs, and innovators from all disciplines and around the world, to develop traceability hardware, software, or data analytics platforms that are low-cost or no-cost to the en...","","https://precision.fda.gov/challenges/13","completed","6","","2021-06-01","2021-07-30","2023-06-23 00:00:00","2023-10-17 23:05:49" -"92","tumor-mutational-burden-tmb-challenge-phase-1","Tumor Mutational Burden (TMB) Challenge Phase 1","Standardize tumor mutational burden (TMB) calculation in cancer research","Tumor mutational burden (TMB) is generally defined as the number of mutations detected in a patient's tumor sample per megabase of DNA sequenced. However different algorithms use different methods for calculating TMB. Mutations in genes in tumor cells may lead to the creation of neoantigens, which have the potential to activate an immune system response against the tumor, and the likelihood of an immune system response may increase with the number of mutations. Thus, TMB is a biomarker for some immunotherapy drugs, called immune checkpoint inhibitors, such as those that target the PD-1 and PD-L1 pathways (Chan et al., 2019). An outstanding problem is the lack of standardization for TMB calculation and reporting between different assays. To address this problem, the Friends of Cancer Research convened a working group of industry and regulatory stakeholders to develop guidance and tools for TMB harmonization. Results from the first phase of this effort were presented at AACR 2020 (...","","https://precision.fda.gov/challenges/17","completed","6","","2021-06-21","2021-09-13","2023-06-23 00:00:00","2023-11-02 18:28:46" -"93","kits21","KiTS21","Contest seeks best kidney tumor segmentation system","The 2021 Kidney and Kidney Tumor Segmentation challenge (abbreviated KiTS21) is a competition in which teams compete to develop the best system for automatic semantic segmentation of renal tumors and surrounding anatomy. Kidney cancer is one of the most common malignancies in adults around the world, and its incidence is thought to be increasing [1]. Fortunately, most kidney tumors are discovered early while they're still localized and operable. However, there are important questions concerning management of localized kidney tumors that remain unanswered [2], and metastatic renal cancer remains almost uniformly fatal [3]. Kidney tumors are notorious for their conspicuous appearance in computed tomography (CT) imaging, and this has enabled important work by radiologists and surgeons to study the relationship between tumor size, shape, and appearance and its prospects for treatment [4,5,6]. It's laborious work, however, and it relies on assessments that are often subjective and impr...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/594/rendering_dimmed.png","https://kits21.grand-challenge.org/","completed","5","","2021-08-23","2021-09-17","2023-06-23 00:00:00","2023-11-28 00:32:11" -"94","realnoisemri","RealNoiseMRI","Brain MRI reconstruction challenge with realistic noise","In recent years, there is a growing focus on the application of fast magnetic resonance imaging (MRI) based on prior knowledge. In the 1980s and 2000s the community used either purely mathematical models such as the partial Fourier transform or solutions derived through advanced engineering such as parallel imaging to speed up MRI acquisition. Since the mid-2000's, compressed sensing and artificial intelligence have been employed to speed up MRI acquisition. These newer methods rely on under sampling the data acquired in Fourier (aka k-) space and then interpolating or augmenting k-space data based on training data content. One of the underlying problems for the development of fast imaging techniques, that just as in e.g. [1], it is common to use a fully sampled image as ground truth and then under sample it in k-space in order to simulate under sampled data. The problem with this approach is that in cases were the under sampled data is corrupted, through e.g. motion, this under s...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/597/Logo_Challenge.png","https://realnoisemri.grand-challenge.org/","completed","5","","2021-09-21","2021-12-06","2023-06-23 00:00:00","2023-11-27 20:40:05" -"95","deep-generative-model-challenge-for-da-in-surgery","Deep Generative Model Challenge for DA in Surgery","Challenge aims to adapt algorithms from simulation to mitral valve surgery","Mitral regurgitation (MR) is the second most frequent indication for valve surgery in Europe and may occur for organic or functional causes [1]. Mitral valve repair, although considerably more difficult, is prefered over mitral valve replacement, since the native tissue of the valve is preserved. It is a complex on-pump heart surgery, often conducted only by a handful of surgeons in high-volume centers. Minimally invasive procedures, which are performed with endoscopic video recordings, became more and more popular in recent years. However, data availability and data privacy concerns are still an issue for the development of automatic scene analysis algorithms. The AdaptOR challenge aims to address these issues by formulating a domain adaptation problem from simulation to surgery. We provide a smaller number of datasets from real surgeries, and a larger number of annotated recordings of training and planning sessions from a physical mitral valve simulator. The goal is to reduce th...","","https://adaptor2021.github.io/","completed","1","","2021-04-01","2021-07-16","2023-06-23 00:00:00","2023-10-14 05:39:34" -"96","aimdatathon","AIM Datathon 2020","AI in Medicine (AIM) Datathon 2020","Join the AI in Medicine ( AIM ) Datathon 2020","","https://www.kaggle.com/competitions/aimdatathon","completed","8","","2020-11-09","2020-11-22","2023-06-23 00:00:00","2023-11-15 22:43:19" -"97","opc-recurrence","Oropharynx Cancer (OPC) Radiomics Challenge :: Local Recurrence Prediction","Determine whether a tumor will be controlled by definitive radiation therapy","Determine from CT data whether a tumor will be controlled by definitive radiation therapy.","","https://www.kaggle.com/competitions/opc-recurrence","completed","8","","2016-07-26","2016-09-12","2023-06-23 00:00:00","2023-11-14 19:11:07" -"98","oropharynx-radiomics-hpv","Oropharynx Cancer (OPC) Radiomics Challenge :: Human Papilloma Virus (HPV) Status Prediction","Predict hpv phenotype of oropharynx tumors; compare to ground truth data","Predict from CT data the HPV phenotype of oropharynx tumors; compare to ground-truth results previously obtained by p16 or HPV testing.","","https://www.kaggle.com/competitions/oropharynx-radiomics-hpv","completed","8","","2016-07-26","2016-09-12","2023-06-23 00:00:00","2023-11-14 19:11:17" -"99","data-science-bowl-2017","Data Science Bowl 2017","Can you improve lung cancer detection?","Can you improve lung cancer detection?","","https://www.kaggle.com/competitions/data-science-bowl-2017","completed","8","","2017-01-12","2017-04-12","2023-06-23 00:00:00","2023-10-14 05:39:38" -"100","predict-impact-of-air-quality-on-death-rates","Predict impact of air quality on mortality rates","Predict CVD and cancer caused mortality rates in England using air quality data","Predict CVD and cancer caused mortality rates in England using air quality data available from Copernicus Atmosphere Monitoring Service","","https://www.kaggle.com/competitions/predict-impact-of-air-quality-on-death-rates","completed","8","","2017-02-13","2017-05-05","2023-06-23 00:00:00","2023-10-14 05:39:38" -"101","intel-mobileodt-cervical-cancer-screening","Intel & MobileODT Cervical Cancer Screening","Which cancer treatment will be most effective?","Which cancer treatment will be most effective?","","https://www.kaggle.com/competitions/intel-mobileodt-cervical-cancer-screening","completed","8","","2017-03-15","2017-06-21","2023-06-23 00:00:00","2023-10-14 05:39:39" -"102","msk-redefining-cancer-treatment","Personalized Medicine-Redefining Cancer Treatment","Predict the effect of genetic variants to enable personalized medicine","Predict the effect of Genetic Variants to enable Personalized Medicine","","https://www.kaggle.com/competitions/msk-redefining-cancer-treatment","completed","8","","2017-06-26","2017-10-02","2023-06-23 00:00:00","2023-11-02 18:32:51" -"103","mubravo","Predicting Cancer Diagnosis","Bravo's machine learning competition!","Bravo's machine learning competition!","","https://www.kaggle.com/competitions/mubravo","completed","8","","2018-07-31","2018-08-13","2023-06-23 00:00:00","2023-10-14 05:39:41" -"104","histopathologic-cancer-detection","Histopathologic Cancer Detection","Identify metastatic tissue in histopathologic scans of lymph node sections","Identify metastatic tissue in histopathologic scans of lymph node sections","","https://www.kaggle.com/competitions/histopathologic-cancer-detection","completed","8","","2018-11-16","2019-03-30","2023-06-23 00:00:00","2023-10-14 05:39:41" -"105","tjml1920-decision-trees","TJML 2019-20 Breast Cancer Detection Competition","Use a decision tree to identify malignant breast cancer tumors","Use a decision tree to identify malignant breast cancer tumors","","https://www.kaggle.com/competitions/tjml1920-decision-trees","completed","8","","2019-09-22","2019-10-16","2023-06-23 00:00:00","2023-10-14 05:39:42" -"106","prostate-cancer-grade-assessment","Prostate cANcer graDe Assessment (PANDA) Challenge","Prostate cancer diagnosis using the gleason grading system","Prostate cancer diagnosis using the Gleason grading system","","https://www.kaggle.com/competitions/prostate-cancer-grade-assessment","completed","8","","2020-04-21","2020-07-22","2023-06-23 00:00:00","2023-10-14 05:39:43" -"107","breast-cancer","Breast Cancer","Use cell nuclei categories to predict breast cancer tumor","Use cell nuclei categories to predict breast cancer tumor.","","https://www.kaggle.com/competitions/breast-cancer","completed","8","","2020-08-12","2020-08-13","2023-06-23 00:00:00","2023-10-14 05:39:43" -"108","breast-cancer-detection","Breast Cancer Detection","Breast cancer detection","breast cancer detection","","https://www.kaggle.com/competitions/breast-cancer-detection","completed","8","","2020-09-25","2020-12-31","2023-06-23 00:00:00","2023-10-14 05:39:44" -"109","hrpred","Prediction of High Risk Patients","Classification of high and low risk cancer patients","Classification of high and low risk cancer patients","","https://www.kaggle.com/competitions/hrpred","completed","8","","2020-11-25","2020-12-05","2023-06-23 00:00:00","2023-10-14 05:39:44" -"110","ml4moleng-cancer","MIT ML4MolEng-Predicting Cancer Progression","MIT 3.100, 10.402, 20.301 In class ML competition (Spring 2021)","MIT 3.100, 10.402, 20.301 In class ML competition (Spring 2021)","","https://www.kaggle.com/competitions/ml4moleng-cancer","completed","8","","2021-05-06","2021-05-21","2023-06-23 00:00:00","2023-11-16 18:41:14" -"111","uw-madison-gi-tract-image-segmentation","UW-Madison GI Tract Image Segmentation","Track healthy organs in medical scans to improve cancer treatment","Track healthy organs in medical scans to improve cancer treatment","","https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation","completed","8","","2022-04-14","2022-07-14","2023-06-23 00:00:00","2023-10-14 05:39:46" -"112","rsna-miccai-brain-tumor-radiogenomic-classification","RSNA-MICCAI Brain Tumor Radiogenomic Classification","Predict the status of a genetic biomarker important for brain cancer treatment","The Brain Tumor Segmentation (BraTS) challenge celebrates its 10th anniversary, and this year is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society. The RSNA-ASNR-MICCAI BraTS 2021 challenge utilizes multi-institutional pre-operative baseline multi-parametric magnetic resonance imaging (mpMRI) scans, and focuses on the evaluation of state-of-the-art methods for (Task 1) the segmentation of intrinsically heterogeneous brain glioblastoma sub-regions in mpMRI scans. Furthemore, this BraTS 2021 challenge also focuses on the evaluation of (Task 2) classification methods to predict the MGMT promoter methylation status. Participants are free to choose whether they want to focus only on one or both tasks.","","https://www.kaggle.com/competitions/rsna-miccai-brain-tumor-radiogenomic-classification","completed","8","","2021-07-13","2021-10-15","2023-06-23 00:00:00","2023-10-14 05:39:46" -"113","breastcancer","Breast Cancer - Beginners ML","Beginners hands-on experience with ML basics","Beginners hands-on experience with ML basics","","https://www.kaggle.com/competitions/breastcancer","completed","8","","2021-12-21","2022-02-12","2023-06-23 00:00:00","2023-10-18 21:18:15" -"114","ml-olympiad-health-and-education","ML Olympiad -Let's Fight lung cancer","Use your ml expertise to help us step another step toward defeating cancer","Use your ML expertise to help us step another step toward defeating cancer [ Starts on the 14th February ]","","https://www.kaggle.com/competitions/ml-olympiad-health-and-education","completed","8","","2022-01-31","2022-03-19","2023-06-23 00:00:00","2023-11-15 18:45:55" -"115","cs98-22-dl-task1","CS98X-22-DL-Task1","This competition is related to task 1 in coursework-breast cancer classification","This competition is related to Task 1 in coursework-breast cancer classification","","https://www.kaggle.com/competitions/CS98-22-DL-Task1","completed","8","","2022-02-28","2022-04-11","2023-06-23 00:00:00","2023-10-14 05:39:48" -"116","parasitedetection-iiitb2019","Parasite detection","Detect if cell image has parasite or is uninfected","detect if cell image has parasite or is uninfected","","https://www.kaggle.com/competitions/parasitedetection-iiitb2019","completed","8","","2019-10-13","2019-11-25","2023-06-23 00:00:00","2023-10-14 05:39:49" -"117","hpa-single-cell-image-classification","Human Protein Atlas -Single Cell Classification","Find individual human cell differences in microscope images","Find individual human cell differences in microscope images","","https://www.kaggle.com/competitions/hpa-single-cell-image-classification","completed","8","","2021-01-26","2021-05-11","2023-06-23 00:00:00","2023-10-14 05:39:50" -"118","stem-cell-predcition","Stem Cell Predcition","Classify stem and non-stem cells using RNA-seq data","Classify stem and non-stem cells using RNA-seq data","","https://www.kaggle.com/competitions/stem-cell-predcition","completed","8","","2021-04-01","2021-07-01","2023-06-23 00:00:00","2023-10-14 05:39:50" -"119","sartorius-cell-instance-segmentation","Sartorius - Cell Instance Segmentation","Detect single neuronal cells in microscopy images","In this competition, you’ll detect and delineate distinct objects of interest in biological images depicting neuronal cell types commonly used in the study of neurological disorders. More specifically, you'll use phase contrast microscopy images to train and test your model for instance segmentation of neuronal cells. Successful models will do this with a high level of accuracy. If successful, you'll help further research in neurobiology thanks to the collection of robust quantitative data. Researchers may be able to use this to more easily measure the effects of disease and treatment conditions on neuronal cells. As a result, new drugs could be discovered to treat the millions of people with these leading causes of death and disability.","","https://www.kaggle.com/competitions/sartorius-cell-instance-segmentation","completed","8","","2021-10-14","2021-12-30","2023-06-23 00:00:00","2023-10-16 18:05:17" -"120","pvelad","Photovoltaic cell anomaly detection","Photovoltaic cell anomaly detection","Hosted by Hebei University of Technology (AIHebut research group) and Beihang University (NAVE research group)","","https://www.kaggle.com/competitions/pvelad","completed","8","","2022-03-15","2022-07-30","2023-06-23 00:00:00","2023-10-14 05:39:51" -"121","blood-mnist","Blood-MNIST","Classifying blood cell types using weights and biases","Classifying blood cell types using Weights and Biases","","https://www.kaggle.com/competitions/blood-mnist","completed","8","","2022-03-19","2022-03-19","2023-06-23 00:00:00","2023-11-14 20:33:37" -"122","insilicomolhack","MolHack","Apply deep learning to speedup drug validation","Apply deep learning to speedup drug validation","","https://www.kaggle.com/competitions/insilicomolhack","completed","8","","2018-04-02","2018-05-25","2023-06-23 00:00:00","2023-10-14 05:39:53" -"123","codata2019challenge","Cell Response Classification","From recorded timeseries of many cells in a well, predict which drug treatme","From recorded timeseries of many cells in a well, predict which drug treatment has been applied","","https://www.kaggle.com/competitions/codata2019challenge","completed","8","","2019-04-08","2019-05-07","2023-06-23 00:00:00","2023-10-14 05:39:53" -"124","drug-solubility-challenge","Drug solubility challenge","Crucial role of solubility in drug formulation for optimal efficacy","Solubility is vital to achieve desired concentration of drug for anticipated pharmacological response.","","https://www.kaggle.com/competitions/drug-solubility-challenge","completed","8","","2019-05-18","2019-10-18","2023-06-23 00:00:00","2023-10-14 05:39:54" -"125","kinase-inhibition-challenge","Kinase inhibition challenge","Unlocking the therapeutic potential of protein kinases: big data insights","Protein kinases have become a major class of drug targets, accumulating a huge amount of data","","https://www.kaggle.com/competitions/kinase-inhibition-challenge","completed","8","","2019-05-20","2019-12-28","2023-06-23 00:00:00","2023-10-14 05:39:54" -"126","ai-drug-discovery","AI Drug Discovery Workshop and Coding Challenge","Fostering core AI programming proficiency for drug discovery advancements","Developing Fundamental AI Programming Skills for Drug Discovery","","https://www.kaggle.com/competitions/ai-drug-discovery","completed","8","","2021-11-12","2021-12-31","2023-06-23 00:00:00","2023-11-02 18:41:48" -"127","protein-compound-affinity","Structure-free protein-ligand affinity prediction - Task 1 Fitting","Developing new AI models for drug discovery","Developing new AI models for drug discovery, main portal (Task-1 fitting)","","https://www.kaggle.com/competitions/protein-compound-affinity","completed","8","","2021-12-06","2021-12-31","2023-06-23 00:00:00","2023-11-14 20:34:30" -"128","cisc873-dm-f21-a5","CISC873-DM-F21-A5","Anti-cancer drug activity prediction","Anti-Cancer Drug Activity Prediction","","https://www.kaggle.com/competitions/cisc873-dm-f21-a5","completed","8","","2021-11-26","2021-12-10","2023-06-23 00:00:00","2023-10-14 05:39:56" -"129","pro-lig-aff-task2-mse","Structure-free protein-ligand affinity prediction - Task 2 Fitting","Developing new AI models for drug discovery","Developing new AI models for drug discovery (Task-2 fitting)","","https://www.kaggle.com/competitions/pro-lig-aff-task2-mse","completed","8","","2021-12-08","2021-12-31","2023-06-23 00:00:00","2023-11-15 22:42:37" -"130","pro-lig-aff-task1-pearsonr","Structure-free protein-ligand affinity prediction - Task 1 Ranking","Developing new AI models for drug discovery","Developing new AI models for drug discovery (Task-1 ranking)","","https://www.kaggle.com/competitions/pro-lig-aff-task1-pearsonr","completed","8","","2021-12-08","2021-12-31","2023-06-23 00:00:00","2023-11-15 22:42:40" -"131","pro-lig-aff-task2-pearsonr","Structure-free protein-ligand affinity prediction - Task 2 Ranking","Developing new AI models for drug discovery","Developing new AI models for drug discovery (Task-2 ranking)","","https://www.kaggle.com/competitions/pro-lig-aff-task2-pearsonr","completed","8","","2021-12-08","2021-12-31","2023-06-23 00:00:00","2023-11-15 22:42:43" -"132","pro-lig-aff-task3-spearmanr","Structure-free protein-ligand affinity prediction - Task 3 Ranking","Developing new AI models for drug discovery","Developing new AI models for drug discovery (Task-3 ranking)","","https://www.kaggle.com/competitions/pro-lig-aff-task3-spearmanr","completed","8","","2021-12-08","2021-12-31","2023-06-23 00:00:00","2023-11-15 22:42:44" -"133","hhp","Heritage Health Prize","Identify patients who will be admitted to a hospital within the next year","Identify patients who will be admitted to a hospital within the next year using historical claims data. (Enter by 06-59-59 UTC Oct 4 2012)","","https://www.kaggle.com/competitions/hhp","completed","8","","2011-04-04","2013-04-04","2023-06-23 00:00:00","2023-11-14 19:11:25" -"134","pf2012","Practice Fusion Analyze This! 2012 - Prediction Challenge","Propose innovative predictive modeling challenges","Start digging into electronic health records and submit your ideas for the most promising, impactful or interesting predictive modeling competitions","","https://www.kaggle.com/competitions/pf2012","completed","8","","2012-06-07","2012-06-30","2023-06-23 00:00:00","2023-11-14 19:11:32" -"135","pf2012-at","Practice Fusion Analyze This! 2012 - Open Challenge","Propose innovative predictive modeling challenges","Start digging into electronic health records and submit your creative, insightful, and visually striking analyses.","","https://www.kaggle.com/competitions/pf2012-at","completed","8","","2012-06-07","2012-09-10","2023-06-23 00:00:00","2023-11-14 19:21:00" -"136","seizure-detection","UPenn and Mayo Clinic's Seizure Detection Challenge","Detect seizures in intracranial EEG recordings","Detect seizures in intracranial EEG recordings","","https://www.kaggle.com/competitions/seizure-detection","completed","8","","2014-05-19","2014-08-19","2023-06-23 00:00:00","2023-10-14 05:40:02" -"137","seizure-prediction","American Epilepsy Society Seizure Prediction Challenge","Predict seizures in intracranial EEG recordings","Predict seizures in intracranial EEG recordings","","https://www.kaggle.com/competitions/seizure-prediction","completed","8","","2014-08-25","2014-11-17","2023-06-23 00:00:00","2023-10-14 05:40:03" -"138","deephealth-1","Deep Health - alcohol","Find correlations and patterns with medical data","Find Correlations and patterns with Medical data","","https://www.kaggle.com/competitions/deephealth-1","completed","8","","2017-02-13","2017-02-19","2023-06-23 00:00:00","2023-10-16 18:14:48" -"139","deep-health-3","Deep Health - Diabetes 2","Deep health hackathon: predicting future diabetes occurrences challenge","This competition is for those attending the Deep Health Hackathon. Predict the next occurrence of diabetes","","https://www.kaggle.com/competitions/deep-health-3","completed","8","","2017-02-15","2017-02-19","2023-06-23 00:00:00","2023-10-16 18:14:50" -"140","d012554-2021","D012554 - 2021","Classify the health of a fetus using CTG data","Classify the health of a fetus using CTG data","","https://www.kaggle.com/competitions/d012554-2021","completed","8","","2021-04-11","2021-05-09","2023-06-23 00:00:00","2023-10-16 18:15:04" -"141","idao-2022-bootcamp-insomnia","IDAO 2022. ML Bootcamp - Insomnia","Predict sleep disorder on given human health data","Predict sleep disorder on given human health data","","https://www.kaggle.com/competitions/idao-2022-bootcamp-insomnia","completed","8","","2021-12-04","2021-12-05","2023-06-23 00:00:00","2023-10-16 18:15:12" -"142","tweet-mental-health-classification","Tweet Mental Health Classification","Build models to classify tweets to determine mental health","Build Models to classify tweets to determine mental health","","https://www.kaggle.com/competitions/tweet-mental-health-classification","completed","8","","2021-12-27","2022-01-31","2023-06-23 00:00:00","2023-10-14 05:40:07" -"143","ml-olympiad-good-health-and-well-being","ML Olympiad - GOOD HEALTH AND WELL BEING","Use your ML expertise to classify if a patient has heart disease or not","Use your ML expertise to classify if a patient has heart disease or not","","https://www.kaggle.com/competitions/ml-olympiad-good-health-and-well-being","completed","8","","2022-02-03","2022-03-01","2023-06-23 00:00:00","2023-10-16 18:15:20" -"144","rsna-breast-cancer-detection","RSNA Screening Mammography Breast Cancer Detection","Find breast cancers in screening mammograms","Find breast cancers in screening mammograms","","https://www.kaggle.com/competitions/rsna-breast-cancer-detection","completed","8","","2022-11-28","2023-02-27","2023-06-23 00:00:00","2023-10-14 05:40:12" -"145","biocreative-vii-text-mining-drug-and-chemical-protein-interactions-drugprot","BioCreative VII: Text mining drug and chemical-protein interactions (DrugProt)","Develop systems to extract drug-gene relations from text","With the rapid accumulation of biomedical literature, it is getting increasingly challenging to exploit efficiently drug-related information described in the scientific literature. One of the most relevant aspects of drugs and chemical compounds are their relationships with certain biomedical entities, in particular genes and proteins. The aim of the DrugProt track (similar to the previous CHEMPROT task of BioCreative VI) is to promote the development and evaluation of systems that are able to automatically detect in relations between chemical compounds/drug and genes/proteins. There are a range of different types of drug-gene/protein interactions, and their systematic extraction and characterization is essential to analyze, predict and explore key biomedical properties underlying high impact biomedical applications. These application scenarios include use cases related to drug discovery, drug repurposing, drug design, metabolic engineering, modeling drug response, pharmacogenet...","","https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/","completed","\N","","2021-06-15","2021-09-22","2023-06-23 00:00:00","2023-11-01 20:37:37" -"146","extended-literature-ai-for-drug-induced-liver-injury","Extended Literature AI for Drug Induced Liver Injury","Develop ML tools to analyze drug texts for liver injury data","Unexpected Drug-Induced Liver Injury (DILI) still is one of the main killers of promising novel drug candidates. It is a clinically significant disease that can lead to severe outcomes such as acute liver failure and even death. It remains one of the primary liabilities in drug development and regulatory clearance due to the limited performance of mandated preclinical models even today. The free text of scientific publications is still the main medium carrying DILI results from clinical practice or experimental studies. The textual data still has to be analysed manually. This process, however, is tedious and prone to human mistakes or omissions, as results are very rarely available in a standardized form or organized form. There is thus great hope that modern techniques from machine learning or natural language processing could provide powerful tools to better process and derive the underlying knowledge within free form texts. The pressing need to faster process potential drug can...","","http://camda2022.bioinf.jku.at/contest_dataset#extended_literature_ai_for_drug_induced_liver_injury","completed","\N","","\N","2022-05-20","2023-06-23 00:00:00","2023-11-01 20:37:38" -"147","anti-microbial-resistance-forensics","Anti-Microbial Resistance Forensics","Classifying bacteriophages to understand microbial evolution","Bacteriophages, being the re-occuring mystery in the history of science are believed to be they key for understanding of microbial evolution and the transfer of AMR genes. Recent studies show that there is a significant correlation between occurence of Phages and AMR genes, indicating that they are indeed taking part in the spread of them. While taking part in AMR dissemination the phages are also considered as the potential alternative to antibiotics. In such contradictory world there is a huge potential as well as urgent need for precise classification, description and analysis of capabilities. Due to pandemic of SARS-CoV-2, advance in phylogenetic algorithms and k-mer based methods have been extremely rapid and those improvements are witing to be adapted to different branches of life sciences.","","http://camda2022.bioinf.jku.at/contest_dataset#anti-microbial_resistance_forensics","completed","\N","","\N","2022-05-20","2023-06-23 00:00:00","2023-10-14 05:40:14" -"148","disease-maps-to-modelling-covid-19","Disease Maps to Modelling COVID-19","Suggest drugs candidate for repurposing","The Disease Maps to modeling COVID-19 Challenge provides highly detailed expert-curated molecular mechanistic maps for COVID-19. Combine them with available omic data to expand the current biological knowledge on COVID-19 mechanism of infection and downstream consequences. The main topic for this year's challenge is drug repurposing with the possibility of Real World Data based validation of the most promising candidates suggested.","","http://camda2022.bioinf.jku.at/contest_dataset#disease_maps_to_modelling_covid-19","completed","\N","","\N","2022-05-20","2023-06-23 00:00:00","2023-11-14 19:23:47" -"149","crowdsourced-evaluation-of-inchi-based-tautomer-identification","Crowdsourced Evaluation of InChI-based Tautomer Identification","Test a modified InChi algorithm","This challenge focuses on the International Chemical Identifier (InChI), which was developed and is maintained under the auspices of the International Union of Pure and Applied Chemistry (IUPAC) and the InChI Trust. The InChI Trust, the IUPAC Working Group on Tautomers, and the U.S. Food and Drug Administration (FDA) call on the scientific community dealing with chemical repositories/data sets and analytics of compounds to test the recently modified InChI algorithm, which was designed for advanced recognition of tautomers. Participants will evaluate this algorithm against real chemical samples in this Crowdsourced Evaluation of InChI-based Tautomer Identification.","","https://precision.fda.gov/challenges/29","completed","6","","2022-11-01","2023-03-01","2023-06-23 00:00:00","2023-11-14 19:21:10" -"150","nctr-indel-calling-from-oncopanel-sequencing-challenge-phase-2","NCTR Indel Calling from Oncopanel Sequencing Challenge Phase 2","Calling from oncopanel sequencing data","The high value of clinically actionable information obtained by oncopanel sequencing makes it a crucial tool for precision oncology[1,2]. With the surge in availability of oncopanels, it is critical to ensure that they have been thoroughly tested and are properly used. FDA has initiated the Sequencing Quality Control phase II (SEQC2) project[3] to develop standard analysis protocols and quality control metrics for fit-for-purpose use of Next Generation Sequencing (NGS) data including oncopanel sequencing to inform regulatory science research and precision medicine. The Oncopanel Sequencing Working Group of FDA-led SEQC2 has developed a reference sample[4] suitable for benchmarking oncopanels and comprehensively assessed the analytical performance of several oncopanels[1,2]. The genomic deoxyribonucleic acid (gDNA) reference sample was derived from ten Universal Human Reference RNA (UHRR, Agilent Technologies, Inc) cell-lines and made publicly available by Agilent. Substantial gen...","","https://precision.fda.gov/challenges/22","completed","6","","2022-07-11","2022-07-26","2023-06-23 00:00:00","2023-11-15 22:53:48" -"151","nctr-indel-calling-from-oncopanel-sequencing-data-challenge-phase-1","NCTR Indel Calling from Oncopanel Sequencing Data Challenge Phase 1","Identify indels in oncopanel sequencing datasets","The high value of clinically actionable information obtained by oncopanel sequencing makes it a crucial tool for precision oncology[1,2]. With the surge in availability of oncopanels, it is critical to ensure that they have been thoroughly tested and are properly used. FDA has initiated the Sequencing Quality Control phase II (SEQC2) project[3] to develop standard analysis protocols and quality control metrics for fit-for-purpose use of Next Generation Sequencing (NGS) data including oncopanel sequencing to inform regulatory science research and precision medicine. The Oncopanel Sequencing Working Group of FDA-led SEQC2 has developed a reference sample[4] suitable for benchmarking oncopanels and comprehensively assessed the analytical performance of several oncopanels[1,2]. The genomic deoxyribonucleic acid (gDNA) reference sample was derived from ten Universal Human Reference RNA (UHRR, Agilent Technologies, Inc) cell-lines and made publicly available by Agilent. Substantial gen...","","https://precision.fda.gov/challenges/21","completed","6","","2022-05-02","2022-07-08","2023-06-23 00:00:00","2023-11-14 19:24:33" -"152","vha-innovation-ecosystem-and-precisionfda-covid-19-risk-factor-modeling-challenge-phase-2","VHA Innovation Ecosystem and precisionFDA COVID-19 Risk Factor Modeling Challenge Phase 2","Validate the top performing models on two additional VA sites' data","The novel coronavirus disease 2019 (COVID-19) is a respiratory disease caused by a new type of coronavirus, known as “severe acute respiratory syndrome coronavirus 2,” or SARS-CoV-2. On March 11, 2020, the World Health Organization (WHO) declared the outbreak a global pandemic. As of January 22nd, 2022, the Johns Hopkins University COVID-19 dashboard reports over 338 million total confirmed cases worldwide. Although most people have mild to moderate symptoms, the disease can cause severe medical complications leading to death in some people. The Centers for Disease Control and Prevention (CDC) have identified several risk factors for severe COVID-19 illness, including people 65 years and older, individuals living in nursing homes or long-term care facilities, and those with serious underlying medical conditions. The Veteran population has a higher prevalence of several of the known risk factors for severe COVID-19 illness, such as advanced age, heart disease, and diabetes. Identif...","","https://precision.fda.gov/challenges/20","completed","6","","2021-04-14","2022-01-28","2023-06-23 00:00:00","2023-11-14 19:24:37" -"153","tumor-mutational-burden-tmb-challenge-phase-2","Tumor Mutational Burden (TMB) Challenge Phase 2","Evaluating various computational pipelines for TMB estimation","Tumor mutational burden (TMB) is generally defined as the number of mutations detected in a patient's tumor sample per megabase of DNA sequenced. However different algorithms use different methods for calculating TMB. Mutations in genes in tumor cells may lead to the creation of neoantigens, which have the potential to activate an immune system response against the tumor, and the likelihood of an immune system response may increase with the number of mutations. Thus, TMB is a biomarker for some immunotherapy drugs, called immune checkpoint inhibitors, such as those that target the PD-1 and PD-L1 pathways (Chan et al., 2019). An outstanding problem is the lack of standardization for TMB calculation and reporting between different assays. To address this problem, the Friends of Cancer Research convened a working group of industry and regulatory stakeholders to develop guidance and tools for TMB harmonization. Results from the first phase of this effort were presented at AACR 2020 (s...","","https://precision.fda.gov/challenges/18","completed","6","","2021-07-19","2021-09-12","2023-06-23 00:00:00","2023-11-14 19:24:44" -"154","predicting-gene-expression-using-millions-of-random-promoter-sequences","Predicting Gene Expression Using Millions of Random Promoter Sequences","Decoding gene expression regulation to understand disease","Decoding how gene expression is regulated is critical to understanding disease. Regulatory DNA is decoded by the cell in a process termed “cis-regulatory logic”, where proteins called Transcription Factors (TFs) bind to specific DNA sequences within the genome and work together to produce as output a level of gene expression for downstream adjacent genes. This process is exceedingly complex to model as a large number of parameters is needed to fully describe the process (see Rationale, de Boer et al. 2020; Zeitingler J. 2020). Understanding the cis-regulatory logic of the human genome is an important goal and would provide insight into the origins of many diseases. However, learning models from human data is challenging due to limitations in the diversity of sequences present within the human genome (e.g. extensive repetitive DNA), the vast number of cell types that differ in how they interpret regulatory DNA, limited reporter assay data, and substantial technical biases present i...","","https://www.synapse.org/#!Synapse:syn28469146/wiki/617075","completed","1","","2022-06-15","2022-08-07","2023-06-23 00:00:00","2023-10-14 05:40:21" -"155","brats-2023","BraTS 2023","Benchmarking brain tumor segmentation with expanded dataset","The International Brain Tumor Segmentation (BraTS) challenge. BraTS, since 2012, has focused on the generation of a benchmarking environment and dataset for the delineation of adult brain gliomas. The focus of this year’s challenge remains the generation of a common benchmarking environment, but its dataset is substantially expanded to ~4,500 cases towards addressing additional i) populations (e.g., sub-Saharan Africa patients), ii) tumors (e.g., meningioma), iii) clinical concerns (e.g., missing data), and iv) technical considerations (e.g., augmentations). Specifically, the focus of BraTS 2023 is to identify the current state-of-the-art algorithms for addressing (Task 1) the same adult glioma population as in the RSNA-ANSR-MICCAI BraTS challenge, as well as (Task 2) the underserved sub-Saharan African brain glioma patient population, (Task 3) intracranial meningioma, (Task 4) brain metastasis, (Task 5) pediatric brain tumor patients, (Task 6) global & local missing data, (Task 7...","","https://www.synapse.org/brats","completed","1","","2023-06-01","2023-08-25","2023-06-23 00:00:00","2023-10-26 23:20:21" -"156","cagi7","CAGI7","The seventh round of CAGI","There have been six editions of CAGI experiments, held between 2010 and 2022. The seventh round of CAGI is planned to take place over the Summer of 2024.","","https://genomeinterpretation.org/challenges.html","upcoming","1","","\N","\N","2023-08-04 21:47:38","2023-11-20 20:19:08" -"157","casp15","CASP15","Establish the state-of-art in modeling proteins and protein complexes","CASP14 (2020) saw an enormous jump in the accuracy of single protein and domain models such that many are competitive with experiment. That advance is largely the result of the successful application of deep learning methods, particularly by the AlphaFold and, since that CASP, RosettaFold. As a consequence, computed protein structures are becoming much more widely used in a broadening range of applications. CASP has responded to this new landscape with a revised set of modeling categories. Some old categories have been dropped (refinement, contact prediction, and aspects of model accuracy estimation) and new ones have been added (RNA structures, protein ligand complexes, protein ensembles, and accuracy estimation for protein complexes). We are also strengthening our interactions with our partners CAPRI and CAMEO. We hope that these changes will maximize the insight that CASP15 provides, particularly in new applications of deep learning.","","https://predictioncenter.org/casp15/index.cgi","completed","\N","","2022-04-18","\N","2023-08-04 21:52:12","2023-09-28 23:09:59" -"158","synthrad2023","SynthRAD2023","Automatic generation of synthetic computed tomography (sCT) for radiotherapy","This challenge aims to provide the first platform offering public data evaluation metrics to compare the latest developments in sCT generation methods. The accepted challenge design approved by MICCAI can be found at https://doi.org/10.5281/zenodo.7746019. A type 2 challenge will be run, where the participant needs to submit their algorithm packaged in a docker both for validation and test.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/678/SynthRAD_square_logo_MnAqldI.png","https://synthrad2023.grand-challenge.org/","active","5","","2023-04-01","2028-09-20","2023-08-04 21:54:31","2024-01-31 22:38:07" -"159","synthetic-data-for-instrument-segmentation-in-surgery-syn-iss","Synthetic Data for Instrument Segmentation in Surgery (Syn-ISS)","Surgical instrument segmentation with synthetic data","A common limitation noted by the surgical data science community is the size of datasets and the resources needed to generate training data at scale for building reliable and high-performing machine learning models. Beyond unsupervised and self-supervised approaches another solution within the broader machine learning community has been a growing volume of literature in the use of synthetic data (simulation) for training algorithms than can be applied to real world data. Synthetic data has multiple benefits like free groundtruth at large scale, possibility to collect larger sample of rare events, include anatomical variations, etc. A first step towards proving the validity of using synthetic data for real world applications is to demonstrate the feasibility within the simulation world itself. Our proposed challenge is to train machine learning methods for instrument segmentation using synthetic datasets and test their performance on synthetic datasets. That is, the challenge parti...","","https://www.synapse.org/#!Synapse:syn50908388/wiki/620516","completed","1","","2023-07-19","2023-09-07","2023-08-04 23:49:44","2023-12-06 07:16:20" -"160","pitvis","PitVis","Surgical workflow and instrument recognition in endonasal surgery","The pituitary gland, found just off the base of the brain, is commonly known as “the master gland”, performing essential functions required for sustaining human life. Clinically relevant tumours that have grown on the pituitary gland have an estimated prevalence of 1 in 1000 of the population, and if left untreated can be life-limiting. The “gold standard” treatment is endoscopic pituitary surgery, where the tumour is directly removed by entering through a nostril. This surgery is particularly challenging due to the small working space which limits both vision and instrument manoeuvrability and thus can lead to poor surgical technique causing adverse outcomes for the patient. Computer-assisted intervention can help overcome these challenges by providing guidance for senior surgeons and operative staff during surgery, and for junior surgeons during training.","","https://www.synapse.org/#!Synapse:syn51232283/wiki/","completed","1","","2023-06-29","2023-09-10","2023-08-04 23:58:01","2023-10-26 23:20:30" -"161","mvseg2023","MVSEG2023","Single frame 3D trans-esophageal echocardiography","Mitral valve (MV) disease is a common pathologic problem occurring in approximately 2 % of the general population but climbing to 10 % in those over the age of 75. The preferred intervention for mitral regurgitation is valve repair, due to superior patient outcomes compared to those following valve replacement. Mitral valve interventions are technically challenging due to the functional and anatomical complexity of mitral pathologies. Repair must be tailored to the patient-specific anatomy and pathology, which requires considerable expert training and experience. Automatic segmentation of the mitral valve leaflets from 3D transesophageal echocardiography (TEE) may play an important role in treatment planning, as well as physical and computational modelling of patient-specific valve pathologies and potential repair approaches. This may have important implications in the drive towards personalized care and has the potential to impact clinical outcomes for those undergoing mitral val...","","https://www.synapse.org/#!Synapse:syn51186045/wiki/621356","completed","1","","2023-05-29","2023-08-07","2023-08-05 0-04-36","2023-11-14 19:25:13" -"162","crossmoda23","crossMoDA23","Medical imaging benchmark for unsupervised domain adaptation","Domain Adaptation (DA) has recently raised strong interest in the medical imaging community. By encouraging algorithms to be robust to unseen situations or different input data domains, Domain Adaptation improves the applicability of machine learning approaches to various clinical settings. While a large variety of DA techniques has been proposed, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly address single-class problems. To tackle these limitations, the crossMoDA challenge introduced the first large and multi-class dataset for unsupervised cross-modality Domain Adaptation. From an application perspective, crossMoDA focuses on MRI segmentation for Vestibular Schwannoma. Compared to the previous crossMoDA instance, which made use of multi-institutional data acquired in controlled conditions for radiosurgery planning and focused on a 2 class segmentation task (tumour and cochlea), the...","","https://www.synapse.org/#!Synapse:syn51236108/wiki/621615","completed","1","","2023-04-15","2023-07-10","2023-08-05 0-13-23","2023-11-14 19:27:00" -"163","icr-identify-age-related-conditions","ICR - Identifying Age-Related Conditions","Detect conditions with measurements of anonymous characteristics of a subject","The goal of this competition is to predict if a person has any of three medical conditions. You are being asked to predict if the person has one or more of any of the three medical conditions (Class 1), or none of the three medical conditions (Class 0). You will create a model trained on measurements of health characteristics. To determine if someone has these medical conditions requires a long and intrusive process to collect information from patients. With predictive models, we can shorten this process and keep patient details private by collecting key characteristics relative to the conditions, then encoding these characteristics.","","https://www.kaggle.com/competitions/icr-identify-age-related-conditions","completed","8","","2023-05-11","2023-08-10","2023-08-05 0-32-01","2023-11-14 19:25:37" -"164","cafa-5-protein-function-prediction","CAFA 5: Protein Function Prediction","Predict the biological function of a protein","The goal of this competition is to predict the function of a set of proteins. You will develop a model trained on the amino-acid sequences of the proteins and on other data. Your work will help ​​researchers better understand the function of proteins, which is important for discovering how cells, tissues, and organs work. This may also aid in the development of new drugs and therapies for various diseases.","","https://www.kaggle.com/competitions/cafa-5-protein-function-prediction","completed","8","","2023-04-18","2023-08-21","2023-08-05 5-18-40","2023-10-19 00:13:14" -"165","rsna-2023-abdominal-trauma-detection","RSNA 2023 Abdominal Trauma Detection","Detect and classify traumatic abdominal injuries","Traumatic injury is the most common cause of death in the first four decades of life and a major public health problem around the world. There are estimated to be more than 5 million annual deaths worldwide from traumatic injury. Prompt and accurate diagnosis of traumatic injuries is crucial for initiating appropriate and timely interventions, which can significantly improve patient outcomes and survival rates. Computed tomography (CT) has become an indispensable tool in evaluating patients with suspected abdominal injuries due to its ability to provide detailed cross-sectional images of the abdomen. Interpreting CT scans for abdominal trauma, however, can be a complex and time-consuming task, especially when multiple injuries or areas of subtle active bleeding are present. This challenge seeks to harness the power of artificial intelligence and machine learning to assist medical professionals in rapidly and precisely detecting injuries and grading their severity. The development...","","https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection","completed","8","","2023-07-26","2023-10-13","2023-08-05 5-24-09","2023-09-28 23:14:12" -"166","hubmap-hacking-the-human-vasculature","HuBMAP: Hacking the Human Vasculature","Microvascular structures from healthy human kidney tissue images","The goal of this competition is to segment instances of microvascular structures, including capillaries, arterioles, and venules. You'll create a model trained on 2D PAS-stained histology images from healthy human kidney tissue slides. Your help in automating the segmentation of microvasculature structures will improve researchers' understanding of how the blood vessels are arranged in human tissues.","","https://www.kaggle.com/competitions/hubmap-hacking-the-human-vasculature","completed","8","","2023-05-22","2023-07-31","2023-08-05 5-31-12","2023-11-14 19:25:45" -"167","amp-parkinsons-disease-progression-prediction","AMP(R)-Parkinson's Disease Progression Prediction","Predict clinical and molecular progression of the disease","The goal of this competition is to predict MDS-UPDR scores, which measure progression in patients with Parkinson's disease. The Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is a comprehensive assessment of both motor and non-motor symptoms associated with Parkinson's. You will develop a model trained on data of protein and peptide levels over time in subjects with Parkinson’s disease versus normal age-matched control subjects. Your work could help provide important breakthrough information about which molecules change as Parkinson’s disease progresses.","","https://www.kaggle.com/competitions/amp-parkinsons-disease-progression-prediction","completed","8","","2023-02-16","2023-05-18","2023-08-05 5-37-12","2023-12-06 22:44:19" -"168","open-problems-multimodal","Open Problems -Multimodal Single-Cell Integration","Predict how DNA, RNA & protein measurements co-vary in single cells","The goal of this competition is to predict how DNA, RNA, and protein measurements co-vary in single cells as bone marrow stem cells develop into more mature blood cells. You will develop a model trained on a subset of 300,000-cell time course dataset of CD34+ hematopoietic stem and progenitor cells (HSPC) from four human donors at five time points generated for this competition by Cellarity, a cell-centric drug creation company. In the test set, taken from an unseen later time point in the dataset, competitors will be provided with one modality and be tasked with predicting a paired modality measured in the same cell. The added challenge of this competition is that the test data will be from a later time point than any time point in the training data. Your work will help accelerate innovation in methods of mapping genetic information across layers of cellular state. If we can predict one modality from another, we may expand our understanding of the rules governing these complex re...","","https://www.kaggle.com/competitions/open-problems-multimodal","completed","8","","2022-08-15","2022-11-15","2023-08-05 5-43-25","2023-10-10 19:52:41" -"169","multi-atlas-labeling-beyond-the-cranial-vault","Multi-Atlas Labeling Beyond the Cranial Vault","Innovative multi-atlas labeling for soft tissue segmentation on clinical CT","Multi-atlas labeling has proven to be an effective paradigm for creating segmentation algorithms from training data. These approaches have been extraordinarily successful for brain and cranial structures (e.g., our prior MICCAI workshops-MLSF’11, MAL’12, SATA’13). After the original challenges closed, the data continue to drive scientific innovation; 144 groups have registered for the 2012 challenge (brain only) and 115 groups for the 2013 challenge (brain/heart/canine leg). However, innovation in application outside of the head and to soft tissues has been more limited. This workshop will provide a snapshot of the current progress in the field through extended discussions and provide researchers an opportunity to characterize their methods on a newly created and released standardized dataset of abdominal anatomy on clinically acquired CT. The datasets will be freely available both during and after the challenge. We have two separate new challenges-abdomen and cervix on routinely ...","","https://www.synapse.org/#!Synapse:syn3193805/wiki/89480","active","1","","2015-04-15","\N","2023-08-07 20:21:22","2023-10-10 19:52:39" -"170","hubmap-organ-segmentation","HuBMAP + HPA: Hacking the Human Body","Segment multi-organ functional tissue units","In this competition, you’ll identify and segment functional tissue units (FTUs) across five human organs. You'll build your model using a dataset of tissue section images, with the best submissions segmenting FTUs as accurately as possible. If successful, you'll help accelerate the world’s understanding of the relationships between cell and tissue organization. With a better idea of the relationship of cells, researchers will have more insight into the function of cells that impact human health. Further, the Human Reference Atlas constructed by HuBMAP will be freely available for use by researchers and pharmaceutical companies alike, potentially improving and prolonging human life.","","https://www.kaggle.com/competitions/hubmap-organ-segmentation","completed","8","","2022-06-22","2022-09-22","2023-08-08 16:30:22","2023-11-02 18:44:27" -"171","hubmap-kidney-segmentation","HuBMAP: Hacking the Kidney","Identify glomeruli in human kidney tissue images","This competition, “Hacking the Kidney, starts by mapping the human kidney at single cell resolution. Your challenge is to detect functional tissue units (FTUs) across different tissue preparation pipelines. An FTU is defined as a “three-dimensional block of cells centered around a capillary, such that each cell in this block is within diffusion distance from any other cell in the same block” ([de Bono, 2013](https://www.ncbi.nlm.nih.gov/pubmed/24103658)). The goal of this competition is the implementation of a successful and robust glomeruli FTU detector. You will also have the opportunity to present your findings to a panel of judges for additional consideration. Successful submissions will construct the tools, resources, and cell atlases needed to determine how the relationships between cells can affect the health of an individual. Advancements in HuBMAP will accelerate the world’s understanding of the relationships between cell and tissue organization and function and human health.","","https://www.kaggle.com/competitions/hubmap-kidney-segmentation","completed","8","","2020-11-16","2021-05-10","2023-08-08 17:31:46","2023-10-12 18:14:16" -"172","ventilator-pressure-prediction","Google Brain: Ventilator Pressure Prediction","Simulate a ventilator connected to a sedated patient's lung","In this competition, you’ll simulate a ventilator connected to a sedated patient's lung. The best submissions will take lung attributes compliance and resistance into account. If successful, you'll help overcome the cost barrier of developing new methods for controlling mechanical ventilators. This will pave the way for algorithms that adapt to patients and reduce the burden on clinicians during these novel times and beyond. As a result, ventilator treatments may become more widely available to help patients breathe.","","https://www.kaggle.com/competitions/ventilator-pressure-prediction","completed","8","","2021-09-22","2021-11-03","2023-08-08 17:53:33","2023-11-02 18:44:22" -"173","stanford-covid-vaccine","OpenVaccine - COVID-19 mRNA Vaccine Degradation Prediction","Urgent need to bring the COVID-19 vaccine to mass production","In this competition, we are looking to leverage the data science expertise of the Kaggle community to develop models and design rules for RNA degradation. Your model will predict likely degradation rates at each base of an RNA molecule, trained on a subset of an Eterna dataset comprising over 3000 RNA molecules (which span a panoply of sequences and structures) and their degradation rates at each position. We will then score your models on a second generation of RNA sequences that have just been devised by Eterna players for COVID-19 mRNA vaccines. These final test sequences are currently being synthesized and experimentally characterized at Stanford University in parallel to your modeling efforts--Nature will score your models!","","https://www.kaggle.com/competitions/stanford-covid-vaccine","completed","8","","2020-09-10","2020-10-06","2023-08-08 18:06:17","2023-10-12 18:14:27" -"174","openvaccine","OpenVaccine","To develop mRNA vaccines stable enough to be deployed to everyone in the world","mRNA vaccines are a relatively new technology that have come into the limelight with the onset of COVID-19. They were the first COVID-19 vaccines to start clinical trials (initially formulated in a matter of days) and the first to be approved and distributed. mRNA vaccines have the potential to transform immunization, being significantly faster to formulate and produce, cheaper, and more effective-including against mutant strains. However, there is one key bottleneck to their widespread viability and our ability to immunize the entire world-poor refrigerator stability in prefilled syringes. The OpenVaccine challenge aims to allow a worldwide community of game players to create an enhanced vaccine to be injected into millions of people. The challenge-design an mRNA that codes for the same amino acid sequence of the spike protein, but is 2x-10x+ more stable. Through a number of academic partnerships and the launch of a Kaggle machine learning challenge to create best-in-class algori...","","https://eternagame.org/challenges/10845741","completed","13","https://doi.org/10.1038/s41467-022-28776-w","\N","2021-12-12","2023-08-08 18:22:49","2023-11-14 19:26:10" -"175","opentb","OpenTB","Detect a gene sequence found to be present only in people with active TB","OpenTB used a recently reported gene signature for active tuberculosis based on three RNAs in the blood. This signature could form the basis for a fast, color-based test for TB, similar to an over-the-counter pregnancy test. What was needed was a sensor that could detect the concentrations of three RNAs, carry out the needed calculation, and report the result by binding another molecule. Over four rounds, players designed RNA sensors that can do the math on these 3 genes. Through experimental feedback, they honed their skills and techniques, which resulted in the creation of multiple designs that have been shown to be successful. These findings are being prepared to be published, and future work will be done to develop diagnostic devices integrating these designs","","https://eternagame.org/challenges/10845742","completed","13","","2016-05-04","2018-04-15","2023-08-08 18:43:09","2023-11-14 19:32:31" -"176","opencrispr","OpenCRISPR","Discover RNAs to make gene editing more precisely controllable","CRISPR gene editing is a RNA-based method that can target essentially any gene in a living organism for genetic changes. Since its first demonstration, CRISPR has been revolutionizing biology and promises to change how we tackle numerous human diseases from malaria to cancer. Stanford's Center for Personal Dynamic Regulomes and UC Berkeley's Innovative Genomics Institute have challenged Eterna players to solve a remaining hurdle in making this technology safe for use. Scientists want the power to turn on and off CRISPR on demand with small molecules. This is almost a perfect match to the small-molecule switches that the Eterna community has worked on. In fact, the MS2 RNA hairpin often used in Eterna is routinely used to recruit new functionality to CRISPR complexes through other molecules tethered to the MS2 protein. The puzzles began with OpenCRISPR Controls, looking for solutions to lock in or lock out the MS2 RNA hairpin within a special loop in the CRISPR RNA. We hope the res...","","https://eternagame.org/challenges/10845743","completed","13","https://doi.org/10.1021/acssynbio.9b00142","2017-08-26","\N","2023-08-08 18:43:14","2023-11-14 19:33:25" -"177","openknot","OpenKnot","Build a diverse library of RNAs that form pseudoknot structures","RNA pseudoknots have significant biological importance in various processes. They participate in gene regulation by influencing translation initiation or termination in mRNA molecules. Pseudoknots also play a role in programmed ribosomal frameshifting, leading to the production of different protein products from a single mRNA. RNA viruses, including SARS-CoV-2 and Dengue virus, utilize pseudoknots to regulate their replication and control the synthesis of viral proteins. Additionally, certain RNA molecules with pseudoknot structures exhibit enzymatic activity, acting as ribozymes and catalyzing biochemical reactions. These functions highlight the crucial role of RNA pseudoknots in gene expression, proteomic diversity, viral replication, and enzymatic processes. Several unanswered scientific questions surround RNA pseudoknots. One key area of inquiry is understanding the folding pathways of pseudoknots and how they form from linear RNA sequences. Elucidating the structural dynamics...","","https://eternagame.org/challenges/11843006","active","13","","2022-06-17","\N","2023-08-08 18:43:22","2023-11-14 19:32:46" -"178","openaso","OpenASO","Design principles for RNA-based therapeutics","The DNA genome is the blueprint for building and operating cells, but this information must be decoded into RNA molecules to be useful. Transcription is the process of decoding DNA genomic information into RNA, resulting in RNA transcripts. Genes are specific sequences of DNA that contain information to produce a specific RNA transcript. The fate of most mRNA molecules in the cell is to be translated by ribosomes into protein molecules. However, mRNA splicing is a crucial step that occurs between the formation of an RNA transcript and protein translation. This step is essential because genes contain non-protein coding introns and protein-coding exons. Splicing removes introns and joins exons to produce a mature mRNA molecule that can be decoded into the correct protein molecule. When the splicing process is corrupted due to genetic mutations, the resulting RNA can become toxic, leading to the synthesis of non-functional proteins or no protein at all, causing various human diseases...","","https://eternagame.org/challenges/11546273","active","13","","2023-02-20","\N","2023-08-08 18:43:25","2023-11-14 19:32:51" -"179","openribosome","OpenRibosome","Learn and change the ribosome's RNAs","Our modern world has many challenges-challenges like climate change, increasing waste production, and human health. Imagine-we could replace petrochemistry with biology, single-use plastics with selectively degradable polymers, broad chemotherapeutics with targeted medicines for fighting specific cancer cells, and complex health equipment with point-of-care diagnostics. These innovations and many more can empower us to confront the challenges affecting humanity, our world, and beyond. But how do we actually create these smart materials and medicines? Is it possible to do so by repurposing one of Nature's molecular machines? We think we can. The answer? Customized ribosomes. In Nature, ribosomes are the catalysts for protein assembly. And proteins are more or less similar, chemically, to the smart materials and medicines we want to synthesize. If we could modify ribosomes to build polymers with diverse components-beyond the canonical amino acids us","","https://eternagame.org/challenges/11043833","active","13","https://doi.org/10.1038/s41467-023-35827-3","2019-01-31","\N","2023-08-08 18:43:27","2023-11-14 19:33:01" -"180","lish-moa","Mechanisms of Action (MoA) Prediction","Improve the algorithm that classifies drugs based on their biological activity","Can you improve the algorithm that classifies drugs based on their biological activity?","","https://www.kaggle.com/competitions/lish-moa","completed","8","","2020-09-03","2020-11-30","2023-08-08 19:09:31","2023-11-14 19:33:49" -"181","recursion-cellular-image-classification","Recursion Cellular Image Classification","CellSignal-Disentangling biological signal in cellular images","This competition will have you disentangling experimental noise from real biological signals. Your entry will classify images of cells under one of 1,108 different genetic perturbations. You can help eliminate the noise introduced by technical execution and environmental variation between experiments. If successful, you could dramatically improve the industry’s ability to model cellular images according to their relevant biology. In turn, applying AI could greatly decrease the cost of treatments, and ensure these treatments get to patients faster.","","https://www.kaggle.com/competitions/recursion-cellular-image-classification","completed","8","","2019-06-27","2019-09-26","2023-08-08 19:38:42","2023-11-14 19:34:11" -"182","tlvmc-parkinsons-freezing-gait-prediction","Parkinson's Freezing of Gait Prediction","Event detection from wearable sensor data","The goal of this competition is to detect freezing of gait (FOG), a debilitating symptom that afflicts many people with Parkinson’s disease. You will develop a machine learning model trained on data collected from a wearable 3D lower back sensor. Your work will help researchers better understand when and why FOG episodes occur. This will improve the ability of medical professionals to optimally evaluate, monitor, and ultimately, prevent FOG events.","","https://www.kaggle.com/competitions/tlvmc-parkinsons-freezing-gait-prediction","completed","8","","2023-03-09","2023-06-08","2023-08-08 19:47:54","2023-10-10 19:53:08" -"183","chaimeleon","CHAIMELEON Open Challenges","AI-powered solutions driving innovation in cancer diagnosis and treatment","The CHAIMELEON Open Challenges is a competition designed to train and refine AI models to answer clinical questions about five types of cancer-prostate, lung, breast, colon, and rectal. Participants are challenged to collaborate and develop innovative AI-powered solutions that can significantly impact cancer diagnosis, management, and treatment. They will be evaluated considering a balance between the performance of their AI algorithms to predict different clinical endpoints such as disease staging, treatment response or progression free survival and their trustworthiness. The challenges are open to the whole scientific and tech community interested in AI. They are a unique opportunity to showcase how AI can be used to advance medical research and improve patient outcomes within the CHAIMELEON project.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/744/Logo_Grand_Challenge_-_2.png","https://chaimeleon.grand-challenge.org/","completed","5","","2023-11-02","2023-11-30","2023-08-09 17:13:09","2023-11-14 19:34:50" -"184","topcow23","Topology-Aware Anatomical Segmentation of the Circle of Willis","Segment the Circle of Willis (CoW) vessel components for both CTA and MRA","The aim of the challenge is to extract the CoW angio-architecture from 3D angiographic imaging by segmentation of the vessel components. There are two sub-tasks-binary segmentation of CoW vessels, and multi-class CoW anatomical segmentation. We release a new dataset of joint-modalities, CTA and MRA of the same patient cohort, both with annotations of the anatomy of CoW. Our challenge has two tracks for the same segmentation task, namely CTA track and MRA track. We made use of the clinical information from both modalities during our annotation. And participants can pick whichever modality they want, both CTA and MRA, and choose to tackle the task for either modality.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/733/TopCow_logo.jpg","https://topcow23.grand-challenge.org/","completed","5","","2023-08-20","2023-09-25","2023-08-09 17:16:22","2024-01-31 22:42:32" -"185","circle-of-willis-intracranial-artery-classification-and-quantification-challenge-2023","Circle of Willis Intracranial Artery Classification and Quantification Challenge 2023","Classify the circle of Willis (CoW) configuration and quantification","The purpose of this challenge is to compare automatic methods for classification of the circle of Willis (CoW) configuration and quantification of the CoW major artery diameters and bifurcation angles.","","https://crown.isi.uu.nl/","completed","\N","","2023-05-01","2023-08-15","2023-08-09 22:13:24","2023-09-28 23:24:54" -"186","making-sense-of-electronic-health-record-ehr-race-and-ethnicity-data","Making Sense of Electronic Health Record (EHR) Race and Ethnicity Data","Make sense of electronic health record race and ethnicity data","The urgency of the coronavirus disease 2019 (COVID-19) pandemic has heightened interest in the use of real-world data (RWD) to obtain timely information about patients and populations and has focused attention on EHRs. The pandemic has also heightened awareness of long-standing racial and ethnic health disparities along a continuum from underlying social determinants of health, exposure to risk, access to insurance and care, quality of care, and responses to treatments. This highlighted the potential that EHRs can be used to describe and contribute to our understanding of racial and ethnic health disparities and their solutions. The OMB Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity provides minimum standards for maintaining, collecting, and presenting data on race and ethnicity for all Federal reporting purposes, and defines the two separate constructs of race and ethnicity.","","https://precision.fda.gov/challenges/30","completed","6","","2023-05-31","2023-06-23","2023-08-10 18:28:06","2023-11-14 19:34:58" -"187","the-veterans-cardiac-health-and-ai-model-predictions-v-champs","The Veterans Cardiac Health and AI Model Predictions (V-CHAMPS)","Predict cardiovascular health related outcomes in veterans","To better understand the risk and protective factors in the Veteran population, the VHA IE and its collaborating partners are calling upon the public to develop AI/ML models to predict cardiovascular health outcomes, including readmission and mortality, using synthetically generated Veteran health records. The Challenge consists of two Phases-Phase 1 is focused on synthetic data. In this Phase of the Challenge, AI/ML models will be developed by Challenge participants and trained and tested on the synthetic data sets provided to them, with a view towards predicting outcome variables for Veterans who have been diagnosed with chronic heart failure (please note that in Phase 1, the data is synthetic Veteran health records). Phase 2 will focus on validating and further exploring the limits of the AI/ML models. During this Phase, high-performing AI/ML models from Phase 1 will be brought into the VA system and validated on the real-world Veterans health data within the VHA. These models...","","https://precision.fda.gov/challenges/31","completed","6","","2023-05-25","2023-08-02","2023-08-10 21:41:10","2023-11-14 19:35:53" -"188","predicting-high-risk-breast-cancer-phase-1","Predicting High Risk Breast Cancer - Phase 1","Predicting High Risk Breast Cancer-a Nightingale OS & AHLI data challenge","Every year, 40 million women get a mammogram; some go on to have an invasive biopsy to better examine a concerning area. Underneath these routine tests lies a deep—and disturbing—mystery. Since the 1990s, we have found far more ‘cancers’, which has in turn prompted vastly more surgical procedures and chemotherapy. But death rates from metastatic breast cancer have hardly changed. When a pathologist looks at a biopsy slide, she is looking for known signs of cancer-tubules, cells with atypical looking nuclei, evidence of rapid cell division. These features, first identified in 1928, still underlie critical decisions today-which women must receive urgent treatment with surgery and chemotherapy? And which can be prescribed “watchful waiting”, sparing them invasive procedures for cancers that would not harm them? There is already evidence that algorithms can predict which cancers will metastasize and harm patients on the basis of the biopsy image. Fascinatingly, these algorithms also h...","","https://app.nightingalescience.org/contests/3jmp2y128nxd","completed","15","","2022-06-01","2023-01-12","2023-08-22 17:07:00","2023-10-12 17:55:10" -"189","predicting-high-risk-breast-cancer-phase-2","Predicting High Risk Breast Cancer - Phase 2","Predicting High Risk Breast Cancer-a Nightingale OS & AHLI data challenge","Every year, 40 million women get a mammogram; some go on to have an invasive biopsy to better examine a concerning area. Underneath these routine tests lies a deep—and disturbing—mystery. Since the 1990s, we have found far more ‘cancers’, which has in turn prompted vastly more surgical procedures and chemotherapy. But death rates from metastatic breast cancer have hardly changed. When a pathologist looks at a biopsy slide, she is looking for known signs of cancer-tubules, cells with atypical looking nuclei, evidence of rapid cell division. These features, first identified in 1928, still underlie critical decisions today-which women must receive urgent treatment with surgery and chemotherapy? And which can be prescribed “watchful waiting”, sparing them invasive procedures for cancers that would not harm them? There is already evidence that algorithms can predict which cancers will metastasize and harm patients on the basis of the biopsy image. Fascinatingly, these algorithms also...","","https://app.nightingalescience.org/contests/vd8g98zv9w0p","completed","15","","2023-02-03","2023-05-13","2023-08-22 17:07:01","2023-10-12 17:55:08" -"190","dream-2-in-silico-network-inference","DREAM 2 - In Silico Network Inference","Predict the connectivity and properties of in-silico networks","Three in-silico networks were created and endowed with a dynamics that simulate biological interactions. The challenge consists of predicting the connectivity and some of the properties of one or more of these three networks.","","https://www.synapse.org/#!Synapse:syn2825394/wiki/71150","completed","1","","2007-03-25","\N","2023-08-24 18:54:05","2023-10-12 17:55:03" -"191","dream-3-in-silico-network-challenge","DREAM 3 - In Silico Network Challenge","Reverse engineering of gene networks from biological data","The goal of the in silico challenges is the reverse engineering of gene networks from steady state and time series data. Participants are challenged to predict the directed unsigned network topology from the given in silico generated gene topic_3170sets.","","https://www.synapse.org/#!Synapse:syn2853594/wiki/71567","completed","1","https://doi.org/10.1089/cmb.2008.09TT","2008-06-09","\N","2023-08-25 16:43:41","2023-11-14 19:35:58" -"192","dream-4-in-silico-network-challenge","DREAM 4 - In Silico Network Challenge","Reverse engineer gene regulatory networks","The goal of the in silico network challenge is to reverse engineer gene regulation networks from simulated steady-state and time-series data. Participants are challenged to infer the network structure from the given in silico gene topic_3170sets. Optionally, participants may also predict the response of the networks to a set of novel perturbations that were not included in the provided datasets.","","https://www.synapse.org/#!Synapse:syn3049712/wiki/74628","completed","1","https://doi.org/10.1073/pnas.0913357107","2009-06-09","\N","2023-08-25 16:43:42","2023-11-14 19:36:02" -"193","dream-5-network-inference-challenge","DREAM 5 - Network Inference Challenge","Reverse engineer gene regulatory networks","The goal of this Network Inference Challenge is to reverse engineer gene regulatory networks from gene topic_3170sets. Participants are given four microarray compendia and are challenged to infer the structure of the underlying transcriptional regulatory networks. Three of the four compendia were obtained from microorganisms, some of which are pathogens of clinical relevance. The fourth compendium is based on an in-silico (i.e., simulated) network. Each compendium consists of hundreds of microarray experiments, which include a wide range of genetic, drug, and environmental perturbations (or in the in-silico network case, simulations thereof). Network predictions will be evaluated on a subset of known interactions for each organism, or on the known network for the in-silico case.","","https://www.synapse.org/#!Synapse:syn2787209/wiki/70349","completed","1","https://doi.org/10.1038/nmeth.2016","2010-06-09","2010-10-31","2023-08-25 16:43:43","2023-11-14 19:36:08" -"194","nlp-sandbox-date-annotation","NLP Sandbox Date Annotation","Identify dates in clinical notes","An NLP Sandbox Date Annotator takes as input a clinical note and outputs a list of predicted date annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","1","https://doi.org/10.7303/syn22277123","2021-06-04","2023-09-01","2023-08-25 16:45:22","2023-11-15 22:41:56" -"195","nlp-sandbox-person-name-annotation","NLP Sandbox Person Name Annotation","Identify person names in clinical notes","An NLP Sandbox Person Name Annotator takes as input a clinical note and outputs a list of predicted person name annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","1","https://doi.org/10.7303/syn22277123","2021-06-04","2023-09-01","2023-09-08 16:44:20","2023-09-28 23:59:20" -"196","nlp-sandbox-location-annotation","NLP Sandbox Location Annotation","Identify location information in clinical notes","An NLP Sandbox Location Annotator takes as input a clinical note and outputs a list of predicted location annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","1","https://doi.org/10.7303/syn22277123","2021-06-04","2023-09-01","2023-09-08 16:44:21","2023-09-28 23:59:21" -"197","nlp-sandbox-contact-annotation","NLP Sandbox Contact Annotation","Identify contact information in clinical notes","An NLP Sandbox contact annotator takes as input a clinical note and outputs a list of predicted contact annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","1","https://doi.org/10.7303/syn22277123","2021-06-04","2023-09-01","2023-09-08 16:44:22","2023-09-28 23:59:21" -"198","nlp-sandbox-id-annotation","NLP Sandbox ID Annotation","Identify identifiers in clinical notes","An NLP Sandbox ID annotator takes as input a clinical note and outputs a list of predicted ID annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","1","https://doi.org/10.7303/syn22277123","2021-06-04","2023-09-01","2023-09-08 16:44:22","2023-09-28 23:59:22" -"199","dream-2-bcl6-transcriptomic-target-prediction","DREAM 2 - BCL6 Transcriptomic Target Prediction","Predict BCL6 transcriptomic targets from biological data","A number of potential transcriptional targets of BCL6, a gene that encodes for a transcription factor active in B cells, have been identified with ChIP-on-chip data and functionally validated by perturbing the BCL6 pathway with CD40 and anti-IgM, and by over-expressing exogenous BCL6 in Ramos cell. We subselected a number of targets found in this way (the gold standard positive set), and added a number decoys (genes that have no evidence of being BCL6 targets, named the gold standard negative set), compiling a list of 200 genes in total. Given this list of 200 genes, the challenge consists of identifying which ones are the true targets and which ones are the decoys, using an independent panel of gene topic_3170.","","https://www.synapse.org/#!Synapse:syn3034857/wiki/","completed","1","https://doi.org/10.1073/pnas.0437996100","2007-04-19","\N","2023-09-12 21:26:22","2023-10-12 17:53:55" -"200","dream-2-protein-protein-interaction-network-inference","DREAM 2 - Protein-Protein Interaction Network Inference","Predict a protein-protein interaction network of 47 proteins","For many pairs of bait and prey genes, yeast protein-protein interactions were tested in an unbiased fashion using a high saturation, high-stringency variant of the yeast two-hybrid (Y2H) method. A high confidence subset of gene pairs that were found to interact in at least three repetitions of the experiment but that hadn’t been reported in the literature was extracted. There were 47 yeast genes involved in these pairs. Including self interactions, there are a total of 47*48/2 possible pairs of genes that can be formed with these 47 genes. As mentioned above some of these gene pairs were seen to consistently interact in at least three repetitions of the Y2H experiments-these gene pairs form the gold standard positive set. A second set among these gene pairs were seen never to interact in repeated experiments and were not reported as interacting in the literature; we call this the gold standard negative set. Finally in a third set of gene pairs, which we shall call the undecided s...","","https://www.synapse.org/#!Synapse:syn2825374/wiki/","completed","1","https://doi.org/10.1126/science.1158684","2007-05-24","\N","2023-09-12 21:26:28","2023-10-12 17:54:00" -"201","dream-2-genome-scale-network-inference","DREAM 2 - Genome-Scale Network Inference","Reconstruct genome-scale networks from microarray data","A panel of single-channel microarrays was collected for a particular microorganism, including some already published and some in-print data. The data was appropriately normalized (to the logarithmic scale). The challenge consists of reconstructing a genome-scale transcriptional network for this organism. The accuracy of network inference will be judged using chromatin precipitation and otherwise experimentally verified Transcription Factor (TF)-target interactions.","","https://www.synapse.org/#!Synapse:syn3034894/wiki/74418","completed","1","https://doi.org/10.1371/journal.pbio.0050008","2007-06-05","2007-10-31","2023-09-12 21:26:34","2023-10-12 17:54:03" -"202","dream-2-synthetic-five-gene-network-inference","DREAM 2 - Synthetic Five-Gene Network Inference","Inferring five-gene networks from synthetic data","A synthetic-biology network consisting of 5 interacting genes was created and transfected to an in-vivo model organism. The challenge consists of predicting the connectivity of the five-gene network from in-vivo measurements.","","https://www.synapse.org/#!Synapse:syn3034869/wiki/74411","completed","1","https://doi.org/10.1016/j.cell.2009.01.055","2007-06-20","2007-10-31","2023-09-12 21:26:56","2023-10-12 17:54:05" -"203","dream-3-signaling-cascade-identification","DREAM 3 - Signaling Cascade Identification","Inferring signaling cascade dynamics from flow cytometry data","The concentration of four intracellular proteins or phospho-proteins (X1, X2, X3 and X4) participating in a signaling cascade were measured in about 104 cells by antibody staining and flow cytometry. The idea of this challenge is to explore what key aspects of the dynamics and topology of interactions of a signaling cascade can be inferred from incomplete flow cytometry data.","","https://www.synapse.org/#!Synapse:syn3033068/wiki/74362","completed","1","","2008-06-01","2008-10-31","2023-09-12 21:27:04","2023-10-12 17:54:08" -"204","dream-3-gene-expression-prediction","DREAM 3 - Gene Expression Prediction","Predicting gene expression from gene datasets","Gene expression time course data is provided for four different strains of yeast (S. Cerevisiae), after perturbation of the cells. The challenge is to predict the rank order of induction/repression of a small subset of genes (the prediction targets) in one of the four strains, given complete data for three of the strains, and data for all genes except the prediction targets in the other strain. You are also allowed to use any information that is in the public domain and are expected to be forthcoming about what information was used.","","https://www.synapse.org/#!Synapse:syn3033083/wiki/74369","completed","1","","2008-06-01","2008-10-31","2023-09-12 21:27:12","2023-10-12 17:54:10" -"205","dream-4-predictive-signaling-network-modelling","DREAM 4 - Predictive Signaling Network Modelling","Cell-type specific high-throughput experimental data","This challenge explores the extent to which our current knowledge of signaling pathways, collected from a variety of cell types, agrees with cell-type specific high-throughput experimental data. Specifically, we ask the challenge participants to create a cell-type specific model of signal transduction using the measured activity levels of signaling proteins in HepG2 cell lines. The model, which can leverage prior information encoded in a generic signaling pathway provided in the challenge, should be biologically interpretable as a network, and capable of predicting the outcome of new experiments.","","https://www.synapse.org/#!Synapse:syn2825304/wiki/71129","completed","1","","2009-03-09","\N","2023-09-12 21:27:14","2023-10-12 17:54:30" -"206","dream-3-signaling-response-prediction","DREAM 3 - Signaling Response Prediction","Predict missing protein concentrations from a large corpus of measurements","Approximately 10,000 intracellular measurements (fluorescence signals proportional to the concentrations of phosphorylated proteins) and extracellular measurements (concentrations of cytokines released in response to cell stimulation) were acquired in human normal hepatocytes and the hepatocellular carcinoma cell line HepG2 cells. The datasets consist of measurements of 17 phospho-proteins (at 0 min, 30 min, and 3 hrs) and 20 cytokines (at 0 min, 3 hrs, and 24 hrs) in two cell types (normal and cancer) after perturbations to the pathway induced by the combinatorial treatment of 7 stimuli and 7 selective inhibitors.","","https://www.synapse.org/#!Synapse:syn2825325/wiki/","completed","1","https://doi.org/10.1126%2Fscisignal.2002212","2009-03-09","\N","2023-09-12 21:27:20","2023-10-12 17:54:33" -"207","dream-4-peptide-recognition-domain-prd-specificity-prediction","DREAM 4 - Peptide Recognition Domain (PRD) Specificity Prediction","Predict binding specificity of peptide-antibody interactions","Many important protein-protein interactions are mediated by peptide recognition domains (PRD), which bind short linear sequence motifs in other proteins. For example, SH3 domains typically recognize proline-rich motifs, PDZ domains recognize hydrophobic C-terminal tails, and kinases recognize short sequence regions around a phosphorylatable residue (Pawson, 2003). Given the sequence of the domains, the challenge consists of predicting a position weight matrix (PWM) that describes the specificity profile of each of the given domains to their target peptides. Any publicly accessible peptide specificity information available for the domain may be used.","","https://www.synapse.org/#!Synapse:syn2925957/wiki/72976","completed","1","","2009-06-01","2009-10-31","2023-09-12 21:27:35","2023-10-12 17:54:35" -"208","dream-5-transcription-factor-dna-motif-recognition-challenge","DREAM 5 - Transcription-Factor, DNA-Motif Recognition Challenge","Predict binding intensities for transcription factors from motifs","Transcription factors (TFs) control the expression of genes through sequence-specific interactions with genomic DNA. Different TFs bind preferentially to different sequences, with the majority recognizing short (6-12 base), degenerate ‘motifs’. Modeling the sequence specificities of TFs is a central problem in understanding the function and evolution of the genome, because many types of genomic analyses involve scanning for potential TF binding sites. Models of TF binding specificity are also important for understanding the function and evolution of the TFs themselves. The challenge consists of predicting the signal intensities for the remaining TFs.","","https://www.synapse.org/#!Synapse:syn2887863/wiki/72185","completed","1","https://doi.org/10.1038/nbt.2486","2011-06-01","2011-09-30","2023-09-12 21:27:41","2023-10-12 17:54:36" -"209","dream-5-epitope-antibody-recognition-ear-challenge","DREAM 5 - Epitope-Antibody Recognition (EAR) Challenge","Predict the binding specificity of peptide-antibody interactions","Humoral immune responses are mediated through antibodies. About 1010 to 1012 different antigen binding sites called paratopes are generated by genomic recombination. These antibodies are capable to bind to a variety of structures ranging from small molecules to protein complexes, including any posttranslational modification thereof. When studying protein-antibody interactions, two types of epitopes (the region paratopes interact with) are to be distinguished from each other-i) conformational and ii) linear epitopes. All potential linear epitopes of a protein can be represented by short peptides derived from the primary amino acid sequence. These peptides can be synthesized and arrayed on solid supports, e.g. glass slides (see Lorenz et al., 2009 [1]). By incubating these peptide arrays with antibody mixtures such as human serum or plasma, peptides can be determined that interact with antibodies in a specific fashion.","","https://www.synapse.org/#!Synapse:syn2820433/wiki/71017","completed","1","","2010-06-09","\N","2023-09-12 21:27:44","2023-10-12 17:54:39" -"210","dream-gene-expression-prediction-challenge","DREAM Gene Expression Prediction Challenge","Predict gene expression levels from promoter sequences in eukaryotes","The level by which genes are transcribed is determined in large part by the DNA sequence upstream to the gene, known as the promoter region. Although widely studied, we are still far from a quantitative and predictive understanding of how transcriptional regulation is encoded in gene promoters. One obstacle in the field is obtaining accurate measurements of transcription derived by different promoters. To address this, an experimental system was designed to measure the transcription derived by different promoters, all of which are inserted into the same genomic location upstream to a reporter gene -a yellow florescence protein gene (YFP). The challenge consists of the prediction of the promoter activity given a promoter sequence and a specific experimental condition. To study a set of promoters that share many elements of the regulatory program, and thus are suitable for computational learning, the data pertains to promoters of most of the ribosomal protein genes (RP) of yeast (S....","","https://www.synapse.org/#!Synapse:syn2820426/wiki/71010","completed","1","","2010-07-09","\N","2023-09-12 21:28:00","2023-10-19 23:32:10" -"211","dream-5-systems-genetics-challenge","DREAM 5 - Systems Genetics Challenge","Predict disease phenotypes and infer gene networks from systems genetics data","The central goal of systems biology is to gain a predictive, system-level understanding of biological networks. This can be done, for example, by inferring causal networks from observations on a perturbed biological system. An ideal experimental design for causal inference is randomized, multifactorial perturbation. The recognition that the genetic variation in a segregating population represents randomized, multifactorial perturbations (Jansen and Nap (2001), Jansen (2003)) gave rise to Systems Genetics (SG), where a segregating or genetically randomized population is genotyped for many DNA variants, and profiled for phenotypes of interest (e.g. disease phenotypes), gene expression, and potentially other ‘omics’ variables (protein expression, metabolomics, DNA methylation, etc.; Figure 1. Figure 1 was taken from Jansen and Nap (2001)). In this challenge we explore the use of Systems Genetics data for elucidating causal network models among genes, i.e. Gene Networks (DREAM5 SYSGEN...","","https://www.synapse.org/#!Synapse:syn2820440/wiki/","completed","1","","2010-07-09","\N","2023-09-12 21:28:10","2023-10-12 17:54:42" -"212","dream-6-estimation-of-model-parameters-challenge","DREAM 6 - Estimation of Model Parameters Challenge","Challenge to estimate model parameters","Given the complete model structures (including expressions for the kinetic rate laws) for three gene regulatory networks, participants are asked to develop and/or apply optimization methods, including the selection of the most informative experiments, to accurately estimate parameters and predict outcomes of perturbations in Systems Biology models.","","https://www.synapse.org/#!Synapse:syn2841366/wiki/71372","completed","1","","2011-06-01","2011-10-31","2023-09-12 21:28:12","2023-10-12 17:54:45" -"213","dream-6-flowcap2-molecular-classification-of-acute-myeloid-leukemia-challenge","DREAM 6 - FlowCAP2 Molecular Classification of Acute Myeloid Leukemia Challenge","Diagnose acute myeloid leukemia from patient data using flow cytometry","Flow cytometry (FCM) has been widely used by immunologists and cancer biologists for more than 30 years as a biomedical research tool to distinguish different cell types in mixed populations, based on the expression of cellular markers. It has also become a widely used diagnostic tool for clinicians to identify abnormal cell populations associated with disease. In the last decade, advances in instrumentation and reagent technologies have enabled simultaneous single-cell measurement of tens of surface and intracellular markers, as well as tens of signaling molecules, positioning FCM to play an even bigger role in medicine and systems biology [1,2]. However, the rapid expansion of FCM applications has outpaced the functionality of traditional analysis tools used to interpret FCM data such that scientists are faced with the daunting prospect of manually identifying interesting cell populations in 20 dimensional data from a collection of millions of cells. For these reasons a reliable...","","https://www.synapse.org/#!Synapse:syn2887788/wiki/72178","completed","1","https://doi.org/10.1038/nmeth.2365","2011-06-01","2011-09-30","2023-09-12 21:28:19","2023-11-14 19:36:22" -"214","dream-6-alternative-splicing-challenge","DREAM 6 - Alternative Splicing Challenge","Compare mRNA-seq methods on primate and rhino transcripts","The goal of the mRNA-seq alternative splicing challenge is to assess the accuracy of the reconstruction of alternatively spliced mRNA transcripts from Illumina short-read mRNA-seq. Reconstructed transcripts will be scored against Pacific Biosciences long-read mRNA-seq. The ensuing analysis of the transcriptomes from mandrill and rhinoceros fibroblasts and their derived induced pluripotent stem cells (iPSC), as well as the transcriptome for human Embrionic Stem Cells (hESC) is an opportunity to discover novel biology as well as investigate species-bias of different methods.","","https://www.synapse.org/#!Synapse:syn2817724/wiki/","completed","1","","2011-08-09","\N","2023-09-12 21:28:25","2023-10-12 17:54:50" -"215","causalbench-challenge","CausalBench Challenge","Gene network inference from single-cell perturbation data","Mapping gene-gene interactions in cellular systems is a fundamental step in early-stage drug discovery that helps generate hypotheses on what molecular mechanisms may effectively be targeted by potential future medicines. In the CausalBench Challenge, we invite the machine-learning community to advance the state-of-the-art in deriving gene-gene networks from large-scale real-world perturbational single-cell datasets to improve our ability to glean causal insights into disease-relevant biology.","","https://www.gsk.ai/causalbench-challenge/","completed","16","https://doi.org/10.48550/arXiv.2308.15395","2023-03-01","2023-04-21","2023-09-12 21:28:25","2023-11-14 19:36:27" -"216","iclr-computational-geometry-and-topology-challenge-2022","ICLR Computational Geometry & Topology Challenge 2022","Advancing computational geometry and topology with python","The purpose of this challenge is to foster reproducible research in geometric (deep) learning, by crowdsourcing the open-source implementation of learning algorithms on manifolds. Participants are asked to contribute code for a published/unpublished algorithm, following Scikit-Learn/Geomstats' or pytorch's APIs and computational primitives, benchmark it, and demonstrate its use in real-world scenarios.","","https://github.com/geomstats/challenge-iclr-2022","completed","\N","","\N","2022-04-04","2023-09-13 16:54:06","2023-10-19 23:28:44" -"217","iclr-computational-geometry-and-topology-challenge-2021","ICLR Computational Geometry & Topology Challenge 2021","Advancing computational geometry and topology with python","The purpose of this challenge is to push forward the fields of computational differential geometry and topology, by creating the best data analysis, computational method, or numerical experiment relying on state-of-the-art geometric and topological Python packages.","","https://github.com/geomstats/challenge-iclr-2021","completed","\N","https://doi.org/10.48550/arXiv.2108.09810","\N","2021-05-02","2023-09-13 17:02:12","2023-10-19 23:28:44" -"218","genedisco-challenge","GeneDisco Challenge","Exploring experimental design with active learning for genetics","The GeneDisco challenge is a machine learning community challenge for evaluating batch active learning algorithms for exploring the vast experimental design space in genetic perturbation experiments. Genetic perturbation experiments, using for example CRISPR technologies to perturb the genome, are a vital component of early-stage drug discovery, including target discovery and target validation. The GeneDisco challenge is organized in conjunction with the Machine Learning for Drug Discovery workshop at ICLR-22.","","https://www.gsk.ai/genedisco-challenge/","completed","16","https://doi.org/10.48550/arXiv.2110.11875","2022-01-31","2022-03-31","2023-09-13 17:20:30","2023-10-19 23:32:43" -"219","hidden-treasures-warm-up","Hidden Treasures: Warm Up","Assess genome sequencing software accuracy with unknown variants","In the context of human genome sequencing, software pipelines typically involve a wide range of processing elements, including aligning sequencing reads to a reference genome and subsequently identifying variants (differences). One way of assessing the performance of such pipelines is by using well-characterized datasets such as Genome in a Bottle’s NA12878. However, because the existing NGS reference datasets are very limited and have been widely used to train/develop software pipelines, benchmarking of pipeline performance would ideally be done on samples with unknown variants. This challenge will provide a unique opportunity for participants to investigate the accuracy of their pipelines by testing the ability to find in silico injected variants in FASTQ files from exome sequencing of reference cell lines. It will be a warm up for the community ahead of a more difficult in silico challenge to come in the fall. This challenge will provide users with a FASTQ file of a NA12878 se...","","https://precision.fda.gov/challenges/1","completed","6","","2017-07-17","2017-09-13","2023-09-13 23:31:39","2023-10-12 17:55:23" -"220","data-management-and-graph-extraction-for-large-models-in-the-biomedical-space","Data management and graph extraction for large models in the biomedical space","Advancing biomedical knowledge graphs","This fall, CMU Libraries is hosting a hackathon in partnership with DNAnexus on the topic of data management and graph extraction for large models in the biomedical space. The hackathon will be held in person at CMU, October 19-21, 2023. The hackathon is a collaborative, rather than competitive, event, with each team working on a dedicated part of the problem. The teams will be focused on the following topics-1) Knowledge graph-based validation for variant (genomic) assertions; 2) Continuous monitoring for RLHF and flexible infrastructure for layering assertions with rollback; 3) Flexible tokenization of complex data types; 4) Assertion tracking in large models; 5) Column headers for data harmonization. The outputs are often published as preprints or on the F1000 hackathon channel. Contact Ben Busby (bbusby@dnanexus.com) with any questions about the hackathon or serving as a team lead.","","https://library.cmu.edu/about/news/2023-08/hackathon-2023","completed","\N","","2023-10-19","2023-10-21","2023-09-13 23:32:59","2023-11-14 19:36:32" -"221","cagi2-asthma-twins","CAGI2: Asthma discordant monozygotic twins","Identify genetic differences between asthmatic and healthy twins","The dataset includes whole genomes of 8 pairs of discordant monozygotic twins (randomly numbered from 1 to 16) that is, in each pair identical twins one has asthma and one does not. In addition, RNA sequencing data for each individual is provided. One of the twins in each pair suffers from asthma while the other twin is healthy.","","https://genomeinterpretation.org/cagi2-asthma-twins.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 18:11:42" -"222","cagi4-bipolar","CAGI4: Bipolar disorder","Predicting bipolar disorder from exome data","Bipolar disorder (BD) is a serious mental illness characterized by recurrent episodes of manias and depression, which are syndromes of abnormal mood, thinking and behavior. It affects 1.0-4.5% of the population [1], and it is among the major causes of disability worldwide. This challenge involved the prediction of which of a set of individuals have been diagnosed with bipolar disorder, given exome data. 500 of the 1000 exome samples were provided for training.","","https://genomeinterpretation.org/cagi4-bipolar.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-09-28 18:25:17" -"223","cagi3-brca","CAGI3: BRCA1 & BRCA2","Assess hereditary cancer risk via BRCA gene analysis","In normal cells, the BRCA1 and BRCA2 genes are involved in homologous recombination for double strand break repair and ensure the stability of a cell's genetic material. Mutations in these genes have been linked to development of breast and ovarian cancer. Myriad Genetics created the BRACAnalysis test in order to assess a woman’s risk of developing hereditary breast or ovarian cancer based on detection of mutations in the BRCA1 and BRCA2 genes. This test has become the standard of care in identification of individuals with hereditary breast and ovarian cancer (HBOC) syndrome. It is based on proprietary methods.","","https://genomeinterpretation.org/cagi3-brca.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-19 23:32:48" -"224","cagi2-breast-cancer-pkg","CAGI2: Breast cancer pharmacogenomics","Exploring CHEK2 as a candidate gene for cancer susceptibility","Cell-cycle-checkpoint kinase 2 (CHEK2; OMIM #604373) is a protein that plays an important role in the maintenance of genome integrity and in the regulation of the G2/M cell cycle checkpoint. CHEK2 has been shown to interact with other proteins involved in DNA repair processes such as BRCA1 and TP53. These findings render CHEK2 an 23 attractive candidate susceptibility gene for a variety of cancers.","","https://genomeinterpretation.org/cagi2-breast-cancer-pkg.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 17:46:22" -"225","cagi4-2eqtl","CAGI4: eQTL causal SNPs","Identify regulatory variants causing gene expression differences","Identifying the causal alleles responsible for variation in expression of human genes has been particularly difficult. This is an important problem, as genome-wide association studies (GWAS) suggest that much of the variation underlying common traits and diseases maps within regions of the genome that do not encode protein. A massively parallel reporter assay (MPRA) has been applied to thousands of single nucleotide polymorphisms (SNPs) and small insertion/deletion polymorphisms in linkage disequilibrium (LD) with cis-expression quantitative trait loci (eQTLs). The results identify variants showing differential expression between alleles. The challenge is to identify the regulatory sequences and the expression-modulating variants (emVars) underlying each eQTL and estimate their effects in the assay.","","https://genomeinterpretation.org/cagi4-2eqtl.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-09-29 3-58-33" -"226","cagi1-cbs","CAGI1: CBS","Seeking to understand CBS enzyme function in cysteine production","CBS is a vitamin-dependent enzyme involved in cysteine biosynthesis. The human CBS requires two cofactors for function, vitamin B6 and heme. Homocystinuria due to CBS deficiency (OMIM #236200) is a recessive inborn error of sulfur amino acid metabolism.","","https://genomeinterpretation.org/cagi1-cbs.html","completed","\N","","\N","2010-12-10","2023-09-28 18:19:48","2023-10-12 17:46:07" -"227","cagi2-cbs","CAGI2: CBS","Developing treatment for homocystinuria caused by cbs deficiency","CBS is a vitamin-dependent enzyme involved in cysteine biosynthesis. The human CBS requires two cofactors for function, vitamin B6 and heme. Homocystinuria due to CBS deficiency (OMIM #236200) is a recessive inborn error of sulfur amino acid metabolism.","","https://genomeinterpretation.org/cagi2-cbs.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-11-15 22:46:32" -"228","cagi1-chek2","CAGI1: CHEK2","Variants in the ATM & CHEK2 genes are associated with breast cancer","Predictors will be provided with 41 rare missense, nonsense, splicing, and indel variants in CHEK2.","","https://genomeinterpretation.org/cagi1-chek2.html","completed","\N","","\N","2010-12-10","2023-09-28 18:19:48","2023-10-19 23:32:57" -"229","cagi3-fch","CAGI3: FCH","Seeking to understand genetic basis of common hyperlipidemia disorder","Familial combined hyperlipidemia (FCH; OMIM 14380) the most prevalent hyperlipidemia, is a complex metabolic disorder characterized by variable occurrence of elevated low-density lipoprotein cholesterol (LDL-C) level and high triglycerides (TG)—a condition that is commonly associated with coronary artery disease (CAD).","","https://genomeinterpretation.org/cagi3-fch.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-01 22:26:44" -"230","cagi3-ha","CAGI3: HA","Raising HDL levels to reduce heart disease risk","Hypoalphalipoproteinemia (HA; OMIM #604091) is characterized by severely decreased serum high-density lipoprotein cholesterol (HDL-C) levels and low apolipoprotein A1 (APOA1). Low HDL-C is a risk factor for coronary artery disease.","","https://genomeinterpretation.org/cagi3-ha.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-15 22:46:55" -"231","cagi2-croshn-s","CAGI2: Crohn's disease","Seeking genes linked to Crohn's, an inflammatory bowel disease","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/cagi2-croshn-s.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-11-15 22:46:26" -"232","cagi3-crohn-s","CAGI3: Crohn's disease","Understanding the genetics behind Crohn's disease","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/cagi3-crohn-s.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-15 22:46:27" -"233","cagi4-chron-s-exome","CAGI4: Crohn's exomes","Seeking to understand genetic basis of Crohn's bowel disease","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/cagi4-chron-s-exome.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-15 22:46:23" -"234","cagi4-hopkins","CAGI4: Hopkins clinical panel","Exonic sequences of 83 genes linked to 14 diseases analyzed","The Johns Hopkins challenge, provided by the Johns Hopkins DNA Diagnostic Laboratory (http://www.hopkinsmedicine.org/dnadiagnostic), comprised of exonic sequence for 83 genes associated with one of 14 disease classes, including 5 decoys","","https://genomeinterpretation.org/cagi4-hopkins.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-10-12 17:45:27" -"235","cagi2-mouse-exomes","CAGI2: Mouse exomes","Predict causative variants from exome sequencing data","Predictors were given SNVs and indels found from exome sequencing. Causative variants had been identified for the L11Jus74 and Sofa phenotypes by the use of traditional breeding crosses,47 and the predictions were compared to these results, which were unpublished at the time of the CAGI submissions. The L11Jus74 phenotype is caused by two SNVs (chr11-102258914A> and chr11-77984176A>T), whereas a 15-nucleotide deletion in the Pfas gene is responsible for the Sofa phenotype. The predictions for Frg and Stn phenotypes could not be compared to experimental data, as the causative variants could not successfully be mapped by linkage","","https://genomeinterpretation.org/cagi2-mouse-exomes.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 17:45:19" -"236","cagi3-mrn-mre11","CAGI3: MRE11","Genomes are subject to constant threat by damaging agents that generate dna","Predict probability of pathogenicity (a number between 0 and 1) for individual rare variants of MRE11 and NBS1.","","https://genomeinterpretation.org/cagi3-mrn.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-15 22:47:19" -"237","cagi4-naglu","CAGI4: NAGLU","Predicting enzymatic activity of NAGLU mutants","NAGLU is a lysosomal glycohydrolyase. Deficiency of NAGLU causes the rare disorder Mucopolysaccharidosis IIIB or Sanfilippo B disease. Naturally occurring NAGLU mutants have been assayed for enzymatic activity in transfected cell lysates. The challenge is to predict the fractional activity of each mutant protein compared to the wild-type enzyme.","","https://genomeinterpretation.org/cagi4-naglu.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-15 22:47:24" -"238","cagi4-npm-alk","CAGI4: NPM: ALK","Predicting kinase activity of NPM-ALK fusion mutants","NPM-ALK is a fusion protein in which the ALK tyrosine kinase is constitutively activated, contributing to cancer. NPM-ALK constructs with mutations in the kinase domain have been assayed in extracts of transfected cells. The challenge is to predict the kinase activity and the Hsp90 binding affinity of the mutant proteins relative to the reference NPM-ALK fusion protein.","","https://genomeinterpretation.org/cagi4-npm-alk.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-15 22:47:32" -"239","cagi3-mrn-nbs1","CAGI3: NBS1","Predicting Pathogenicity of Rare MRE11 and NBS1 Variants","Predict probability of pathogenicity (a number between 0 and 1) for individual rare variants of MRE11 and NBS1.","","https://genomeinterpretation.org/cagi3-mrn.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-16 18:38:55" -"240","cagi3-p16","CAGI3: p16","Assessing p16 protein variants' effects on cell growth","Evaluate how different variants of p16 protein impact its ability to block cell proliferation.","","https://genomeinterpretation.org/cagi3-p16.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-20 23:28:57" -"241","cagi2-p53","CAGI2: p53 reactivation","Predictors are asked to submit predictions on the effect of the cancer rescue","The transcription factor p53 is a central tumor suppressor protein that controls DNA repair, cell cycle arrest, and apoptosis (programmed cell death). About half of human cancers have p53 mutations that inactivate p53. Over 250,000 US deaths yearly are due to tumors that express full-length p53 that has been inactivated by a single point mutation. For the past several years, the group of Rick Lathrop at University of California, Irvine, has been engaged in a complete functional census of p53 second-site suppressor (“cancer rescue”) mutations. These cancer rescue mutations are additional amino acids changes (to otherwise cancerous p53 mutations), which have been found to rescue p53 tumor suppressor function, reactivating otherwise inactive p53. These intragenic rescue mutations reactivate cancer mutant p53 in yeast and human cell assays by providing structural changes that compensate for the cancer mutation.","","https://genomeinterpretation.org/cagi2-p53.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-20 23:28:58" -"242","cagi1-pgp","CAGI1: PGP","CAGI challenges utilizing public genomic and phenotypic data resources","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/cagi1-pgp.html","completed","\N","","\N","2010-12-10","2023-09-28 18:19:48","2023-09-27 21:05:22" -"243","cagi2-pgp","CAGI2: PGP","CAGI challenges utilizing public genomic and phenotypic data resources","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/cagi2-pgp.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-09-27 21:05:23" -"244","cagi3-pgp","CAGI3: PGP","CAGI challenges utilizing public genomic and phenotypic data resources","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/cagi3-pgp.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-09-27 21:05:23" -"245","cagi4-pgp","CAGI4: PGP","CAGI challenges utilizing public genomic and phenotypic data resources","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/cagi4-pgp.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-01 22:26:17" -"246","cagi4-pyruvate-kinase","CAGI4: Pyruvate kinase","Predicting mutation impacts on pyruvate kinase activity and regulation","Pyruvate kinase catalyzes the last step in glycolysis and is regulated by allosteric effectors. Variants in the gene encoding the isozymes expressed in red blood cells and liver, including missense variants mapping near the effector binding sites, cause PK deficiency. A large set of single amino acid mutations in the liver enzyme has been assayed in E. coli extracts for the effect on allosteric regulation of enzyme activity. The challenge is to predict the impacts of mutations on enzyme activity and allosteric regulation.","","https://genomeinterpretation.org/cagi4-pyruvate-kinase.html","completed","\N","","\N","2015-01-11","2023-09-28 18:19:48","2023-11-01 22:26:25" -"247","cagi2-rad50","CAGI2: RAD50","Assessing RAD50 variants for breast cancer risk","RAD50 is a candidate intermediate-risk breast cancer susceptibility gene. The RAD50 data provided for CAGI challenge include a list of potentially interesting sequence variants observed from sequencing RAD50 gene in about 1,400 breast cancer cases and 1,200 ethnically matched controls. Variants in the list were observed between 1 and 20 times.","","https://genomeinterpretation.org/cagi2-rad50.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-11-15 22:47:40" -"248","cagi2-risksnps","CAGI2: riskSNPs","Exploring molecular mechanisms linking SNPs to disease risk","The goal of this experiment is to explore current understanding of the molecular level mechanisms underlying associations between SNPs and disease risk, incorporating expertise in each of the known mechanism areas, and as far as possible assigning possible mechanisms for each association locus. The correct mechanisms are unknown, so there can be no ranking of accuracy-that is not the point of the experiment. Rather, the goal is to ascertain which mechanisms appear most relevant, how confidently they can be assigned, and what fraction of loci can currently be assigned plausible mechanisms.","","https://genomeinterpretation.org/cagi2-risksnps.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-19 23:33:11" -"249","cagi3-risksnps","CAGI3: riskSNPs","Exploring molecular mechanisms linking SNPs to disease risk","The goal of this experiment is to explore current understanding of the molecular level mechanisms underlying associations between SNPs and disease risk, incorporating expertise in each of the known mechanism areas, and as far as possible assigning possible mechanisms for each association locus. The correct mechanisms are unknown, so there can be no ranking of accuracy-that is not the point of the experiment. Rather, the goal is to ascertain which mechanisms appear most relevant, how confidently they can be assigned, and what fraction of loci can currently be assigned plausible mechanisms.","","https://genomeinterpretation.org/cagi3-risksnps.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-19 23:33:13" -"250","cagi2-nav1-5","CAGI2: SCN5A","Predict the effect of SCN5A mutants in cardiac electrophysiology","The cardiac action potential (AP) is the sum of a number of distinct ionic currents. It can be divided into five phases (phase 0‐4). From pacemaker cells of the SA node the initial depolarizing wave front will spread throughout the cardiomyocytes via gap junctions. If the depolarization is sufficient voltage‐dependent sodium channels (Nav1.5) are activated and allow Na+ influx. This results in a further depolarization of the membrane which will lead to opening of even more Nav channels. This positive feedback mechanism is seen as the rapid upstroke in the initial phase (phase 0) of the action potential. Nav1.5 is encoded by SCN5A and mutations in this gene have been associated with various diseases such as Atrial fibrillation, Long QT syndrome, Cardiac Conduction Defect, Sick Sinus Disease, and Brugada Syndrome (BrS).","","https://genomeinterpretation.org/cagi2-nav1.5.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-11-14 19:36:54" -"251","cagi2-mr-1","CAGI2: Shewanella oneidensis strain MR-1","How MR-1 affect the fitness of that gene in a given condition","Predictors are asked to submit predictions on how insertions in the given gene of MR-1 affect the fitness of that gene in a given condition (stressor).","","https://genomeinterpretation.org/cagi2-mr-1.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:55","2023-11-14 19:37:40" -"252","cagi3-mr-1","CAGI3: Shewanella oneidensis strain MR-1","How MR-1 affect the fitness of that gene in a given condition","Predictors are asked to submit predictions on how insertions in the given gene of MR-1 affect the fitness of that gene in a given condition (stressor).","","https://genomeinterpretation.org/cagi3-mr-1.html","completed","\N","","\N","2013-04-25","2023-09-28 18:20:01","2023-11-14 19:37:47" -"253","cagi4-sickkids","CAGI4: SickKids","Match genome sequence to clinical descriptions","Realizing the promise of precision medicine will require developing methods for interpreting genome sequence data to infer individuals’ phenotypic traits and predispositions to disease. This challenge involves 25 children with suspected genetic disorders who were referred for clinical genome sequencing. Predictors are given their genome sequences and their clinical phenotypic descriptions, as provided to the diagnostic laboratory, and asked to predict which genome corresponds to which clinical description. Additionally, identify the diagnostic variants underlying the predictions. Optionally, identify predictive secondary variants conferring high risk of other diseases whose phenotypes are not reported in the clinical descriptions.","","https://genomeinterpretation.org/cagi4-sickkids.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-14 19:38:30" -"254","cagi4-sumo-ligase","CAGI4: SUMO ligase","Predict effect of the variants on SUMO ligase","SUMO ligase identifies target proteins and covalently attaches SUMO to them, thereby modulating the functions of hundreds of proteins including proteins implicated in cancer, neurodegeneration, and other diseases. A large library of missense mutations in human SUMO ligase has been assessed for competitive growth in a high-throughput yeast-based complementation assay. The challenge is to predict the effect of mutations on function, as measured by the change in fractional representation of each mutant SUMO ligase clone, relative to wild-type clones, in a competitive yeast growth assay.","","https://genomeinterpretation.org/cagi4-sumo-ligase.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-14 19:38:48" -"255","cagi3-splicing","CAGI3: TP53 splicing","Which TP53 mutations potentially contribute to cancer","The function of exonic splicing regulatory elements can be undermined by DNA sequence variation and in some cases can contribute to pathogenesis. Thousands of disease-causing mutations disrupt exonic splicing regulatory elements. These data suggest that >25 percent of missense mutations may impact pre-mRNA splicing rather than mRNA translation. Using minigene constructs derived from a fragment of the TP53 gene, we have experimentally determined if each mutation influences splicing fidelity in HEK293T cells. We hope that CAGI participants will be able to predict the outcome of our experiments. A long-term goal will be the computational prioritization of disease-causing mutations prior to experimental validation. This contribution is expected to have major impacts in understanding the pathogenic basis of disease-causing mutations.","","https://genomeinterpretation.org/cagi3-splicing.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-14 19:39:00" -"256","cagi4-warfarin","CAGI4: Warfarin exomes","Predict the therapeutic doses of warfarin","With over 33 million prescriptions in 2011, warfarin is the most commonly used anticoagulant for preventing thromboembolic events. Warfarin has a twenty-fold inter-individual dose variability and a narrow therapeutic index, and it is responsible for a third of adverse drug event hospitalizations in older Americans [2]. Alternatives to warfarin, such as direct thrombin inhibitors and factor Xa inhibitors, are now available. However, these are more expensive, irreversible, and may cause a higher rate of acute coronary events compared to warfarin [3,4]. Thus, warfarin remains a mainstay of anticoagulant therapy, and better methods of dosing warfarin will lead to fewer adverse events due to overcoagulation.","","https://genomeinterpretation.org/cagi4-warfarin.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-14 19:39:08" -"257","cagi6-calmodulin","CAGI6: Calmodulin","Predict competitive growth score of different Calmodulin variants","Calmodulin (CaM) is a ubiquitous calcium (Ca2+) sensor protein interacting with more than 200 molecular partners, thereby regulating a variety of biological processes. Missense point mutations in the genes encoding CaM have been associated with ventricular tachycardia and sudden cardiac death. A library encompassing up to 17 point mutations was assessed by far-UV circular dichroism (CD) by measuring melting temperature (Tm) and percentage of unfolding (%unfold) upon thermal denaturation at pH and salt concentration that mimic the physiological conditions. The challenge is to predict- the Tm and %unfold values for isolated CaM variants under Ca2+-saturating conditions (Ca2+-CaM) and in the Ca2+-free (apo) state; whether the point mutation stabilizes or destabilizes the protein (based on Tm and %unfold).","","https://genomeinterpretation.org/cagi6-cam.html","completed","1","","\N","2021-12-31","2023-09-28 18:19:48","2023-11-15 22:47:52" -"258","cagi2-splicing","CAGI2: splicing","Compare exons to understand the mechanisms underlying pre-mRNA splicing errors","Accurate precursor mRNA (pre-mRNA) splicing is required for the expression of protein coding genes from the human genome. In this process, intervening sequences (introns) are removed from pre-mRNA and coding/regulatory sequences (exons) are ligated together generating a mature mRNA. A large ribonucleoprotein machine called the spliceosome assembles de novo upon every nascent intron and catalyzes the chemical steps of splicing.","","https://genomeinterpretation.org/cagi2-splicing.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-18 15:32:55" -"259","cagi6-lc-arsa","CAGI6: ARSA","Predict the effect of naturally occurring missense mutations","Metachromatic Leukodystrophy (MLD) is an autosomal recessive, lysosomal-storage disease caused by mutations in Arylsulfatase A (ARSA) and toxic accumulation of sulfatide substrate. Genome sequencing has revealed hundreds of protein-altering, ARSA missense variants, but the functional effect of most variants remains unknown. ARSA enzyme activity using a high-throughput cellular assay was measured for a large set of variants of known significance and variants of unknown significance. The challenge is to predict the fractional enzyme activity of each mutant protein compared to the wildtype protein.","","https://genomeinterpretation.org/cagi6-lc-arsa.html","completed","1","","\N","2022-11-16","2023-09-28 18:20:23","2023-11-14 19:39:42" -"260","predict-hits-for-the-wdr-domain-of-lrrk2","CACHE1: Predict Hits for The WDR Domain of LRRK2","Finding ligands targeting the central cavity of the WDR domain of LRRK2","The first CACHE Challenge target is LRRK2, the most commonly mutated gene in familial Parkinson's Disease. Participants are asked to find hits for the WD40 repeat (WDR) domain of LRRK2. Read more under Details below.","","https://cache-challenge.org/challenges/predict-hits-for-the-wdr-domain-of-lrrk2","completed","17","","2021-12-01","2022-01-31","2023-09-27 19:01:55","2023-11-14 19:39:53" -"261","finding-ligands-targeting-the-conserved-rna-binding-site-of-sars-cov-2-nsp13","CACHE2: Finding Ligands Targeting The Conserved RNA Binding Site of SARS-CoV-2 NSP13","Target the NSP13 helicase of SARS-CoV-2","Predicted compounds will be procured and tested at CACHE using both enzymatic and binding assays","","https://cache-challenge.org/challenges/finding-ligands-targeting-the-conserved-rna-binding-site-of-sars-cov-2-nsp13","completed","17","","2022-06-22","2022-09-04","2023-09-27 19:02:43","2023-11-14 19:41:01" -"262","finding-ligands-targeting-the-macrodomain-of-sars-cov-2-nsp3","CACHE3: Finding ligands targeting the macrodomain of SARS-CoV-2 Nsp3","Study the macrodomain of SARS-CoV-2 Nsp3 for potential therapeutic applications","To predict ligands that bind to the ADPr site of SARS-CoV-2 Nsp3 macrodomain (Mac1).","","https://cache-challenge.org/challenges/finding-ligands-targeting-the-macrodomain-of-sars-cov-2-nsp3","completed","17","","2022-11-02","2023-01-01","2023-09-27 19:03:13","2023-10-16 19:01:19" -"263","finding-ligands-targeting-the-tkb-domain-of-cblb","CACHE4: Finding ligands targeting the TKB domain of CBLB","Investigate the TKB domain of CBLB to discover novel compounds for treatment","Predict compounds that bind to the closed conformation of the CBLB TKB domain with novel chemical templates and KD below 30 micromolar.","","https://cache-challenge.org/challenges/finding-ligands-targeting-the-tkb-domain-of-cblb","completed","17","","2023-03-09","2023-05-09","2023-09-27 19:03:14","2023-10-16 19:01:22" -"264","rare-disease-ai-hackathon","Rare Disease AI Hackathon","Advance rare disease diagnosis using artificial intelligence (AI) models","Bring AI and medical experts together to build open source models for rare diseases. Create zero-barrier access to rare disease expertise for patients, researchers and physicians. Use AI to Uncover novel links between rare diseases. Establish validation methods for medical AI models. Jumpstart an open source community for rare disease AI models. Launch models for Beta testing on Hypophosphatasia.ai and EhlersDanlos.ai.","","https://www.rarediseaseaihackathon.org/","active","\N","","2023-09-30","2024-06-15","2023-09-27 19:10:40","2024-02-05 16:55:39" -"265","cometh-benchmark","COMETH Benchmark","Quantify tumor heterogeneity","Successful treatment of cancer is still a challenge and this is partly due to a wide heterogeneity of cancer composition across patient population. Unfortunately, accounting for such heterogeneity is very difficult. Clinical evaluation of tumor heterogeneity often requires the expertise of anatomical pathologists and radiologists.This benchmark is dedicated to the quantification of intra-tumor heterogeneity using appropriate statistical methods on cancer omics data.In particular, it focuses on estimating cell types and proportion in biological samples based on methylation and methylome data sets. The goal is to explore various statistical methods for source separation/deconvolution analysis (Non-negative Matrix Factorization, Surrogate Variable Analysis, Principal component Analysis, Latent Factor Models, ...) using both RNA-seq and methylome data.","","https://www.codabench.org/competitions/218/","completed","10","","2020-06-14","2020-12-29","2023-09-28 23:25:52","2023-11-14 19:41:05" -"266","the-miccai-2014-machine-learning-challenge","The MICCAI 2014 Machine Learning Challenge","Predict binary and continuous phenotypes from Structural Brain MRI","Machine learning tools are increasingly applied to brain MRI scans for predicting individual-level clinical phenotypes. Despite methodological advancements, benchmark studies with standardized datasets are scarce, hindering tool validation and comparison. The MICCAI 2014 Machine Learning Challenge (MLC) addresses this gap, utilizing four large-scale (N > 70) brain MRI datasets with clinically relevant phenotypes. The aim is to showcase the current state of neuroimage-based prediction, drawing machine-learning practitioners to MICCAI and medical image computing. MICCAI 2014 MLC complements the main conference, the Machine Learning in Medical Imaging Workshop, and the CADDementia challenge focused on Alzheimer's diagnosis from brain MR scans.","","https://competitions.codalab.org/competitions/1471","completed","9","","2014-04-16","2014-06-14","2023-09-28 23:36:12","2023-11-14 19:41:17" -"267","cagi6-annotate-all-missense","CAGI6: Annotate All Missense","Predict the functional effect of every coding SNV in the human genome","dbNSFP currently describes 81,782,923 possible protein-altering variants in the human genome. The challenge is to predict the functional effect of every such variant. For the vast majority of these missense and nonsense variants, the functional impact is not currently known, but experimental and clinical evidence is accruing rapidly. Rather than drawing upon a single discrete dataset as typical with CAGI, predictions will be assessed by comparing with experimental or clinical annotations made available after the prediction submission date, on an ongoing basis. If predictors assent, predictions will also be incorporated into dbNSFP.","","https://genomeinterpretation.org/cagi6-annotate-all-missense.html","completed","1","","2021-06-01","2021-10-11","2023-06-23 00:00:00","2023-11-15 22:48:16" -"268","cagi6-hmbs","CAGI6: HMBS","Submit the fitness score for each of the variants in the HMBS gene","Hydroxymethylbilane synthase (HMBS), also known as porphobilinogen deaminase (PBGD) or uroporphyrinogen I synthase, is an enzyme involved in heme production. In humans, variants that affect HMBS function result in acute intermittent porphyria (AIP), an autosomal dominant genetic disorder caused by a build-up of porphobilinogen in the cytoplasm. A large library of HMBS missense variants was assessed with respect to their effects on protein function using a high-throughput yeast complementation assay. The challenge is to predict the functional effects of these variants.","","https://genomeinterpretation.org/cagi6-hmbs.html","completed","1","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-11-15 22:48:31" -"269","cagi6-id-panel","CAGI6: Intellectual Disability Panel","Analyze data of Intellectual Disability Panel to identify causative variants","The objective in this challenge is to predict a patient's clinical phenotype and the causal variant(s) based on their gene panel sequences. Sequence data for 74 genes from a cohort of 500 patients with a range of neurodevelopmental presentations (intellectual disability, autistic spectrum disorder, epilepsy, microcephaly, macrocephaly, hypotonia, ataxia) has been made available for this challenge. Additional data from 150 patients from the same clinical study is available for training and validation.","","https://genomeinterpretation.org/cagi6-id-panel.html","completed","1","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-11-14 19:43:25" -"270","cagi6-mapk1","CAGI6: MAPK1","Predict the ΔΔG(H2O) value for the MAPK1","MAPK1 (ERK2) is active as serine/threonine kinase in the Ras-Raf-MEK-ERK signal transduction cascade that regulates cell proliferation, transcription, differentiation, and cell cycle progression. MAPK1 is activated by phosphorylation which occurs with strict specificity by MEK1/2 on Thr185 and Tyr187, and may also act as a transcriptional repressor independent of its kinase activity. A library of eleven missense variants selected from the COSMIC database was assessed by near and far-UV circular dichroism and intrinsic fluorescence spectra to determine thermodynamic stability at different concentrations of denaturant. These data were used to calculate a ΔΔGH20 value; i.e., the difference in unfolding free energy ΔGH20 between each variant and the wildtype protein, both in phosphorylated and unphosphorylated forms. The challenge is to predict these two ΔΔGH20 values and the catalytic efficiency (kcat/km)mut/(kcat/km)wt, as determined by a fluorescence assay, of the phosphorylated fo...","","https://genomeinterpretation.org/cagi6-mapk1.html","completed","1","","2021-07-08","2021-10-11","2023-06-23 00:00:00","2023-11-15 22:48:43" -"271","cagi6-mapk3","CAGI6: MAPK3","Predict the ΔΔG(H2O) value for the MAPK3","MAPK3 (ERK1) is active as serine/threonine kinase in the Ras-Raf-MEK-ERK signal transduction cascade that regulates cell proliferation, transcription, differentiation, and cell cycle progression. MAPK3 is activated by phosphorylation which occurs with strict specificity by MEK1/2 on Thr202 and Tyr204, and may also act as a transcriptional repressor independent of its kinase activity. A library of twelve missense variants selected from the COSMIC database was assessed by near and far-UV circular dichroism and intrinsic fluorescence spectra to determine thermodynamic stability at different concentrations of denaturant. These data were used to calculate a ΔΔGH20 value; i.e., the difference in unfolding free energy ΔGH20 between each variant and the wildtype protein, both in phosphorylated and unphosphorylated forms. The challenge is to predict these two ΔΔGH20 values and the catalytic efficiency (kcat/km)mut/(kcat/km)wt, as determined by a fluorescence assay, of the phosphorylated fo...","","https://genomeinterpretation.org/cagi6-mapk3.html","completed","1","","2021-08-04","2021-10-11","2023-06-23 00:00:00","2023-11-15 22:48:47" -"272","cagi6-mthfr","CAGI6: MTHFR","Submit predictions for each missense variant in the MTHFR","Methylenetetrahydrofolate reductase (MTHFR) catalyzes the production of 5-methyltetrahydrofolate, which is needed for conversion of homocysteine to methionine. Humans with variants affecting MTHFR function present with a wide range of phenotypes, including homocystinuria, homocysteinemia, developmental delay, severe mental retardation, psychiatric disturbances, and late-onset neurodegenerative disorders. A further complication to interpretation of variants in this gene is a common variant, Ala222Val, carried by a large fraction of the human population. A large library of MTHFR missense variants was assessed with respect to their effects on protein function using a high-throughput yeast complementation assay. The challenge is to predict the functional effects of these variants in two different settings- for the wildtype protein, and for the protein with the common Ala222Val variant.","","https://genomeinterpretation.org/cagi6-mthfr.html","completed","1","","2021-05-03","2021-06-30","2023-06-23 00:00:00","2023-11-15 22:48:59" -"273","cagi6-prs","CAGI6: Polygenic Risk Scores","Estimate polygenic risk scores (PRS) for complex diseases","Polygenic risk scores (PRS) have potential clinical utility for risk surveillance, prevention and personalized medicine. Participants will be provided with datasets of four real phenotypes (Type 2 Diabetes, Breast Cancer, Inflammatory Bowel Disease and Coronary Artery Disease) and of thirty simulated phenotypes representing a range of genetic architectures of common polygenic diseases. The challenge is to predict the disease outcomes of individuals in held-out validation cohorts.","","https://genomeinterpretation.org/cagi6-prs.html","completed","1","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-11-14 19:42:40" -"274","cagi6-rgp","CAGI6: Rare Genomes Project","Identify causative variants in rare disease genomes for diagnosis","The Rare Genomes Project (RGP) is a direct-to-participant research study on the utility of genome sequencing for rare disease diagnosis and gene discovery. The study is led by genomics experts and clinicians at the Broad Institute of MIT and Harvard. Research subjects are consented for genomic sequencing and the sharing of their sequence and phenotype information with researchers working to understand the molecular causes of rare disease. When a candidate disease variant believed to be related to the phenotype is identified, the variant is confirmed with Sanger sequencing in a clinical setting and returned to the participant via his or her local physician. In this challenge, whole genome sequence data and phenotype data from a subset of the solved and unsolved RGP families will be provided. Participants in the challenge will try to identify the causative variant(s) in each case. For the unsolved cases, prioritized variants from the participating teams will be examined to see if ad...","","https://genomeinterpretation.org/cagi6-rgp.html","completed","1","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:27" -"275","cagi6-invitae","CAGI6: Sherloc clinical classification","122,000 coding variants predicted for ClinVar","Invitae is a genetic testing company that publishes their variant interpretations to ClinVar. In this challenge, over 122,000 previously uncharacterized variants are provided, spanning the range of effects seen in the clinic. Following the close of this challenge, Invitae will submit their interpretations for these variants to ClinVar. Predictors are asked to interpret the pathogenicity of these variants, and the clinical utility of predictions will be assessed across multiple categories by Invitae.","","https://genomeinterpretation.org/cagi6-invitae.html","completed","1","","2021-07-08","2021-12-01","2023-06-23 00:00:00","2023-11-16 17:44:21" -"276","cagi6-splicing-vus","CAGI6: Splicing VUS","Predict whether vus disrupt splicing and contribute to genetic disorders","Variants causing aberrant splicing have been implicated in a range of common and rare disorders, including retinitis pigmentosa, autism spectrum disorder, amyotrophic lateral sclerosis, and a variety of cancers. However, such variants are frequently overlooked by diagnostic sequencing pipelines, leading to missed diagnoses for patients. Clinically ascertained variants of unknown significance underwent whole-blood based RT-PCR to test for impact on splicing. The challenge is to predict which of the tested variants disrupt splicing.","","https://genomeinterpretation.org/cagi6-splicing-vus.html","completed","1","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-11-14 19:44:47" -"277","cagi6-stk11","CAGI6: STK11","Impact of variants in STK11 gene for Peutz-Jeghers syndrome","Serine/Threonine Kinase 11 (STK11) is considered a master kinase that functions as a tumor suppressor and nutrient sensor within a heterotrimeric complex with pseudo-kinase STRAD-alpha and structural protein MO25. Germline variants resulting in loss of STK11 define Peutz-Jaghers Syndrome, an autosomal dominant cancer predisposition syndrome marked by gastrointestinal hamartomas and freckling of the oral mucosa. Somatic loss of function variants, both nonsense and missense, occur in 15-30% of non-small cell lung adenocarcinomas, where they correlate clinically with insensitivity to anti-PD1 monoclonal antibody therapy. The challenge is to predict the impact on STK11 function for each missense variant in relation to wildtype STK11.","","https://genomeinterpretation.org/cagi6-stk11.html","completed","1","","2021-06-08","2021-09-01","2023-06-23 00:00:00","2023-11-16 17:44:00" -"278","qbi-hackathon","QBI hackathon","The QBI hackathon","The QBI hackathon is a 48-hour event connecting the vibrant Bay Area developer community with the scientists from UCSF, UCB and UCSC, during which we work together on the cutting edge biomedical problems. Advances in computer vision, AI, and machine learning have enabled computers to pick out cat videos, recognize people’s faces from photos, play video games and drive cars. More recently, application of deep neural nets to protein structure prediction completely revolutionized the field. We look forward to seeing how far we can push science ahead when we apply these latest algorithms to biomedically relevant light microscopy, electron microscopy, and proteomics data. If you love FFTs, transformers, language models, topological data processing, or simply writing code, this is your chance to apply your skills to make an impact on global healthcare. Beyond the actual event, we hope to establish a better connection between talented developers and scientists in the Bay Area, so that we...","","https://www.eventbrite.com/e/qbi-hackathon-2023-tickets-633794304827?aff=oddtdtcreator","completed","\N","","2023-11-04","2023-11-05","2023-10-06 21:22:51","2023-11-15 22:49:20" -"279","niddk-central-repository-data-centric-challenge","NIDDK Central Repository Data-Centric Challenge","Enhance NIDDK datasets for future Artificial Intelligence (AI) applications","The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repository (https://repository.niddk.nih.gov/home/) is conducting a Data Centric Challenge aimed at augmenting existing Repository data for future secondary research including data-driven discovery by artificial intelligence (AI) researchers. The NIDDK Central Repository (NIDDK-CR) program strives to increase the utilization and impact of the resources under its guardianship. However, lack of standardization and consistent metadata within and across studies limit the ability of secondary researchers to easily combine datasets from related studies to generate new insights using data science methods. In the fall of 2021, the NIDDK-CR began implementing approaches to augment data quality to improve AI-readiness by making research data FAIR (findable, accessible, interoperable, and reusable) via a small pilot project utilizing Natural Language Processing (NLP) to tag study variables. In 2022, the NIDD...","","https://www.challenge.gov/?challenge=niddk-central-repository-data-centric-challenge","completed","\N","","2023-09-20","2023-11-03","2023-10-18 16:58:17","2023-11-15 22:49:26" -"280","stanford-ribonanza-rna-folding","Stanford Ribonanza RNA Folding","A path to programmable medicine and scientific breakthroughs","Ribonucleic acid (RNA) is essential for most biological functions. A better understanding of how to manipulate RNA could help usher in an age of programmable medicine, including first cures for pancreatic cancer and Alzheimer’s disease as well as much-needed antibiotics and new biotechnology approaches for climate change. But first, researchers must better understand each RNA molecule's structure, an ideal problem for data science.","","https://www.kaggle.com/competitions/stanford-ribonanza-rna-folding","completed","8","","2023-08-23","2023-11-24","2023-10-23 20:58:06","2023-11-15 22:49:31" -"281","uls23","Universal Lesion Segmentation Challenge '23","Advancements, challenges, and a universal solution emerges","Significant advancements have been made in AI-based automatic segmentation models for tumours. Medical challenges focusing on e.g. Liver, kidney, or lung tumours have resulted in large performance improvements for segmenting these types of lesions. However, in clinical practice there is a need for versatile and robust models capable of quickly segmenting the many possible lesions types in the thorax-abdomen area. Developing a universal lesion segmentation (uls) model that can handle this diversity of lesions types requires a well-curated and varied dataset. Whilst there has been previous work on uls [6-8], most research in this field has made extensive use of a single partially annotated dataset [9], containing only the long- and short-axis diameters on a single axial slice. Furthermore, a test set containing 3d segmentation masks used during evaluation on this dataset by previous publications is not publicly available.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/747/ULS23_logo_aoB8tlx.png","https://uls23.grand-challenge.org/","active","5","","2023-10-29","2024-03-17","2023-11-02 15:35:22","2023-11-17 21:29:35" -"282","vessel12","VESSEL12","Assess methods for blood vessels in lung CT images","The VESSEL12 challenge compares methods for automatic (and semi-automatic) segmentation of blood vessels in the lungs from CT images.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/1/logo.png","https://vessel12.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.media.2014.07.003","2011-11-25","2012-04-01","2023-11-08 00:42:00","2023-11-17 21:30:05" -"283","crass","CRASS","Invites participants to submit clavicle segmentation results","Crass stands for chest radiograph anatomical structure segmentation. The challenge currently invites participants to send in results for clavicle segmentation algorithms.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/5/logo.png","https://crass.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:09:56" -"284","anode09","ANODE09","Automatic pulmonary nodule detection systems in chest CT scans","ANODE09 is an initiative to compare systems that perform automatic detection of pulmonary nodules in chest CT scans on a single common database, with a single evaluation protocol.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/7/logo.png","https://anode09.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.media.2010.05.005","\N","\N","2023-11-08 00:42:00","2023-11-17 23:17:55" -"285","cause07","CAUSE07","Compares algorithms for caudate nucleus segmentation in brain MRI scans","The goal of CAUSE07 is to compare different algorithms to segment the caudate nucleaus from brain MRI scans.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/8/logo.png","https://cause07.grand-challenge.org/","completed","5","","2007-10-26","\N","2023-11-08 00:42:00","2023-11-17 21:34:10" -"286","subsolidnodules","Subsolid Nodules","We present results of our segmentation method for subsolid lung nodules","We are presenting results of our segmentation method for subsolid lung nodules.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/10/logo.png","https://subsolidnodules.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-08 21:24:01" -"287","caddementia","CADDementia","Classification in AD, MCI, and healthy controls using MRI data","We seek algorithms that perform multi-class classification of patients with Alzheimer's disease (AD), patients with mild cognitive impairment (MCI) and healthy controls (CN) using multi-center structural MRI data.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/17/logo3_100.png","https://caddementia.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.neuroimage.2015.01.048","\N","\N","2023-11-08 00:42:00","2023-11-17 23:18:33" -"288","mitos-atypia-14","MITOS-ATYPIA-14","Mitosis detection and nuclear atypia on breast cancer H&E stained images","MITOS & ATYPIA 14 contest, hosted by conference ICPR 2014 - detection of mitosis and evaluation of nuclear atypia on breast cancer H&E stained images","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/20/logo_mitos_atypia.png","https://mitos-atypia-14.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:19:06" -"289","lola11","LOLA11","Segmentation of lungs and lobes in chest CT scans","The goal of LOLA11 (LObe and Lung Analysis 2011) is to compare methods for (semi-)automatic segmentation of the lungs and lobes from chest computed tomography scans. Any team, whether from academia or industry, can join.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/39/lola11_web_GVIrfhf.png","https://lola11.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:19:28" -"290","promise12","PROMISE12","Segmentation algorithms for MRI of the prostate","The goal of this challenge is to compare interactive and (semi)-automatic segmentation algorithms for MRI of the prostate.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/40/promise12.png","https://promise12.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.media.2013.12.002","\N","\N","2023-11-08 00:42:00","2023-11-14 19:48:34" -"291","camelyon16","CAMELYON16","Evaluating algorithms for automated cancer metastasis detection","The goal of this challenge is to evaluate new and existing algorithms for automated detection of cancer metastasis in digitized lymph node tissue sections. Two large datasets from both the radboud university medical center and the university medical center utrecht are provided.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/65/logo.png","https://camelyon16.grand-challenge.org/","completed","5","https://doi.org/10.1001/jama.2017.14585","2015-11-25","2016-04-01","2023-11-08 00:42:00","2023-11-11 01:44:54" -"292","isbi-aida","ISBI-AIDA","The isbi challenge focuses on evaluating endoscopic image analysis methods","The aim of this challenge is to bring together the community of researchers working on the various types of optical endoscopy at its multiple scales and different needs, to provide reference databases and reference results both for the imaging community and those interested in the translation to the clinical practice.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/67/logo.png","https://isbi-aida.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:50:12" -"293","luna16","LUNA16","Nodule detection algorithms for chest CT in a large-scale setting","The LUNA16 challenge will focus on a large-scale evaluation of automatic nodule detection algorithms for chest CT.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/71/luna16_logo.png","https://luna16.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.media.2017.06.015","\N","\N","2023-11-08 00:42:00","2023-11-17 23:19:48" -"294","camelyon17","CAMELYON17","Automated detection and classification of breast cancer metastases","Automated detection and classification of breast cancer metastases in whole-slide images of histological lymph node sections. This task has high clinical relevance and would normally require extensive microscopic assessment by pathologists.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/80/camelyon17_logo.png","https://camelyon17.grand-challenge.org/","active","5","https://doi.org/10.1109/tmi.2018.2867350","2016-11-16","\N","2023-11-08 00:42:00","2023-11-11 01:45:01" -"295","retouch","RETOUCH","Detecting retinal fluid in optical coherence tomography images","Retinal OCT fluid challenge (RETOUCH) compares automated algorithms that are able to detect and segment different types of retinal fluid in optical coherence tomography (OCT).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/111/retouch-logo.png","https://retouch.grand-challenge.org/","completed","5","https://doi.org/10.1109/tmi.2019.2901398","2017-04-03","\N","2023-11-08 00:42:00","2023-11-17 23:20:14" -"296","cataracts","CATARACTS","Image-based tool detection algorithms for cataract surgery","The challenge on automatic tool annotation for cataract surgery aims at evaluating image-based tool detection algorithms in the context of the most common surgical procedure in the world.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/130/logo.png","https://cataracts.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.media.2018.11.008","\N","\N","2023-11-08 00:42:00","2023-11-14 19:48:56" -"297","tadpole","TADPOLE","Assesses Alzheimer's disease prediction of longitudinal evolution","The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge is brought to you by the europond consortium in collaboration with the Alzheimer's Disease Neuroimaging Initiative (ADNI).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/141/logo_gC1i5c5.png","https://tadpole.grand-challenge.org/","completed","5","https://arxiv.org/abs/2002.03419","\N","\N","2023-11-08 00:42:00","2023-11-17 23:21:11" -"298","coronare","CoronARe","Methods in coronary artery reconstruction using C-arm angiography","Coronare ranks state-of-the-art methods in symbolic and tomographic coronary artery reconstruction from interventional c-arm rotational angiography. Specifically, we will benchmark the performance of the methods using accurately pre-processed data, and study the effects of imperfect pre-processing conditions (segmentation and background subtraction errors). The evaluation will be performed in a controlled environment using digital phantom images.","","https://coronare.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:49:15" -"299","iciar2018-challenge","ICIAR 2018","Automatic detection of cancerous regions in breast cancer histology images","Can you develop a method for automatic detection of cancerous regions in breast cancer histology images?","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/176/logo_small.png","https://iciar2018-challenge.grand-challenge.org/","active","5","https://doi.org/10.1016/j.media.2019.05.010","2023-01-09","\N","2023-11-08 00:42:00","2023-11-14 19:49:22" -"300","sliver07","SLIVER07","Liver segmentation in clinical 3D CT scans in this competition","The goal of this competition is to compare different algorithms to segment the liver from clinical 3d computed tomography (CT) scans.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/178/splash2.jpg","https://sliver07.grand-challenge.org/","completed","5","https://doi.org/10.1109/tmi.2009.2013851","\N","\N","2023-11-08 00:42:00","2023-11-17 23:21:37" -"301","rocc","ROCC","DR disease in retina OCT volumes","Retinal OCT Classification Challenge (ROCC) is organized as a one day challenge in conjunction with MVIP2017. The goal of this challenge is to call different automated algorithms that are able to detect DR disease from normal retina on a common dataset of OCT volumes, acquired with topcon SD-OCT devices.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/180/logo.jpg","https://rocc.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:22:17" -"302","idrid","IDRiD","Retinal lesion segmentation, optic disc/fovea detection, and DR grading","This challenge evaluates automated techniques for analysis of fundus photographs. We target segmentation of retinal lesions like exudates, microaneurysms, and hemorrhages and detection of the optic disc and fovea. Also, we seek grading of fundus images according to the severity level of DR and DME.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/183/g2385.png","https://idrid.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.media.2019.101561","\N","\N","2023-11-08 00:42:00","2023-11-17 23:22:36" -"303","empire10","EMPIRE10","Chest CT images; assess the accuracy of algorithms","The EMPIRE10 challenge was launched in early 2010 with an initial set of 20 scan pairs to be registered by participants in their own facility. This was followed in September by a workshop at the MICCAI 2010 conference where participants registered a further 10 scan pairs live within a 3 hour timeframe. This process and the results obtained are described in detail in Murphy et al., ""Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge."", IEEE Trans Med Imaging. 2011 Nov;30(11):1901-20. Please cite this publication if you wish to reference the EMPIRE10 challenge. From this point forward all participants will be judged based on the full set of 30 scan pairs. New participants and new submissions are always welcome - in this way we hope that the EMPIRE10 website will continue to reflect the state of the art in registration of pulmonary CT images.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/186/logo.png","https://empire10.grand-challenge.org/","completed","5","https://doi.org/10.1109/tmi.2011.2158349","\N","\N","2023-11-08 00:42:00","2023-11-15 22:51:00" -"304","lumic","LUMIC","CT chest images using an anthropomorphic digital phantom","The LUMIC challenge tests the accuracy in registration between pre- and post-contrast CT chest images for algorithms, using an anthropomophic digital phantom.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/203/lumiclogo.png","https://lumic.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:22:52" -"305","continuousregistration","Continuous Registration","Submit your lung and brain registration method","Submit your method for lung and brain registration on https://github.com/superelastix/superelastix! Your method is easily accessible to end-users and automatically compiled, tested, and benchmarked weekly on several different data sets.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/207/logo.png","https://continuousregistration.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:50:44" -"306","drive","DRIVE","Develop a system to automatically segment vessels in human retina fundus images","The DRIVE database has been established to enable comparative studies on segmentation of blood vessels in retinal images. Retinal vessel segmentation and delineation of morphological attributes of retinal blood vessels, such as length, width, tortuosity, branching patterns and angles are utilized for the diagnosis, screening, treatment, and evaluation of various cardiovascular and ophthalmologic diseases such as diabetes, hypertension, arteriosclerosis and chorodial neovascularization. Automatic detection and analysis of the vasculature can assist in the implementation of screening programs for diabetic retinopathy, can aid research on the relationship between vessel tortuosity and hypertensive retinopathy, vessel diameter measurement in relation with diagnosis of hypertension, and computer-assisted laser surgery. Automatic generation of retinal maps and extraction of branch points have been used for temporal or multimodal image registration and retinal image mosaic synthesis. Mor...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/210/logo_drive.PNG","https://drive.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:51:30" -"307","curious2018","CuRIOUS 2018","MICCAI Challenge 2018 for brainshift correction with intra-operative ultrasound","Early brain tumor resection can effectively improve the patient’s survival rate. However, resection quality and safety can often be heavily affected by intra-operative brain tissue shift due to factors, such as gravity, drug administration, intracranial pressure change, and tissue removal. Such tissue shift can displace the surgical target and vital structures (e.g., blood vessels) shown in pre-operative images while these displacements may not be directly visible in the surgeon’s field of view. Intra-operative ultrasound (iUS) is a robust and relatively inexpensive technique to track intra-operative tissue shift and surgical tools, but to help update pre-surgical plans with this information, accurate and robust image registration algorithms are needed to relate pre-surgical MRI to iUS images. Despite the great progress so far, medical image registration techniques still have not made into the surgical room to directly benefit the patients with brain tumors. This challege/worksh...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/221/curious2018_logo.png","https://curious2018.grand-challenge.org/","completed","5","https://doi.org/10.1109/tmi.2019.2935060","\N","\N","2023-11-08 00:42:00","2023-11-15 22:51:50" -"308","refuge","REFUGE","Algorithms for glaucoma detection and optic disc/cup segmentation","The goal of the REtinal FUndus Glaucoma Challenge (REFUGE) is to evaluate and compare automated algorithms for glaucoma detection and optic disc/cup segmentation on a common dataset of retinal fundus images.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/229/logo_refuge_200x200.png","https://refuge.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:23:34" -"309","monuseg","MoNuSeg","Segmenting nuclei from H&E stained histopathological images","This challenge will showcase the best nuclei segmentation techniques that will work on a diverse set of H&E stained histology images obtained from different hospitals spanning multiple patients and organs. This will enable the training and testing of readily usable (or generalized) nuclear segmentation softwares.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/238/monuseg_logo.png","https://monuseg.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:52:09" -"310","paves","PAVES","Mra images for lower limb arterial occlusive disease planning","Peripheral artery:vein enhanced segmentation (PAVES) is the challenge focussed on providing easily interpretable and relevant images that can be readily understood by clinicians (vascular interventional radiologists & vascular surgeons) from mra datasets where the venous and arterial vasculature may be equally enhanced. The setting is lower limb arterial occlusive disease where imaging of the below knee arterial vasculature is critical in planning limb salvage interventions. However, the competing demands of the high spatial resolution needed to image small vessels versus imaging time constraints where there is often a very short arteriovenous transit time for contrast passage form arterial to venous compartments makes imaging challenging. While dynamic mra techniques can usually allow arterial imaging without venous ‚äòcontamination‚äô these necessarily sacrifice spatial resolution.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/243/paveslogo.png","https://paves.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:23:47" -"311","prostatex","PROSTATEx","Clinical significance of prostate lesions using MRI data","This challenge is the live continuation of the offline PROSTATEx Challenge (""SPIE-AAPM-NCI Prostate MR Classification Challenge”) that was held in conjunction with the 2017 SPIE Medical Imaging Symposium. In this challenge, the task is to predict the clinical significance of prostate lesions found in MRI data.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/258/prostatex-logo.jpg","https://prostatex.grand-challenge.org/","completed","5","","\N","2022-04-30","2023-11-08 00:42:00","2023-11-15 22:52:25" -"312","hc18","HC18","Measuring fetal head circumference using 2D ultrasound images","Automated measurement of fetal head circumference using 2D ultrasound images","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/265/HC18_LogoV1.png","https://hc18.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:23:58" -"313","anhir","ANHIR","Aligning multi-stained histology tissue samples","The challenge focuses on comparing the accuracy (using manually annotated landmarks) and the approximate speed of automatic non-linear registration methods for aligning microscopy images of multi-stained histology tissue samples.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/285/logo_sq_OdYGo3e.png","https://anhir.grand-challenge.org/","active","5","https://doi.org/10.1109/tmi.2020.2986331","\N","\N","2023-11-08 00:42:00","2023-11-14 19:51:43" -"314","breastpathq","BreastPathQ: Cancer Cellularity Challenge 2019","Develop a method for analyzing histology patches","SPIE-AAPM-NCI BreastPathQ:Cancer Circularity Challenge 2019: Participants will be tasked to develop an automated method for analyzing histology patches extracted from whole slide images and assign a score reflecting cancer cellularity for tumor burden assessment in each.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/296/spie_with_overlays.png","https://breastpathq.grand-challenge.org/","active","5","https://doi.org/10.1117/1.jmi.8.3.034501","\N","\N","2023-11-08 00:42:00","2023-11-17 23:27:13" -"315","chaos","CHAOS","Segment liver in CT data and liver, spleen, and kidneys in MRI data","In this challenge, you segment the liver in CT data, and segment liver, spleen, and kidneys in MRI data.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/298/logo_8sv4fA4_SWcTFEs.png","https://chaos.grand-challenge.org/","active","5","https://doi.org/10.1016/j.media.2020.101950","\N","\N","2023-11-08 00:42:00","2023-11-17 21:31:53" -"316","ead2019","EAD2019","Address multi-class artefact detection, region segmentation, and detection","Endoscopic artefact detection (EAD) is a core problem and needed for realising robust computer-assisted tools. The EAD challenge has 3 tasks: 1) multi-class artefact detection, 2) region segmentation, 3) detection generalisation.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/302/1772_A_M62_00022_1.jpg","https://ead2019.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:25:06" -"317","acdc-lunghp","ACDC-LungHP","Methods for whole-slide lung histopathology images","Automatic cancer detection and classification in whole-slide lung histopathology","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/305/logo.png","https://acdc-lunghp.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:52:50" -"318","palm","PALM","Pathological Myopia diagnosis and fundus lesion segmentation in patients","The pathologic myopia challenge (PALM) focuses on the investigation and development of algorithms associated with the diagnosis of pathological myopia (PM) and segmentation of lesions in fundus photos from PM patients.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/307/palm-logo.jpg","https://palm.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:25:23" -"319","ichallenges","iChallenges","Eye image modalities, including REFUGE, PALM, RETOUCH, among others","We organized a serial of challenges on different eye image modalities, such as REFUGE, PALM, RETOUCH, etc.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/345/ichallenge.png","https://ichallenges.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:25:37" -"320","decathlon-10","Decathlon","Test machine learning algorithm generalizability across 10 different tasks","The medical segmentation decathlon challenge tests the generalisability of machine learning algorithms when applied to 10 different semantic segmentation task.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/350/background_dark_logo.png","https://decathlon-10.grand-challenge.org/","completed","5","https://doi.org/10.1038/s41467-022-30695-9","2022-07-21","2023-08-20","2023-11-08 00:42:00","2023-11-14 19:53:19" -"321","lyon19","LYON19","Develop methods for automatic lymphocyte detection in IHC stained specimens","Automatic Lymphocyte detection in IHC stained specimens.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/355/ban2_kpuoTJg.png","https://lyon19.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:27:28" -"322","kits19","KiTS19","Participate in the segmentation challenge for kidneys and kidney tumors in 2019","2019 Kidney and Kidney Tumor Segmentation Challenge","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/360/Screenshot_from_2019-01-02_17-23-36.png","https://kits19.grand-challenge.org/","active","5","https://arxiv.org/abs/1912.01054","\N","\N","2023-11-08 00:42:00","2023-11-17 23:27:36" -"323","paip2019","PAIP 2019","Address liver cancer segmentation and viable tumor burden estimation","PAIP2019: Liver Cancer Segmentation Task 1: Liver Cancer Segmentation Task 2: Viable Tumor Burden Estimation","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/370/Untitled_design.png","https://paip2019.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:27:53" -"324","orcascore","orCaScore","Coronary artery calcium scoring in cardiac CT scans","The purpose of the orCaScore challenge is to compare methods for automatic and semi-automatic coronary artery calcium scoring in cardiac CT scans. This evaluation framework was launched at the MICCAI 2014 workshops in Boston, USA, where we organized the Challenge on Automatic Coronary Calcium Scoring.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/378/00b670348bfdf4464e00c44310ec259f.jpg","https://orcascore.grand-challenge.org/","active","5","https://doi.org/10.1118/1.4945696","\N","\N","2023-11-08 00:42:00","2023-11-17 23:28:02" -"325","curious2019","curious2019","MRI to intra-operative ultrasound (iUS) before and after tumor resection","MICCAI Challenge 2019 for Correction of Brainshift with Intra-Operative Ultrasound. Taks 1: Register pre-operative MRI to iUS before tumor resection;Taks 2: Register iUS after tumor resection to iUS before tumor resection","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/380/CuRIOUS.png","https://curious2019.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:28:13" -"326","patchcamelyon","PatchCamelyon","Detect breast cancer metastasis in lymph nodes","PatchCamelyon is a new and challenging image classification dataset of 327.680 color images (96 x 96px) extracted from histopathology images of the CAMELYON16 challenge. The goal is to detect breast cancer metastasis in lymph nodes.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/381/Screen_Shot_2019-05-05_at_21.43.25.png","https://patchcamelyon.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:28:22" -"327","endovissub2019-scared","Stereo Correspondence and Reconstruction of Endoscopic Data","Address stereo correspondence and reconstruction challenges in endoscopic data","Stereo correspondence and reconstruction of endoscopic data","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/385/c7714704.jpg","https://endovissub2019-scared.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:58:30" -"328","verse2019","VerSe`19","Vertebrae labelling and segmentation on 150 CT scans","Vertebrae labelling and segmentation on a spine dataset on an unprecedented 150 CT scans with voxel-level vertebral annotations.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/388/logo_border.png","https://verse2019.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 21:30:59" -"329","gleason2019","Gleason2019","Methods for prostate cancer from H&E-stained histopathology images","MICCAI 2019 automatic prostate gleason grading challenge: this challenge aims at the automatic gleason grading of prostate cancer from h&e-stained histopathology images. This task is of critical importance because gleason score is a strong prognostic predictor. On the other hand, it is very challenging because of the large degree of heterogeneity in the cellular and glandular patterns associated with each gleason grade, leading to significant inter-observer variability, even among expert pathologists.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/391/GLEASON2019.png","https://gleason2019.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-14 20:00:08" -"330","age","AGE","Assess automatic methods for angle closure glaucoma evaluation","Angle closure glaucoma evaluation challenge","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/394/icon.png","https://age.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-11 01:48:53" -"331","amd","iChallenge-AMD","Tackle challenges in age-related macular degeneration diagnosis and analysis","Age-related macular degeneration challenge","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/395/%E6%BC%94%E7%A4%BA%E6%96%87%E7%A8%BF1-2.png","https://amd.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:58:34" -"332","structseg2019","StructSeg2019","Automated structure segmentation for radiotherapy planning","Welcome to automatic structure segmentation for radiotherapy planning challenge 2019. This competition is part of the MICCAI 2019 challenge.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/398/logo_aAzg3xS.jpg","https://structseg2019.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:54:50" -"333","digestpath2019","DigestPath2019","Algorithms for signet ring cell detection and colonoscopy tissue screening","The challenge aims to evaluate algorithms for signet ring cell detection and colonoscopy tissue screening in digestive system pathological images. It introduces the first public dataset for these tasks, providing expert-level annotations to advance research on automatic pathological object detection and lesion segmentation.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/399/digestpath-logo2.png","https://digestpath2019.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 18:40:29" -"334","odir2019","ODIR-2019","Compete in recognizing ocular diseases using morphological features","Peking university international competition on ocular disease intelligent recognition","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/402/logo.jpg","https://odir2019.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-14 20:27:41" -"335","lysto","Lymphocyte Assessment Hackathon","Workshop for lymphocyte assessment in computational pathology","Lymphocyte assessment hackathon in conjunction with the MICCAI compay 2019 workshop on computational pathology","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/421/lysto_square.png","https://lysto.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:55:01" -"336","aasce19","AASCE","Develop accurate automated methods for estimating spinal curvature","Accurate automated spinal curvature estimation","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/424/logo.png","https://aasce19.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:58:39" -"337","ecdp2020","HEROHE","Identify HER2-positive from HER2-negative breast cancer specimens","Unlike previous challenges, this proposes to find an image analysis algorithm to identify her2-positive from her2-negative breast cancer specimens evaluating only the morphological features present on the he slide, without the staining patterns of ihc.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/433/ECDP2020_square.jpg","https://ecdp2020.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:55:07" -"338","monusac-2020","MoNuSAC 2020","Address segmentation and classification of nuclei in multi-organ images","Multi-organ nuclei segmentation and classification challenge","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/445/logo.PNG","https://monusac-2020.grand-challenge.org/","completed","5","https://doi.org/10.13140/rg.2.2.12290.02244/1","\N","\N","2023-11-08 00:42:00","2023-11-08 00:58:42" -"339","endocv","EndoCV2020","Focus on artefact detection and disease detection in endoscopic images","Endoscopy computer vision challenge (endocv2020) introduces two core sub-themes in endoscopy: 1) artefact detection and segmentation (ead2020) and 2) disease detection and segmentation (edd2020).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/462/endoLogo.png","https://endocv.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:55:17" -"340","lndb","LNDb Challenge","Determine nodule detection and characterization for lung cancer screening","Lung cancer screening and fleischner follow-up determination in chest CT through nodule detection, segmentation and characterization","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/470/thumbnail_lndb.png","https://lndb.grand-challenge.org/","active","5","https://arxiv.org/abs/1911.08434","\N","\N","2023-11-08 00:42:00","2023-11-17 21:31:00" -"341","verse2020","VerSe'20","Label and segment vertebrae on a diverse CT dataset","Vertebrae labelling and segmentation on a multi-centre, multi-scanner, and anatomically-diverse CT dataset.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/473/logo_border.png","https://verse2020.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 21:31:02" -"342","paip2020","PAIP2020","Classify molecular subtypes in colorectal cancers, predict MSI","Built on the success of its predecessor, paip2020 is the second challenge organized by the pathology AI platform (paip) and the seoul national university hospital (snuh). Paip2020 will proceed to not only detect whole tumor areas in colorectal cancers but also to classify their molecular subtypes, which will lead to characterization of their heterogeneity with respect to prognoses and therapeutic responses. All participants should predict one of the molecular carcinogenesis pathways, i.e., microsatellite instability(msi) in colorectal cancer, by performing digital image analysis without clinical tests. This task has a high clinical relevance as the currently used procedure requires an extensive microscopic assessment by pathologists. Therefore, those automated algorithms would reduce the workload of pathologists as a diagnostic assistance.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/480/paip2020_thumb_640x640.jpg","https://paip2020.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 21:33:31" -"343","ribfrac","RibFrac","Benchmark rib fracture detection and classification on 660 CT scans","Rib fracture detection and classification challenge: a large-scale benchmark of 660 CT scans with ~5,000 rib fractures (around 80gb)","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/482/challenge-logo-white_b8a8xbr.jpg","https://ribfrac.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 21:31:05" -"344","tn-scui2020","Thyroid Nodule Segmentation and Classification","Develop algorithms for thyroid nodules using a diverse ultrasound dataset","The main topic of this tn-scui2020 challenge is finding automatic algorithms to accurately classify the thyroid nodules in ultrasound images. It will provide the biggest public dataset of thyroid nodule with over 4500 patient cases from different ages, genders, and were collected using different ultrasound machines. Each ultrasound image is provided with its ground truth class (benign or maglinant) and a detailed delineation of the nodule. This challenge will provide a unique opportunity for participants from different backgrounds (e.g. academia, industry, and government, etc.) To compare their algorithms in an impartial way.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/484/Capture8888.PNG","https://tn-scui2020.grand-challenge.org/","active","5","","2022-01-20","\N","2023-11-08 00:42:00","2023-11-14 19:56:07" -"345","learn2reg","Learn2Reg","Address challenges in learning from small datasets","Challenge on medical image registration addressing: learning from small datasets; estimating large deformations; dealing with multi-modal scans; and learning from noisy annotations","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/486/logo_warped.png","https://learn2reg.grand-challenge.org/","active","5","https://doi.org/10.1109/tmi.2022.3213983","\N","\N","2023-11-08 00:42:00","2023-11-14 19:56:18" -"346","asoca","Automated Segmentation Of Coronary Arteries","Develop automated methods for segmentation of coronary arteries","Automated segmentation of coronary arteries","","https://asoca.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-08 17:59:56" -"347","knoap2020","KNOAP2020","Compare methods MRI, X-ray, and clinical risk factors for knee osteoarthritis","Knee osteoarthritis causes a large economic burden on the society and reduces life quality of an individual. Therefore, methods that are able to identify subjects who will develop the disease in the future are important. Usually the methods are optimized for specific datasets and it is unclear how the different methods would perform on previously unseen data. Therefore, we are organizing a challenge to objectively compare methods that use mri, x-ray image data, and clinical risk factors for prediction of incident symptomatic radiographic knee osteoarthritis.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/492/KNOAP_logo_640x640.png","https://knoap2020.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:58:04" -"348","humanactivitiyclassificationwithradar","Human Activity Classification with Radar","Benchmark classification algorithms on a publicly available radar dataset","The radar challenge is a new event hosted at 2020 iet international radar conference that enables participants to test their classification algorithms on a common, publicly available database of radar data in order to benchmark performances. Dataset download link: http://researchdata.gla.ac.uk/848/","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/493/logo.png","https://humanactivitiyclassificationwithradar.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2024-01-31 22:45:45" -"349","saras-esad","SARAS-ESAD","AI-based surgical scene for robotic assistants in minimally invasive surgery","This challenge is part of medical imaging with deep learning conference, 2020. The conference is held between 6 ‚äë 8 july 2020 in montr√©al. The saras (smart autonomous robotic assistant surgeon) eu consortium, www.saras-project.eu, is working towards replacing the assistant surgeon in mis with two assistive robotic arms. To accomplish that, an artificial intelligence based system is required which not only can understand the complete surgical scene but also detect the actions being performed by the main surgeon. This information can later be used infer the response required from the autonomous assistant surgeon.","","https://saras-esad.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-28 00:29:54" -"350","surgvisdom","SurgVisDom","VR simulations to overcome data privacy concerns in context-aware models","Exploring visual domain adaptation using vr simulations to overcome data privacy concerns in context-aware models.","","https://surgvisdom.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-22 19:40:08" -"351","cada","CADA","Cerebral aneurysm image analysis challenge","Cerebral aneurysms are local dilations of arterial blood vessels caused by a weakness of the vessel wall. Subarachnoid hemorrhage (sah) caused by the rupture of a cerebral aneurysm is a life-threatening condition associated with high mortality and morbidity. The mortality rate is above 40%, and even in case of survival cognitive impairment can affect patients for a long time. Major goals in image analysis are the detection and risk assessment of aneurysms. We, therefore, subdivided the challenge into three categories. The first task is finding the aneurysm; the second task is the accurate segmentation to allow for a longitudinal assessment of the development of suspicious aneurysms. The third task is the estimation of the rupture risk of the aneurysm.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/531/data-first-row-2.png","https://cada.grand-challenge.org/","completed","5","https://doi.org/10.1007/978-3-030-72862-5","\N","\N","2023-11-08 00:42:00","2023-11-11 01:54:15" -"352","dfu2020","Diabetic Foot Ulcer Challenge 2020","Diabetic foot ulcer challenge 2020","Diabetic foot ulcer challenge 2020","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/532/bb.png","https://dfu2020.grand-challenge.org/","active","5","https://doi.org/10.1016/j.compbiomed.2021.104596","\N","\N","2023-11-08 00:42:00","2023-11-08 22:47:03" -"353","covid-ct","CT diagnosis of COVID-19","COVID-19 CT Image Diagnosis Competition","Coronavirus disease 2019 (COVID-19) has infected more than 1.3 million individuals all over the world and caused more than 106,000 deaths. One major hurdle in controlling the spreading of this disease is the inefficiency and shortage of medical tests. To mitigate the inefficiency and shortage of existing tests for COVID-19, we propose this competition to encourage the development of effective deep learning techniques to diagnose COVID-19 based on CT images. The problem we want to solve is to classify each CT image into positive COVID-19 (the image has clinical findings of COVID-19) or negative COVID-19 ( the image does not have clinical findings of COVID-19). It‚äôs a binary classification problem based on CT images.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/537/covid-CT2.png","https://covid-ct.grand-challenge.org/","completed","5","","\N","\N","2023-11-14 20:25:11","2023-11-17 21:32:02" -"354","autoimplant","AutoImplant","MICCAI 2020 Cranial Implant Design","The MICCAI 2020 cranial implant design challenge","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/540/logos.PNG","https://autoimplant.grand-challenge.org/","completed","5","https://arxiv.org/abs/2006.12449","\N","\N","2023-11-14 20:25:44","2023-11-08 00:59:03" -"355","cada-rre","CADA - Rupture Risk Estimation","Cerebral aneurysm challenge: detect, segment, and assess rupture risk","Cerebral aneurysms are local dilations of arterial blood vessels caused by a weakness of the vessel wall. Subarachnoid hemorrhage (sah) caused by the rupture of a cerebral aneurysm is a life-threatening condition associated with high mortality and morbidity. The mortality rate is above 40%, and even in case of survival cognitive impairment can affect patients for a long time. Major goals in image analysis are the detection and risk assessment of aneurysms. We, therefore, subdivided the challenge into three categories. The first task is finding the aneurysm; the second task is the accurate segmentation to allow for a longitudinal assessment of the development of suspicious aneurysms. The third task is the estimation of the rupture risk of the aneurysm.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/541/data-first-row-2.png","https://cada-rre.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-11 01:59:43" -"356","cada-as","CADA - Aneurysm Segmentation","Cerebral aneurysm image analysis: detect, segment, assess risk","Cerebral aneurysms are local dilations of arterial blood vessels caused by a weakness of the vessel wall. Subarachnoid hemorrhage (sah) caused by the rupture of a cerebral aneurysm is a life-threatening condition associated with high mortality and morbidity. The mortality rate is above 40%, and even in case of survival cognitive impairment can affect patients for a long time. Major goals in image analysis are the detection and risk assessment of aneurysms. We, therefore, subdivided the challenge into three categories. The first task is finding the aneurysm; the second task is the accurate segmentation to allow for a longitudinal assessment of the development of suspicious aneurysms. The third task is the estimation of the rupture risk of the aneurysm.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/543/data-first-row-2.png","https://cada-as.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:59:10" -"357","panda","The PANDA challenge","PANDA Challenge: prostate cancer grading","The panda challenge: prostate cancer grade assessment using the gleason grading system","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/544/panda_logo_notext.png","https://panda.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:59:10" -"358","pathvqachallenge","Pathology Visual Question Answering","Pathology visual question answering","Pathology visual question answering","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/548/grand_challenge3.jpg","https://pathvqachallenge.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-11 01:50:06" -"359","qubiq","QUBIQ","Biomedical image segmentation uncertainties","Quantification of uncertainties in biomedical image segmentation challenge","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/552/brain.png","https://qubiq.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:59:15" -"360","lodopab","LoDoPaB-CT","Low-dose CT reconstruction challenge","Low-dose CT reconstruction in the setting of the lodopab-ct dataset.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/555/logo_white_bg.png","https://lodopab.grand-challenge.org/","active","5","https://doi.org/10.1038/s41597-021-00893-z","\N","\N","2023-11-08 00:42:00","2023-11-17 21:31:13" -"361","apples-ct","Apples-CT","Ct reconstruction for apple defect detection","High-throughput CT image reconstruction and defect detection for apples","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/557/logo.png","https://apples-ct.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:59:18" -"362","riadd","RIADD (ISBI-2021)","Retinal image analysis for disease detection","Retinal image analysis for multi-disease detection","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/562/Logo_ISBI_640_OO4Fuj9.png","https://riadd.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:59:20" -"363","mitoem","MitoEM","3D mitochondria segmentation benchmark","Large-scale 3d mitochondria instance segmentation benchmark","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/566/logo2.png","https://mitoem.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:59:22" -"364","a-afma","A-AFMA","Automated prenatal ultrasound measurement","Prenatal ultrasound (us) measurement of amniotic fluid is an important part of fetal surveillance as it provides a non-invasive way of assessing if there is oligohydramnios (insufficient amniotic fluid) and polyhydramnios (excess amniotic fluid), which are associated with numerous problems both during pregnancy and after birth. In this image analysis challenge, we aim to attract attention from the image analysis community to work on the problem of automated measurement of the mvp using the predefined ultrasound video clip based on a linear-sweep protocol [1]. We define two tasks. The first task is to automatically detect amniotic fluid and the maternal bladder. The second task is to identify the appropriate points for mvp measurement given the selected frame of the video clip, and calculate the length of the connected line between these points. The data was collected from women in the second trimester of pregnancy, as part of the pure study at the john radcliffe hospital in oxford, uk.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/567/Figure_3_MVP_example.png","https://a-afma.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-11 07:00:24" -"365","covid-segmentation","COVID-19 LUNG CT LESION SEGMENTATION CHALLENGE - 2020","SARS-CoV-2 lung lesion segmentation","This challenge will create the platform to evaluate emerging methods for the segmentation and quantification of lung lesions caused by SARS-CoV-2 infection from CT images.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/569/Challenge_Image.png","https://covid-segmentation.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:09:24" -"366","valdo","Where is VALDO?","Vascular lesion detection challenge 2021","This challenge aims at promoting the development of new solutions for the automated segmentation of such very sparse and small objects while leveraging weak and noisy labels. The central objective of this challenge is to facilitate quantification of CSVD in brain MRI scans.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/570/LogoVALDO.png","https://valdo.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:45:10" -"367","segpc-2021","SegPC-2021","Plasma cell cancer segmentation challenge","This challenge is positioned towards robust segmentation of cells which is the first stage to build such a tool for plasma cell cancer, namely, multiple myeloma (mm), which is a type of blood cancer.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/574/logo_fRPkhwS.png","https://segpc-2021.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:59:29" -"368","endocv2021","EndoCV2021","Endoscopy Computer Vision Challenge 2021","Computer-aided detection, localization, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking and development of computer vision methods remains an open problem. This is mostly due to the lack of datasets or challenges that incorporate highly heterogeneous dataset appealing participants to test for generalisation abilities of the methods. We aim to build a comprehensive, well-curated, and defined dataset from 6 different centres worldwide and provide 5 datasets types that include: i) multi-centre train-test split from 5 centres ii) polyp size-based split (participants should do this by themselves if of interest), iii) data centre wise split, iv) modality split (only test) and v) one hidden centre test. Participants will be evaluated on all types to address strength and weaknesses of each participants’ method. Both detection bounding boxes and pixel-wise segme...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/575/endoLogo-2021_AdZmuvg.jpg","https://endocv2021.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:38:42" -"369","fusc","Foot Ulcer Segmentation Challenge","Foot ulcer segmentation challenge","The goal of this challenge is to segment the wound area from natural images photographed during clinical visits. In the dataset provided, over 1000 images are collected over 2 years from hundreds of patients. All images are completely de-identified by removing personal identifiers defined by hipaa.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/579/Logo.png","https://fusc.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:38:28" -"370","nucmm","NucMM","Millimeter-scale nucleus 3D segmentation","Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and clinical analysis, enabling the study of cellular expression patterns and cell lineages. We pushed the task forward to the sub-cubic millimeter scale and curated the NucMM dataset with two fully annotated volumes: one 0.1 mm^3 electron microscopy (EM) volume containing nearly the entire zebrafish brain with around 170,000 nuclei; and one 0.25 mm^3 micro-CT (uCT) volume containing part of a mouse visual cortex with about 7,000 nuclei.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/580/NucMM_logo.png","https://nucmm.grand-challenge.org/","completed","5","https://doi.org/10.1007/978-3-030-87193-2_16","\N","\N","2023-11-08 00:42:00","2023-11-15 22:45:22" -"371","vessel-wall-segmentation","Carotid Artery Vessel Wall Segmentation Challenge","Carotid artery vessel wall segmentation","In this challenge, the task is to segment the vessel wall from 3D-MERGE image with high accuracy and robustness. While the challenges of segmentation in different body regions are different, all vessel wall segmentation requires the basic steps of identifying the artery (localization) and lumen and outer wall segmentation. Then the wall thickness (difference between the lumen and outer wall contours) can be measured. Other clinically usable measurements such as lumen area or percent stenosis can also be derived from the vessel wall segmentation. Therefore, this challenge focuses on the important first step of vessel wall segmentation.","","https://vessel-wall-segmentation.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:38:02" -"372","crossmoda","Cross-Modality Domain Adaptation Image Segmentation - 2021","Cross-modality domain adaptation 2021","This challenge proposes the first medical imaging benchmark of unsupervised cross-modality Domain Adaptation approaches (from contrast-enhanced T1 to high-resolution T2).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/592/crossmoda_logo_black.png","https://crossmoda.grand-challenge.org/","active","5","https://doi.org/10.1016/j.media.2022.102628","\N","\N","2023-11-08 00:42:00","2023-11-15 22:37:49" -"373","brainptm-2021","BrainPTM 2021","Brain pre-surgical tractography mapping","In this challenge we ask the participants to perform direct white matter tracts mapping in clinical brain MRI scans we provide. The data that is provided consists of 75 cases (patients referred for brain tumor removal) that were acquired at Sheba Medical Center at Tel HaShomer, Israel [2]. Patient pathologies include oligodendrogliomas , astrocytomas, glioblastomas and cavernomas, on first occurrence or in a post-surgical recurrence. According to the neuro-radiologist's estimation, the tumor volumes ranged from 4 (cavernoma) to 60 [cm^3] (glioblastoma multiforme). Also, different levels of edema are present around the dataset tumors, from inexistent to very significant. Along with each case both T1 Structural and Diffusion Weighted modalities are provided. For 60 cases (training) semi-manual white matters tracts mapping is provided in the form of binary segmentation maps. For the rest 15 cases (test) no tracts annotations are provided as these will be used for participants algori...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/595/logo_square.jpg","https://brainptm-2021.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-16 17:40:36" -"374","cholectriplet2021","CholecTriplet 2021","EndoVis sub-challenge for surgical action","This sub-challenge focuses on exploiting machine learning methods for the online automatic recognition of surgical actions as a series of triplets. Participants will develop and compete with algorithms to recognize action triplets directly from the provided surgical videos. This novel challenge investigates the state-of-the-art on surgical fine-grained activity recognition and will establish a new promising research direction in computer-assisted surgery.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/596/logo-challenge.png","https://cholectriplet2021.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:37:21" -"375","paip2021","PAIP2021","PAIP 2021 challenge: perineural invasion","PAIP 2021 challenge aims to promote the development of a common algorithm for automatic detection of perineural invasion in resected specimens of multi-organ cancers. PAIP 2021 challenge will have a technical impact in the following fields: detection of composite targets (nerve and tumor) and common modeling for target images in multiple backgrounds. This challenge will provide a good opportunity to overcome the limitations of current disease-organ-specific modeling and develop a technological approach to the universality of histology in multiple organs.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/598/640-640.png","https://paip2021.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:37:00" -"376","flare","FLARE21","Abdominal organ segmentation challenge","Abdominal organ segmentation plays an important role in clinical practice, and to some extent, it seems to be a solved problem because the state-of-the-art methods have achieved inter-observer performance in several benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can be generalized on more diverse datasets. Moreover, many SOTA methods use model ensembles to boost performance, but these solutions usually have a large model size and cost extensive computational resources, which are impractical to be deployed in clinical practice. To address these limitations, we organize the Fast and Low GPU Memory Abdominal Organ Segmentation challenge that has two main features: (1) the dataset is large and diverse, includes 511 cases from 11 medical centers. (2) we not only focus on segmentation accuracy but also segmentation efficiency, whi...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/599/logo_hDqJ8uG.gif","https://flare.grand-challenge.org/","active","5","https://doi.org/10.1016/j.media.2022.102616","\N","\N","2023-11-08 00:42:00","2023-11-15 22:36:39" -"377","nucls","NuCLS","Triple-negative breast cancer nuclei challenge","Classification, Localization and Segmentation of nuclei in scanned FFPE H&E stained slides of triple-negative breast cancer from The Cancer Genome Atlas. See: Amgad et al. 2021. arXiv:2102.09099 [cs.CV].","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/601/TCGA-AR-A0U4-DX1_id-5ea40a88ddda5f8398990ccf_left-42405_top-70784_bo_PgpXdUu.png","https://nucls.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:29:28" -"378","bcsegmentation","Breast Cancer Segmentation","Triple-negative breast cancer segmentation","Semantic segmentation of histologic regions in scanned FFPE H&E stained slides of triple-negative breast cancer from The Cancer Genome Atlas. See: Amgad M, Elfandy H, ..., Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/602/BCSegmentationLogo.png","https://bcsegmentation.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:29:37" -"379","feta","FeTA - Fetal Tissue Annotation Challenge","Fetal tissue annotation challenge","The Fetal Tissue Annotation and Segmentation Challenge (FeTA) is a multi-class, multi-institution image segmentation challenge part of MICCAI 2022. The goal of FeTA is to develop generalizable automatic multi-class segmentation methods for the segmentation of developing human brain tissues that will work with data acquired at different hospitals. The challenge provides manually annotated, super-resolution reconstructed MRI data of human fetal brains which will be used for training and testing automated multi-class image segmentation algorithms. In FeTA 2021, we used the first publicly available dataset of fetal brain MRI to encourage teams to develop automatic brain tissue segmentation algorithms. This year, FeTA 2022 takes it to the next level by launching a multi-center challenge for the development of image segmentation algorithms that will be generalizable to different hospitals with unseen data. We will include data from two institutions in the training dataset, and there wi...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/604/FeTA_logo_640.png","https://feta.grand-challenge.org/","upcoming","5","","2024-03-21","2024-04-26","2023-11-08 00:42:00","2023-12-12 19:00:18" -"380","fastpet-ld","fastPET-LD","PET scan ""hot spots"" detection challenge","In this challenge, we provide 2 training datasets of 68 cases each: the first one was acquired at Sheba medical center (Israel) nuclear medicine department with a very-short exposure of 30s pbp, while the second is the same data followed by a denoising step implemented by a fully convolutional Dnn architecture trained under perceptual loss [1,2]. The purpose of this challenge is the detection of “hot spots”, that is locations that have an elevated standard uptake value (SUV) and potential clinical significance. Corresponding CT scans are also provided. The ground truth, common to both datasets, was generated by Dr. Liran Domachevsky, chair of nuclear medicine at Sheba medical center. It consists of a 3-D segmentation map of the hot spots as well as an Excel file containing the position and size of a 3D cuboid bounding box for each hot spot.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/605/IMG_19052021_144815_600_x_600_pixel.jpg","https://fastpet-ld.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:35:52" -"381","autoimplant2021","AutoImplant 2021","Automatic cranial implant design challenge","Please see our AutoImplant 2020 website for an overview of the cranial implant design topic. Our 2nd AutoImplant Challenge (referred to as AutoImplant 2021) sees the (not limited to) following three major improvements compared to the prior edition, besides a stronger team: Real craniotomy defective skulls will be provided in the evaluation phase. Task specific metrics (e.g., boundary Dice Score) that are optimally in agreement with the clinical criteria of cranial implant design will be implemented and used. Besides a metric-based scoring and ranking system, neurosurgeons will be invited to verify, score and rank the participants-submitted cranial implants based their clinical usability (for the real cases in Task 2).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/607/AutoImplant_2021_Logo.png","https://autoimplant2021.grand-challenge.org/","completed","5","https://doi.org/10.1109/tmi.2021.3077047","\N","\N","2023-11-08 00:42:00","2023-11-16 17:41:01" -"382","dfu-2021","DFUC2021","Diabetic foot ulcer challenge 2021","We have received approval from the UK National Health Service (NHS) Re-search Ethics Committee (REC) to use these images for the purpose of research. The NHS REC reference number is 15/NW/0539. Foot images with DFU were collected from the Lancashire Teaching Hospital over the past few years. Three cameras were used for capturing the foot images, Kodak DX4530, Nikon D3300and Nikon COOLPIX P100. The images were acquired with close-ups of the full foot at a distance of around 30–40 cm with the parallel orientation to the plane of an ulcer. The use of flash as the primary light source was avoided, and instead, adequate room lights were used to get the consistent colours in images. Images were acquired by a podiatrist and a consultant physician with specialization in the diabetic foot, both with more than 5 years professional experience. As a pre-processing stage, we have discarded photographs with out of focus and blurry artefacts. The DFUC2021 consists of 15,683 DFU patche...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/608/footsnap_logo.png","https://dfu-2021.grand-challenge.org/","active","5","https://doi.org/10.1007/978-3-030-94907-5_7","\N","\N","2023-11-08 00:42:00","2023-11-16 17:41:08" -"383","saras-mesad","SARAS-MESAD","MICCAI 2021 multi-domain surgeon action detection","This challenge is organized under MICCAI 2021, the 24th International Conference on Medical Image Computing and Computer Assisted Intervention. The event will be held from September 27th to October 1st 2021 in Strasbourg, France. The challenge focuses on multi-domain surgeon action detection.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/609/Screenshot_3.png","https://saras-mesad.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:30:14" -"384","node21","NODE21","NODE21: nodule generation and detection","Among both men and women, lung cancer causes the greatest number of cancer deaths worldwide. Symptoms of lung cancer typically occur at an advanced stage of the disease, when treatment has a reduced chance of success. Early detection is therefore a key factor in reducing mortality rates from lung cancer. Pulmonary nodules, detected through imaging, are the initial manifestation of lung cancer, visible well before clinical symptoms or signs emerge. They can be visible on a chest radiograph (CXR), and chest radiography is by far the most common radiological exam in the world. Thus, CXR plays a critical role in the accurate identification of nodules in the drive towards early detection of lung cancer. Pulmonary nodules are frequently encountered as incidental findings in patients undergoing routine examination or CXR imaging for issues unrelated to lung cancer.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/612/node21logo.jpg","https://node21.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:34:20" -"385","wsss4luad","WSSS4LUAD","WSSS4LUAD semantic segmentation challenge","The WSSS4LUAD dataset contains over 10,000 patches of lung adenocarcinoma from whole slide images from Guangdong Provincial People's Hospital and TCGA with image-level annotations. The goal of this challenge is to perform semantic segmentation for differentiating three important types of tissues in the WSIs of lung adenocarcinoma, including cancerous epithelial region, cancerous stroma region and normal region. Paticipants have to use image-level annotations to give pixel-level prediction.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/621/%E6%88%AA%E5%B1%8F2021-07-05_%E4%B8%8A%E5%8D%8810.17.09.png","https://wsss4luad.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:30:23" -"386","ski10","SKI10","SKI10 cartilage and bone segmentation challenge","Welcome to the SKI10 website. The goal of SKI10 was to compare different algorithms for cartilage and bone segmentation from knee MRI data. Knee cartilage segmentation is a clinically relevant segmentation problem that has gained considerable importance in recent years. Among others, it is used to quantify cartilage deterioration for the diagnosis of Osteoarthritis and to optimize surgical planning of knee implants. See the SKI10 paper in the SKI10 Zenodo repository for further details. SKI10 started out as one of the three competitions of the Grand Challenge Workshop 2010, organized in conjunction with the MICCAI 2010 conference.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/624/ski10sq-big.png","https://ski10.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:31:16" -"387","emsig","EMSIG","EMSIG hackathon: radar-based activity recognition","Welcome to the EMSIG Hackathon 2021, organised by EMSIG (www.emsig.org.uk/), the University of Glasgow, Edinburgh Napier University, UCL, DSTL and BAE Systems plc. The goal of the challenge is to evaluate and compare algorithms for human activity recognition based on radar data. We invite the UK radar community to participate by developing and testing existing and novel automated classification methods.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/628/EMSIGgroup_nYL722C.png","https://emsig.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:30:58" -"388","qubiq21","QUBIQ2021","Quantification of uncertainties challenge 2021","The QUBIQ challenge deals with benchmarking algorithms that quantify uncertainties in biomedical image segmentation. Participants will work on binary segmentation tasks, all of which with multiple annotations from domain experts. To be segmented are various pathologies and anatomical structures, such as brain, kidney, or prostate, in MR or CT image data. A successful algorithm will segment these structures and reproduce the distribution of the experts’ annotations.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/629/brain.png","https://qubiq21.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:30:44" -"389","midog2021","MIDOG Challenge 2021","Mitosis domain generalization challenge 2021","Motivation: Mitosis detection is a key component of tumor prognostication for various tumors, including breast cancer. Scanning microscopy slides with different scanners leads to a significant visual difference, resulting in a domain shift. This domain shift prevents most deep learning models from generalizing to other scanners, leading to strongly reduced performance.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/633/midog_logo.png","https://midog2021.grand-challenge.org/","completed","5","https://doi.org/10.5281/zenodo.4573978","\N","\N","2023-11-08 00:42:00","2023-11-15 22:26:47" -"390","tiger","TIGER","Tumor infiltrating lymphocytes assessment","TIGER is the first challenge on fully automated assessment of tumor-infiltrating lymphocytes (TILs) in H&E breast cancer slides. It is organized by the Diagnostic Image Analysis Group (DIAG) of the Radboud University Medical Center (Radboudumc) in Nijmegen (The Netherlands), in close collaboration with the International Immuno-Oncology Biomarker working Group (www.tilsinbreastcancer.org). The goal of this challenge is to evaluate new computer algorithms for the automated assessment of tumor-infiltrating lymphocytes (TILs) in Her2 positive and Triple Negative breast cancer (BC) histopathology slides. In recent years, several studies have shown the predictive and prognostic value of visually scored TILs in BC as well as in other cancer types, making TILs a powerful biomarker that can potentially be used in the clinic. With TIGER, we aim at developing computer algorithms that can automatically generate a ""TIL score"" with a high prognostic value.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/636/tiger-logo_qr7B8JU.png","https://tiger.grand-challenge.org/","active","5","","2022-02-03","\N","2023-11-08 00:42:00","2023-11-15 22:26:17" -"391","stoic2021","STOIC2021 - COVID-19 AI Challenge","COVID-19 AI challenge: CT diagnosis and prognosis","We are launching an artificial intelligence challenge aimed at predicting the severe outcome of COVID-19, based on the largest dataset of Computed Tomography (CT) images of COVID-19 suspects and patients collected to date. Participants will have access to data from the STOIC project, recently published in Radiology. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. The focus of the challenge is the prediction of severe COVID-19, defined as intubation or death within one month from the acquisition of the CT scan (AUC, primary metric). COVID19 positivity will be assessed as a secondary metric in the leaderboard.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/637/stoic2021logo_B3b4JM9.png","https://stoic2021.grand-challenge.org/","active","5","","2021-12-23","2028-01-01","2023-11-08 00:42:00","2023-11-15 22:24:32" -"392","cxr-covid19","Chest XR COVID-19 detection","AI models for COVID-19 detection in chest x-rays","The Coronavirus Disease 2019 (COVID-19) has spread globally and caused unprecedented damages worldwide. Technology, in particular AI, can play an important role in helping fight against this pandemic. In addition, lessons learned can be helpful in fighting and preventing future pandemics. Multiple hospitals and health professionals have shared COVID-19 images coming from multiple modalities to help advance the research in the field. This challenge aims to develop a multiclass classification algorithm capable of detecting COVID-19 in Chest X-ray images. The dataset contains 3 image classes: COVID-19, Pneumonia and Normal (healthy) (See example images below). With 20,000+ images, the participants can train their algorithms to solve this challenge. A test set will be released and will be used to benchmark the obtained results. This Challenge is part of the ‘Ethics and Explainability for Responsible Data Science (EE-RDS) conference’ which will be held virtually and in Johannesburg (So...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/638/CovidChallenge.png","https://cxr-covid19.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:13:57" -"393","pi-cai","The PI-CAI Challenge","AI and radiologists: prostate cancer detection","Prostate cancer (PCa) is one of the most prevalent cancers in men. One million men receive a diagnosis and 300,000 die from clinically significant PCa (csPCa) (defined as ISUP ≥ 2 cancer) each year, worldwide. Multiparametric magnetic resonance imaging (mpMRI) is playing an increasingly important role in the early diagnosis of prostate cancer, and has been recommended by the 2019 European Association of Urology (EAU) guidelines and the 2019 UK National Institute for Health and Care Excellence (NICE) guidelines, prior to biopsies (Mottet et al., 2021). However, current guidelines for reading prostate mpMRI (i.e. PI-RADS v2.1) follow a semi-quantitative assessment, mandating substantial expertise for proper usage. Moreover, prostate cancer can exhibit a broad range of clinical behavior and highly heterogeneous morphology in MRI. As such, assessments are susceptible to low inter-reader agreement (<50%), sub-optimal interpretation and overdiagnosis (Rosenkrantz et al., 2016, Westphale...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/642/square_logo_we441sa.jpg","https://pi-cai.grand-challenge.org/","active","5","https://doi.org/10.5281/zenodo.6667655","2022-06-12","\N","2023-11-08 00:42:00","2023-11-15 22:10:32" -"394","ultra-low-dose-pet","Ultra-low Dose PET Imaging Challenge","Low-dose PET scanner imaging recovery challenge","This challenge aims to develop computational algorithms capable of recovering high-quality imaging from low statistics corresponding to low dose scans, with the hope of reducing the radiation exposure to be equivalent to transatlantic flight.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/643/Ultra-low_Dose_PET-01.jpg","https://ultra-low-dose-pet.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:09:48" -"395","airogs","AIROGS","AI for robust glaucoma screening challenge","Early detection of glaucoma can avoid visual impairment, which could be facilitated through screening. Artificial intelligence (AI) could increase the cost-effectiveness of glaucoma screening, by reducing the need for manual labor. AI approaches for glaucoma detection from color fundus photography (CFP) have been proposed and promising at-the-lab performances have been reported. However, large performance drops often occur when AI solutions are applied in real-world settings. Unexpected out-of-distribution data and bad quality images are major causes for this performance drop. Aim: The development of solutions for glaucoma screening from CFP that are robust to real-world scenarios.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/644/logo2-01-logo.png","https://airogs.grand-challenge.org/","active","5","","2021-12-01","\N","2023-11-08 00:42:00","2023-11-15 22:03:44" -"396","conic-challenge","CoNIC 2022","Colon nuclei identification and counting 2022","The CoNIC challenge starts with the discovery phase where we share the training data, challenge goals, and the evaluation code. Participants should start experimenting with the dataset and train/validate their model on it. All challenge submissions are in the form of a docker container, which means that participants should submit their method to be evaluated on the test sets. In other words, only the training set will be released during the challenge and participants will not have access to any part of the test set. The test will be done in two phases. The first phase, the preliminary test, will give participants a chance to work on their submissions for two weeks, get familiar with the submission workflow, improve their code if needed, and make sure their method works fine in the challenge evaluation pipeline. We will release a template docker structure and tutorials on how participants should package their code using the template and submit it to the challenge website. During ...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/648/conic_logo_YbqWBDc.png","https://conic-challenge.grand-challenge.org/","completed","5","https://arxiv.org/abs/2111.14485","2022-02-13","\N","2023-11-08 00:42:00","2024-01-31 22:31:47" -"397","cholectriplet2022","CholecTriplet2022: Surgical Action Triplet Detection","Dounding box localization of the regions of action triplets","Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies acted upon provides a better, comprehensive and fine-grained modeling of surgical activities. Automatic recognition of these triplet activities directly from surgical videos would facilitate the development of intra-operative decision support systems that are more helpful, especially for safety, in the operating room (OR). Our previous EndoVIS challenge, CholecTriplet2021 (MICCAI 2021), and existing works on surgical action triplet recognition tackles this as a multi-label classification of all possible combinations. For better clinical utility, real-time modeling of tool-tissue interaction will go beyond determining the presence of these action triplets, to also include estimating their locations in each video frame. Hence, this challenge extends our previous challenge on action triplet recognition to also include bounding box localization of the...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/649/t50-logo-2022_kz11kSp.png","https://cholectriplet2022.grand-challenge.org/","completed","5","https://doi.org/10.1007/978-3-030-59716-0_35","\N","\N","2023-11-08 00:42:00","2023-11-27 20:45:26" -"398","endocv2022","EndoCV2022","Endoscopic video sequence detection and segmentation","Accurate detection of artefacts is a core challenge in a wide-range of endoscopic applications addressing multiple different disease areas. The importance of precise detection of these artefacts is essential for high-quality endoscopic video acquisition crucial for realising reliable computer assisted endoscopy tools for improved patient care. In particular, colonoscopy requires colon preparation and cleaning to obtain improved adenoma detection rate. Computer aided systems can help to guide both expert and trainee endoscopists to obtain consistent high quality surveillance and detect, localize and segment widely known cancer precursor lesion, “polyps”. While deep learning has been successfully applied in the medical imaging, generalization is still an open problem. Generalizability issue of deep learning models need to be clearly defined and tackled to build more reliable technology for clinical translation. Inspired by the enthusiasm of participants on our previous challenges, t...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/650/EndoCV2022-logo-v2.jpg","https://endocv2022.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.media.2021.102002","2022-02-20","\N","2023-11-08 00:42:00","2023-11-15 21:56:32" -"399","mela","MELA2022","MICCAI 2022 MELA challenge: ct scan benchmark","The mediastinum is the common site of various lesions, including hyperplasia, cysts, tumors, and lymph nodes transferred from the lungs, which might cause serious problems due to their location. Therefore, the detection of mediastinal lesions has important indications for the early screening and diagnosis of related diseases. Computer-aided diagnosis methods have been developed to assist doctors in interpreting massive computed tomography (CT) scans. However, few prior studies investigate deep learning methods on this labor-intensive task. This challenge establishes a large-scale benchmark dataset to automatically detect mediastinal lesions from 1100 CT scans, consisting of 770 CTs for training, 110 CTs for validation, and 220 CTs for testing. Each annotation file includes coordinates of the bounding box of each mediastinal lesion region per CT scan for serving the task of detection. We hope this challenge could facilitate the research and application of automatic mediastinal lesi...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/651/LOGO-%E8%93%9D-900.png","https://mela.grand-challenge.org/","completed","5","","2022-07-02","2022-07-17","2023-11-08 00:42:00","2023-11-15 21:56:16" -"400","kipa22","KiPA22 (Regular Challenge)","Kidney and artery segmentation challenge","The challenge is aimed to segment kidney, renal tumors, arteries, and veins from computed tomography angiography (CTA) images in one inference.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/654/logo3_%E5%89%AF%E6%9C%AC.png","https://kipa22.grand-challenge.org/","active","5","https://doi.org/10.1016/j.media.2021.102055","2022-07-01","\N","2023-11-08 00:42:00","2023-11-17 23:31:41" -"401","parse2022","Parse2022","Pulmonary artery segmentation challenge 2022","It is of significant clinical interest to study pulmonary artery structures in the field of medical image analysis. One prerequisite step is to segment pulmonary artery structures from CT with high accuracy and low time-consuming. The segmentation of pulmonary artery structures benefits the quantification of its morphological changes for diagnosis of pulmonary hypertension and thoracic surgery. However, due to the complexity of pulmonary artery topology, automated segmentation of pulmonary artery topology is a challenging task. Besides, the open accessible large-scale CT data with well labeled pulmonary artery are scarce (The large variations of the topological structures from different patients make the annotation an extremely challenging process). The lack of well labeled pulmonary artery hinders the development of automatic pulmonary artery segmentation algorithm. Hence, we try to host the first Pulmonary ARtery SEgmentation challenge in MICCAI 2022 (Named Parse2022) to start a...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/658/logo.jpg","https://parse2022.grand-challenge.org/","active","5","","2023-06-30","\N","2023-11-08 00:42:00","2023-11-17 23:31:47" -"402","tdsc-abus2023","TDSC-ABUS2023","Automated 3D breast ultrasound tumor challenge","Tumor Detection, Segmentation And Classification Challenge On Automated 3D Breast Ultrasound","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/662/logo.png","https://tdsc-abus2023.grand-challenge.org/","completed","5","","2023-07-15","2023-08-20","2023-11-08 00:42:00","2023-11-08 01:00:36" -"403","instance","INSTANCE2022","Intracranial hemorrhage segmentation challenge 2022","Participants are required to segment Intracranial Hemorrhage region in Non-Contrast head CT (NCCT).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/667/Logo_%E9%A1%B5%E9%9D%A2_2.png","https://instance.grand-challenge.org/","active","5","https://doi.org/10.1109/jbhi.2021.3103850","2022-07-14","\N","2023-11-08 00:42:00","2023-11-15 21:55:33" -"404","bcnb","BCNB","Early breast cancer core-needle biopsy dataset","Breast cancer (BC) has become the greatest threat to women’s health worldwide. Clinically, identification of axillary lymph node (ALN) metastasis and other tumor clinical characteristics such as ER, PR, and so on, are important for evaluating the prognosis and guiding the treatment for BC patients. Several studies intended to predict the ALN status and other tumor clinical characteristics by clinicopathological data and genetic testing score. However, due to the relatively poor predictive values and high genetic testing costs, these methods are often limited. Recently, deep learning (DL) has enabled rapid advances in computational pathology, DL can perform high-throughput feature extraction on medical images and analyze the correlation between primary tumor features and above status. So far, there is no relevant research on preoperatively predicting ALN metastasis and other tumor clinical characteristics based on WSIs of primary BC samples.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/668/BCNB-logo_CUGMa0V.png","https://bcnb.grand-challenge.org/","completed","5","https://doi.org/10.3389/fonc.2021.759007","\N","\N","2023-11-08 00:42:00","2023-11-16 17:41:33" -"405","ravir","RAVIR","Retinal arteries and veins segmentation dataset","The retinal vasculature provides important clues in the diagnosis and monitoring of systemic diseases including hypertension and diabetes. The microvascular system is of primary involvement in such conditions, and the retina is the only anatomical site where the microvasculature can be directly observed. The objective assessment of retinal vessels has long been considered a surrogate biomarker for systemic vascular diseases, and with recent advancements in retinal imaging and computer vision technologies, this topic has become the subject of renewed attention. In this paper, we present a novel dataset, dubbed RAVIR, for the semantic segmentation of Retinal Arteries and Veins in Infrared Reflectance (IR) imaging. It enables the creation of deep learning-based models that distinguish extracted vessel type without extensive post-processing.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/673/IR_Case_022.png","https://ravir.grand-challenge.org/","active","5","https://doi.org/10.1109/jbhi.2022.3163352","2022-07-18","\N","2023-11-08 00:42:00","2023-11-16 17:41:34" -"406","dfuc2022","DFUC 2022","Diabetic foot ulcer (DFU) segmentation challenge","Diabetes is a global epidemic affecting around 425 million people and expected to rise to 629 million by 2045. Diabetic Foot Ulcer (DFU) is a severe condition that can result from the disease. The rise of the condition over the last decades is a challenge for healthcare systems. Cases of DFU usually lead to severe conditions that greatly prolongs treatment and result in limb amputation or death. Recent research focuses on creating detection algorithms to monitor their condition to improve patient care and reduce strain on healthcare systems. Work between Manchester Metropolitan University, Lancashire Teaching Hospitals and Manchester University NHS Foundation Trust has created an international repository of up to 11,000 DFU images. Analysis of ulcer regions is a key for DFU management. Delineation of ulcers is time-consuming. With effort from the lead scientists of the UK, US, India and New Zealand, this challenge promotes novel work in DFU segmentation and promote interdisciplina...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/674/footsnap_logo.png","https://dfuc2022.grand-challenge.org/","active","5","https://arxiv.org/abs/2204.11618","2022-06-20","\N","2023-11-08 00:42:00","2023-11-17 23:32:01" -"407","atlas","ATLAS R2.0 - Stroke Lesion Segmentation","Anatomical tracings of lesions after stroke","Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires significant neuroanatomical expertise. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N=1271), a larger dataset of T1w stroke MRIs and manually segmented lesion masks that includes training (public. n=655), test (masks hidden, n=300), and generalizability (completely hidden, n=316) data. Algorithm development using this larger sample should lead to more robust solutions, and the hidden test and generalizability datasets allow for unbiased performance eval...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/676/ATLAS_Logo_square.png","https://atlas.grand-challenge.org/","active","5","https://doi.org/10.1101/2021.12.09.21267554","2022-09-18","\N","2023-11-08 00:42:00","2023-11-15 21:54:33" -"408","3dteethseg","3D Teeth Scan Segmentation and Labeling Challenge MICCAI2022","Teeth segmentation in orthodontic CAD systems","Computer-aided design (CAD) tools have become increasingly popular in modern dentistry for highly accurate treatment planning. In particular, in orthodontic CAD systems, advanced intraoral scanners (IOSs) are now widely used as they provide precise digital surface models of the dentition. Such models can dramatically help dentists simulate teeth extraction, move, deletion, and rearrangement and therefore ease the prediction of treatment outcomes. Although IOSs are becoming widespread in clinical dental practice, there are only few contributions on teeth segmentation/labeling available in the literature and no publicly available database. A fundamental issue that appears with IOS data is the ability to reliably segment and identify teeth in scanned observations. Teeth segmentation and labelling is difficult as a result of the inherent similarities between teeth shapes as well as their ambiguous positions on jaws.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/680/Grand-Challenge-Logo_2.jpg","https://3dteethseg.grand-challenge.org/","completed","5","","2022-07-01","2022-08-15","2023-11-08 00:42:00","2023-11-17 23:32:08" -"409","flare22","MICCAI FLARE 2022","Fast and low-resource abdominal organ segmentation","We extend the FLARE 2021 Challenge from fully supervised settings to a semi-supervised setting that focuses on how to use unlabeled data. Specifically, we provide a small number of labeled cases (50) and a large number of unlabeled cases (2000) in the training set, 50 visible cases for validation, and 200 hidden cases for testing. The segmentation targets include 13 organs: liver, spleen, pancreas, right kidney, left kidney, stomach, gallbladder, esophagus, aorta, inferior vena cava, right adrenal gland, left adrenal gland, and duodenum. In addition to the typical Dice Similarity Coefficient (DSC) and Normalized Surface Dice (NSD), our evaluation metrics also focus on the inference speed and resources (GPU, CPU) consumption. Compare to the FLARE 2021 challenge, the dataset is 4x larger and the segmentations targets are increased to 13 organs. Moreover, the resource-related metrics are changed to the area under GPU memory-time curve and the area under CPU utilization-time curve rat...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/683/challenge-logo_xJtvQKE.png","https://flare22.grand-challenge.org/","active","5","https://doi.org/10.1109/tpami.2021.3100536","2023-01-01","\N","2023-11-08 00:42:00","2023-11-15 21:54:18" -"410","aggc22","AGGC22","Segment the Circle of Willis vessel components for both CTA and MRA","Driving innovation in computational pathology for prostate cancer diagnosis. Develop algorithms to identify Gleason Patterns in H&E-stained whole slide images.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/684/logo_yEwrCqI.PNG","https://aggc22.grand-challenge.org/","active","5","","2022-06-29","\N","2023-11-08 00:42:00","2023-11-17 23:32:15" -"411","autopet","autoPET","Whole-body FDG-PET/CT lesion segmentation","Automatic tumor lesion segmentation in whole-body FDG-PET/CT on large-scale database of 1014 studies of 900 patients (training database) acquired on a single site: accurate and fast lesion segmentation avoidance of false positives (brain, bladder, etc.) Testing will be performed on 200 150 studies (held-out test database) with 100 studies originating from the same hospital as the training database and 100 50 are drawn from a different hospital with similar acquisition protocol to assess algorithm robustness and generalizabilit","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/686/autopet-5.png","https://autopet.grand-challenge.org/","completed","5","","2022-05-02","2022-09-04","2023-11-08 00:42:00","2023-11-15 21:53:42" -"412","acrobat","ACROBAT 2023","Acrobat challenge: WSI registration in breast cancer","The ACROBAT challenge aims to advance the development of WSI registration algorithms that can align WSIs of IHC-stained breast cancer tissue sections to corresponding tissue regions that were stained with H&E. All WSIs originate from routine diagnostic workflows.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/687/final_logo_1280x1280_qgi9ILO.png","https://acrobat.grand-challenge.org/","active","5","","2022-06-15","\N","2023-11-08 00:42:00","2023-11-17 23:32:30" -"413","surgt","SurgT: Surgical Tracking","Surgical video tracking for trajectory estimation","This challenge consists of surgical videos with a target bounding box and the participants are expected to develop visual tracking algorithms to estimate the trajectory of the bounding box throughout the video-sequence.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/688/Screenshot_from_2022-06-27_09-41-30.png","https://surgt.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:32:36" -"414","slcn","Surface Learning for Clinical Neuroimaging","Developmental phenotypes prediction from cortical imaging","The goal of this challenge will therefore be to elicit submissions of novel methods for registration-free or registration-robust cortical phenotype regression, with emphasis on interpretable or explainable machine learning methods which deliver biomarkers predictive of risk for neurodevelopmental impairment. These will be benchmarked on the tasks of regression of gestational age at birth (seen as a correlate of prematurity) on both registered and native space cortical surface data.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/689/SLCN_Logo.png","https://slcn.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.neuroimage.2018.01.054","2022-04-24","2022-07-15","2023-11-08 00:42:00","2023-11-15 21:53:02" -"415","p2ilf","Preoperative to Intraoperative Laparoscopy Fusion","Preoperative to intraoperative laparoscopy fusion","Augmented reality (AR) in laparoscopic liver surgery needs key landmark detection in intraoperative 2D laparoscopic images and its registration with the preoperative 3D model from CT/MRI data. Such AR techniques are vital to surgeons as they enable precise tumor localisation for surgical removal. A full resection of targeted tumor minimises the risk of recurrence. However, the task of automatic anatomical curve segmentation (considered as landmarks), and its registration to 3D models is a non-trivial and complex task. Most developed methods in this domain are built around traditional methodologies in computer vision. This challenge is designed to challenge participants to deploy machine learning methods for two tasks - Task I: segmentation of five key anatomical curves from laparoscopic video images and 3D model, including ridge (L, R), ligament, silhouettes, liver boundary; Task 2: matching these segmented curves to the 3D liver model from volumetric data (CT/MR).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/690/logo_V3.png","https://p2ilf.grand-challenge.org/","completed","5","","2022-09-02","2022-09-14","2023-11-08 00:42:00","2023-11-15 21:52:39" -"416","hecktor","MICCAI HECKTOR 2022","Head and neck tumor segmentation in PET/CT","Following the success of the first two editions of the HECKTOR challenge in 2020 and 2021, this challenge will be presented at the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022. Two tasks are proposed this year (participants can choose to participate in either or both tasks): Task 1: The automatic segmentation of Head and Neck (H&N) primary tumors and lymph nodes (new!) in FDG-PET/CT images; Task 2: The prediction of patient outcomes, namely Recurrence-Free Survival (RFS) from the FDG-PET/CT images and available clinical data.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/691/grandchallenge_logo_d8JNqKz.png","https://hecktor.grand-challenge.org/","active","5","https://doi.org/10.1007/978-3-030-98253-9","2022-08-26","2024-05-01","2023-11-08 00:42:00","2023-11-15 21:52:24" -"417","k2s","K2S: from undersampled K-space To automatic Segmentation","K2s: undersampled k-space to segmentation","Magnetic resonance imaging (MRI) is the modality of choice for evaluating knee joint degeneration, but it can be susceptible to long acquisition times, tedious post processing, and lack of standardization. One of the most compelling applications of deep learning, therefore, is accelerated analysis of knee MRI. In addition to faster MRI acquisition, deep learning has enhanced image post-processing applications such as tissue segmentation. While fast, undersampled MRI acquisition may not have qualitative, visual acuity that comes from fully-sampled data, the underlying embedding space may be adequate for some applications. The implications for down-stream tasks such as tissue segmentation using convolutional neural networks are not well-characterized. Efficient segmentation of key anatomical structures from undersampled data is an open question that has clinical relevance, e.g., patient triage. The goal of this challenge, therefore, is to train segmentation models from 8x undersamp...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/692/K2S_gradient_5AE5wa5.png","https://k2s.grand-challenge.org/","completed","5","https://doi.org/10.1148/ryai.2021200165","2022-05-16","2022-06-16","2023-11-08 00:42:00","2023-11-15 21:51:47" -"418","drac22","Diabetic Retinopathy Analysis Challenge MICCAI2022","DR lesions segmentation in UW-OCTA-M images","Diabetic retinopathy is one of the leading causes of blindness and affects approximately 78% people, with a history of diabetes of 15 years or longer [1]. DR often causes gradual changes in vasculature structure and resulting abnormalities. DR is diagnosed by visually inspecting retinal fundus images for the presence of retinal lesions, such as microaneurysms (MAs), intraretinal microvascular abnormalities (IRMAs), nonperfusion areas and neovascularization. The detection of these lesions is critical to the diagnosis of DR. There have been some works using fundus images for DR diagnosis [2]. With rising popularity, OCT angiography (OCTA) has the capability of visualizing the retinal and choroidal vasculature at a microvascular level in great detail [3]. Specially, swept-source (SS)-OCTA allows additionally the individual assessment of the choroidal vasculature. There are already some works using SS-OCTA to grade for qualitative features of diabetic retinopathy [4-6]. Further, ultra...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/694/logo_kT1YYna.png","https://drac22.grand-challenge.org/","active","5","","2022-08-07","\N","2023-11-08 00:42:00","2023-11-15 21:50:58" -"419","amos22","Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022","Multi-modality abdominal multi-organ segmentation","Abdominal multi-organ segmentation is one of the most attractive topics in the field of medical image analysis, which plays an important role in supporting clinical workflows such as disease diagnosis and treatment planning. The recent success of deep learning methods applied for abdominal multi-organ segmentation exposes the lack of large-scale comprehensive benchmarks for developing and comparing such methods. While several benchmark datasets for abdominal organ segmentation are available, the limited number of organs of interest and training samples still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of different methods. And, most research in medical image analysis today focuses on building bespoke systems to handle stereotypical inputs and outputs associated with a single task, the complexity of systems like this can grow dramatically as the inputs or outputs grow more diverse. If a single algorithm could h...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/695/AMOS_2022.png","https://amos22.grand-challenge.org/","active","5","","2022-10-09","2060-12-31","2023-11-08 00:42:00","2023-11-15 21:50:34" -"420","surgtoolloc","Surgical Tool Localization in endoscopic videos","Surgical tool localization in endoscopic videos","The ability to automatically detect and track surgical instruments in endoscopic video will enable many transformational interventions. Assessing surgical performance and efficiency, identifying skilled tool use and choreography, and planning operational and logistical aspects of OR resources are just some of the applications that would benefit. Unfortunately obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task. Annotating bounding boxes frame-by-frame in video is tedious and time consuming, yet a wide variety of surgical tools and surgeries must be captured for robust training. Moreover, ongoing annotator training is needed to stay up to date with surgical instrument innovation. In robot-assisted surgery however, potentially informative data like timestamps of instrument installation and removal can be programmatically harvested. The ability to use only tool presence labels to localize tools would significan...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/698/grand-challenge_logo.png","https://surgtoolloc.grand-challenge.org/","completed","5","","2022-08-29","2022-09-08","2023-11-08 00:42:00","2023-11-15 21:50:20" -"421","curious2022","Brain shift with Intraoperative Ultrasound - Segmentation tasks","Brain shift with intraoperative ultrasound segmentation","Early brain tumor resection can effectively improve the patient’s survival rate. However, resection quality and safety can often be heavily affected by intra-operative brain tissue shift due to factors, such as gravity, drug administration, intracranial pressure change, and tissue removal. Such tissue shift can displace the surgical target and vital structures (e.g., blood vessels) shown in pre-operative images while these displacements may not be directly visible in the surgeon’s field of view. Intra-operative ultrasound (iUS) is a robust and relatively inexpensive technique to track intra-operative tissue shift and surgical tools. Automatic algorithms for brain tissue segmentation in iUS, especially brain tumors and resection cavity can greatly facilitate the robustness and accuracy of brain shift correction through image registration, and allow easy interpretation of the iUS. This has the potential to improve surgical outcomes and patient survival rate. The challenge is an ex...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/699/CuRIOUS_logo.png","https://curious2022.grand-challenge.org/","completed","5","","2022-08-15","2022-09-13","2023-11-08 00:42:00","2023-11-15 21:50:06" -"422","vessel-wall-segmentation-2022","Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis","Carotid vessel wall segmentation and diagnosis","In this challenge, the task is to segment the vessel wall from 3D-VISTA images and diagnose the atherosclerotic lesions with high accuracy and robustness. And then the clinical usable measurements such as wall thickness (difference between the lumen and outer wall contours), lumen area or stenosis percent can be derived from the vessel wall segmentation. In addition, the identification of the lumen and outer wall boundary of the vessel wall is also critical for the diagnosis of lesions. In summary, this challenge focuses on carotid vessel wall segmentation and atherosclerotic lesion diagnosis.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/700/%E5%9B%BE%E7%89%871.png","https://vessel-wall-segmentation-2022.grand-challenge.org/","completed","5","","2022-07-08","2022-08-01","2023-11-08 00:42:00","2023-11-16 17:41:55" -"423","crossmoda2022","Cross-Modality Domain Adaptation: Segmentation & Classification","CrossMoDA 2022: unsupervised domain adaptation","The CrossMoDA 2022 challenge is the second edition of the first large and multi-class medical dataset for unsupervised cross-modality Domain Adaptation.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/701/squarelogo_2022.png","https://crossmoda2022.grand-challenge.org/","active","5","","2022-05-11","\N","2023-11-08 00:42:00","2023-11-17 23:32:53" -"424","atm22","Multi-site, Multi-Domain Airway Tree Modeling (ATM'22)","Airway segmentation in x-ray CT for pulmonary diseases","Airway segmentation is a crucial step for the analysis of pulmonary diseases including asthma, bronchiectasis, and emphysema. The accurate segmentation based on X-Ray computed tomography (CT) enables the quantitative measurements of airway dimensions and wall thickness, which can reveal the abnormality of patients with chronic obstructive pulmonary disease (COPD). Besides, the extraction of patient-specific airway models from CT images is required for navigatiisted surgery.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/702/logo_xqf7twK.png","https://atm22.grand-challenge.org/","active","5","https://doi.org/10.1007/978-3-031-16431-6_48","2022-08-17","\N","2023-11-08 00:42:00","2023-11-21 17:16:40" -"425","ps-fh-aop-2023","FH-PS-AOP challenge","Fetal head and pubic symphysis segmentation","The task of the FH-PS-AOP grand challenge is to automatically segment 700 FH-PSs from transperineal ultrasound images in the devised Set 2 (test set), given the availability of Set 1, consisting of 401 images. Set 2 is held private and therefore not released to the potential participants to prevent algorithm tuning, but instead the algorithms have to be submitted in the form of Docker containers that will be run by organizers on Set 2. The challenge is organized by taking into account the current guidelines for biomedical image analysis competitions, in particular the recommendations of the Biomedical Image Analysis Challenges (BIAS) initiative for transparent challenge reporting.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/703/F2_WDBTbsq.tif","https://ps-fh-aop-2023.grand-challenge.org/","completed","5","https://doi.org/10.1007/s11517-022-02747-1","2023-03-27","2023-09-20","2023-11-08 00:42:00","2023-11-16 17:41:56" -"426","shifts","Shifts Challenge 2022","Shifts challenge 2022: distributional shift and uncertainty","The goal of the Shifts Challenge 2022 is to raise awareness among the research community about the problems of distributional shift, robustness, and uncertainty estimation, and to identify new solutions to address them. The competition will consist of two new tracks: White Matter Multiple Sclerosis (MS) lesion segmentation in 3D Magnetic Resonance Imaging (MRI) of the brain and Marine cargo vessel power estimation.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/704/logo_1200.png","https://shifts.grand-challenge.org/","active","5","https://arxiv.org/abs/2206.15407","2022-09-15","2024-04-08","2023-11-08 00:42:00","2023-11-17 23:33:07" -"427","megc2022","ACMMM MEGC2022: Facial Micro-Expression Grand Challenge","Facial macro- and micro-expressions spotting","The unseen testing set (MEGC2022-testSet) contains 10 long video, including 5 long videos from SAMM (SAMM Challenge dataset) and 5 clips cropped from different videos in CAS(ME)3. The frame rate for SAMM Challenge dataset is 200fps and the frame rate for CAS(ME)3 is 30 fps. The participants should test on this unseen dataset.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/705/acmmm2022_logo.png","https://megc2022.grand-challenge.org/","active","5","https://doi.org/10.1109/fg47880.2020.00029","2022-05-23","\N","2023-11-08 00:42:00","2023-11-16 17:39:17" -"428","midog2022","MItosis DOmain Generalization Challenge 2022","Mitosis domain generalization challenge 2022","Motivation: Mitosis detection is a key component of tumor prognostication for various tumors. Modern deep learning architectures provide detection accuracies for mitosis that are on the level of human experts. Mitosis is known to be relevant for many tumor types, yet, when trained on one tumor / tissue type, the performance will typically drop significantly on another. Scope: Detect mitotic figures (cells undergoing cell division) from histopathology images (object detection). You will be provided with images from 6 different tumor types, 5 out of which are labeled. In total the set consists of 405 cases and includes 9501 mitotic figure annotations in the training set. Evaluation will be done on ten different tumor types with the F1 score as main metric.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/706/midog_compact.png","https://midog2022.grand-challenge.org/","completed","5","https://doi.org/10.5281/zenodo.6362337","2022-08-04","2022-08-30","2023-11-08 00:42:00","2023-11-16 17:39:11" -"429","isles22","Ischemic Stroke Lesion Segmentation Challenge","Ischemic stroke lesion segmentation challenge","The goal of this challenge is to evaluate automated methods of stroke lesion segmentation in MR images. Participants are tasked with automatically generating lesion segmentation masks from DWI, ADC and FLAIR MR modalities. The task consist on a single phase of algorithms evaluation. Participants will submit their segmentation model (""algorithm"") via a Docker container which will then be used to generate predictions on a hidden dataset.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/707/Slide1_N1qHO1K.png","https://isles22.grand-challenge.org/","active","5","","2022-07-15","2030-12-06","2023-11-08 00:42:00","2023-11-15 21:48:03" -"430","neurips22-cellseg","Cell Segmentation in Multi-modality Microscopy Images","Weakly supervised cell segmentation in high-res microscopy","Cell segmentation is usually the first step for downstream single-cell analysis in microscopy image-based biology and biomedical research. Deep learning has been widely used for image segmentation, but it is hard to collect a large number of labeled cell images to train models because manually annotating cells is extremely time-consuming and costly. Furthermore, datasets used are often limited to one modality and lacking in diversity, leading to poor generalization of trained models. This competition aims to benchmark cell segmentation methods that could be applied to various microscopy images across multiple imaging platforms and tissue types. We frame the cell segmentation problem as a weakly supervised learning task to encourage models that use limited labeled and many unlabeled images for cell segmentation as unlabeled images are relatively easy to obtain in practice.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/708/logo_EOVfhip.PNG","https://neurips22-cellseg.grand-challenge.org/","active","5","","2023-08-01","\N","2023-11-08 00:42:00","2023-11-16 17:39:21" -"431","bci","Breast Cancer Immunohistochemical Image Generation Challenge","Breast cancer immunohistochemical image generation","This is an image-to-image translation task that builds a mapping between two domains (HE and IHC). Given an HE image, the algorithm should predict the corresponding IHC image.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/711/logo.png","https://bci.grand-challenge.org/","active","5","","2022-08-24","\N","2023-11-08 00:42:00","2023-11-16 17:39:23" -"432","auto-rtp","Fully Automated Radiotherapy Treatment Planning Challenge","Automated radiotherapy treatment planning in prostate cancer","Participants will be provided with simulation CTs for ten prostate cancer patients, together with a treatment intent/prescription (in a machine readable format). The cases will be a mix of prostate only and prostate + nodes. Participants are asked to generate a treatment plan in an as-automated-as-possible way, including contouring and plan generation. No manual intervention on contouring or planning should be performed, but manual steps to transfer data between systems are permitted if required. Freedom is given to participants with respect to the ""treatment machine"" the plan is designed for. However, it is expected that all participants produce a plan that is deliverable in clinically reasonable time frame.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/713/AUTO-RTP_Logo.png","https://auto-rtp.grand-challenge.org/","active","5","","2023-06-05","\N","2023-11-08 00:42:00","2023-11-16 17:39:25" -"433","2023paip","PAIP 2023: TC prediction in pancreatic and colon cancer","Tumor cellularity prediction in pancreatic and colon cancer","Tumor cellularity (TC) is used to compute the residual tumor burden in several organs, such as the breast and colon. The TC is measured based on semantic cell segmentation, which accurately classifies and delineates individual cells. However, manual analysis of TC is impractical in clinics because of the large volumes of pathological images and is unreliable owing to inconsistent TC values among pathologists. Essentially, tumor cellularity should be calculated by individual cell counting; however, manual counting is impossible, and human pathologists cannot avoid individual differences in diagnostic performance. Automatic image analysis is the ideal method for solving this problem, and it can efficiently reduce the workload of pathologists.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/716/PAIP2023-640.png","https://2023paip.grand-challenge.org/","active","5","","2023-02-15","\N","2023-11-08 00:42:00","2023-11-16 17:39:26" -"434","snemi3d","SNEMI3D: 3D Segmentation of neurites in EM images","IEEE ISBI 2013 challenge: multimodal segmentation","In this challenge, a full stack of electron microscopy (EM) slices will be used to train machine-learning algorithms for the purpose of automatic segmentation of neurites in 3D. This imaging technique visualizes the resulting volumes in a highly anisotropic way, i.e., the x- and y-directions have a high resolution, whereas the z-direction has a low resolution, primarily dependent on the precision of serial cutting. EM produces the images as a projection of the whole section, so some of the neural membranes that are not orthogonal to a cutting plane can appear very blurred. None of these problems led to major difficulties in the manual labeling of each neurite in the image stack by an expert human neuro-anatomist.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/717/logo.png","https://snemi3d.grand-challenge.org/","active","5","","2013-01-15","\N","2023-11-08 00:42:00","2023-11-16 17:39:27" -"435","han-seg2023","The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge","Endometrial carcinoma prediction on whole-slide images","Cancer in the region of the head and neck (HaN) is one of the most prominent cancers, for which radiotherapy represents an important treatment modality that aims to deliver a high radiation dose to the targeted cancerous cells while sparing the nearby healthy organs-at-risk (OARs). A precise three-dimensional spatial description, i.e. segmentation, of the target volumes as well as OARs is required for optimal radiation dose distribution calculation, which is primarily performed using computed tomography (CT) images. However, the HaN region contains many OARs that are poorly visible in CT, but better visible in magnetic resonance (MR) images. Although attempts have been made towards the segmentation of OARs from MR images, so far there has been no evaluation of the impact the combined analysis of CT and MR images has on the segmentation of OARs in the HaN region. The Head and Neck Organ-at-Risk Multi-Modal Segmentation Challenge aims to promote the development of new and applicatio...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/718/logo.jpg","https://han-seg2023.grand-challenge.org/","active","5","","2023-03-26","2024-02-17","2023-11-08 00:42:00","2024-01-31 22:38:41" -"436","endo-aid","Endometrial Carcinoma Detection in Pipelle biopsies","Non-rigid registration challenge for expansion microscopy","Evaluation platform as reference benchmark for algorithms that can predict endometrial carcinoma on whole-slide images of Pipelle sampled endometrial slides stained in H&E, based on the test data set used in our project.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/719/logo-challenge.png","https://endo-aid.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2024-01-31 22:33:53" -"437","rnr-exm","Robust Non-rigid Registration Challenge for Expansion Microscopy","Xray projectomic reconstruction with skeleton segmentation","Despite the wide adoption of ExM, there are few public benchmarks to evaluate the registration pipeline, which limits the development of robust methods for real-world deployment. To address this issue, we have launched RnR-ExM, a challenge that releases 24 pairs of 3D image volumes from three different species. Participants are asked to align these pairs and submit dense deformation fields for assessment. Half of the volume pairs (the validation and test set) have annotated cell structures (nuclei, blood vessels) as registration landmarks.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/720/RnR-ExM_Logo.png","https://rnr-exm.grand-challenge.org/","active","5","","2023-02-17","2028-03-16","2023-11-08 00:42:00","2023-11-16 17:39:32" -"438","xpress","Xray Projectomic Reconstruction Extracting Segment with Skeleton","Automated lesion segmentation in PET/CT - domain generalization","In this task, we provide volumetric XNH images of cortical white matter axons from the mouse brain at 100 nm per voxel isotropic resolution. Additionally, we provide ground truth annotations for axon trajectories. Manual voxel-wise annotation of ground truth is a time-consuming bottleneck for training segmentation networks. On the other hand, skeleton-based ground truth is much faster to annotate, and sufficient to determine connectivity. Therefore, we encourage participants to develop methods to leverage skeleton-based training. To this end, we provide two types of training (validation) sets: a small volume of voxel-wise annotations and a larger volume with skeleton-based annotations. The participants will have the flexibility to use either or both of the provided annotations to train their models, and are challenged to submit an accurate voxel-wise prediction on the test volume. Entries will be evaluated on how accurately the submitted segmentations agree with the ground-truth s...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/721/XPRESS_logo_sq2-01.png","https://xpress.grand-challenge.org/","active","5","","2023-02-06","\N","2023-11-08 00:42:00","2023-11-16 17:39:34" -"439","autopet-ii","autoPET-II","Automated lesion segmentation in PET/CT - domain generalization challenge","Positron Emission Tomography / Computed Tomography (PET/CT) is an integral part of the diagnostic workup for various malignant solid tumor entities. Due to its wide applicability, Fluorodeoxyglucose (FDG) is the most widely used PET tracer in an oncological setting reflecting glucose consumption of tissues, e.g. typically increased glucose consumption of tumor lesions. As part of the clinical routine analysis, PET/CT is mostly analyzed in a qualitative way by experienced medical imaging experts. Additional quantitative evaluation of PET information would potentially allow for more precise and individualized diagnostic decisions. A crucial initial processing step for quantitative PET/CT analysis is segmentation of tumor lesions enabling accurate feature extraction, tumor characterization, oncologic staging and image-based therapy response assessment. Manual lesion segmentation is however associated with enormous effort and cost and is thus infeasible in clinical routine. Automatio...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/722/autopet-5.png","https://autopet-ii.grand-challenge.org/","completed","5","","2023-02-28","2023-09-24","2023-11-08 00:42:00","2023-11-15 21:45:49" -"440","toothfairy","ToothFairy: Cone-Beam Computed Tomography Segmentation Challenge","Toothfairy challenge: inferior alveolar canal segmentation","This is the first edition of the ToothFairy challenge organized by the University of Modena and Reggio Emilia with the collaboration of Raudboud University. This challenge aims at pushing the development of deep learning frameworks to segment the Inferior Alveolar Canal (IAC) by incrementally extending the amount of publicly available 3D-annotated Cone Beam Computed Tomography (CBCT) scans. CBCT modality is becoming increasingly important for treatment planning and diagnosis in implant dentistry and maxillofacial surgery. The three-dimensional information acquired with CBCT can be crucial to plan a vast number of surgical interventions with the aim of preserving noble anatomical structures such as the Inferior Alveolar Canal (IAC), which contains the homonymous nerve (Inferior Alveolar Nerve, IAN). Deep learning models can support medical personnel in surgical planning procedures by providing a voxel-level segmentation of the IAN automatically extracted from CBCT scans.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/723/logo.jpg","https://toothfairy.grand-challenge.org/","active","5","","2023-06-30","\N","2023-11-08 00:42:00","2023-11-17 23:33:47" -"441","spider","SPIDER","Lumbar SPIDER challenge: MRI segmentation of spinal structures","The Lumbar SPIDER Challenge focuses on the segmentation of three anatomical structures in lumbar spine MRI: vertebrae, intervertebral discs (IVDs), and spinal canal. The segmentation task requires participants to produce separate masks for each vertebra, IVD, and the spinal canal in the lumbar spine MRI volume. The numbering of the vertebrae and IVDs is not specific and may vary across different cases.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/724/SPIDER_logo_square_jsl2NDu.png","https://spider.grand-challenge.org/","active","5","","2023-07-26","2024-04-30","2023-11-08 00:42:00","2023-11-17 23:34:08" -"442","lnq2023","LNQ2023","3D lymph node segmentation for comprehensive disease evaluation","Accurate lymph node size estimation is critical for staging cancer patients, initial therapeutic management, and in longitudinal scans, assessing response to therapy. Current standard practice for quantifying lymph node size is based on a variety of criteria that use unidirectional or bidirectional measurements on just one or a few nodes, typically on just one axial slice. But humans have hundreds of lymph nodes, any number of which may be enlarged to various degrees due to disease or immune response. While a normal lymph node may be approximately 5mm in diameter, a diseased lymph node may be several cm in diameter. The mediastinum, the anatomical area between the lungs and around the heart, may contain ten or more lymph nodes, often with three or more enlarged greater than 1cm. Accurate segmentation in 3D would provide more information to evaluate lymph node disease.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/725/LNQ-square.png","https://lnq2023.grand-challenge.org/","completed","5","","2023-05-01","2023-09-30","2023-11-08 00:42:00","2023-11-17 23:34:13" -"443","arcade","ARCADE-MICCAI2023","ARCADE 2023: automatic region-based coronary artery disease diagnostics","Coronary artery disease (CAD) is a condition that affects blood supply of heart, due to buildup of atherosclerotic plaque in the coronary arteries. CAD is one of the leading death causes around the world. The most common diagnosis procedure for CAD is coronary angiography, which uses contrast material and X-rays for observation of lesions in arteries, this type of procedure showing blood flow in coronary arteries in real time what allows precise detection of stenosis and control of intraventricular interventions and stent insertions. Coronary angiography is useful diagnostic method for planning necessary revascularization procedures based on calculated occlusion and affected segment of coronary arteries. The development of automated analytical tool for lesion detection and localization is a promising strategy for increasing effectiveness of detection and treatment strategies for CAD.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/726/aRCADE__1.jpg","https://arcade.grand-challenge.org/","completed","5","","2023-06-07","2023-09-20","2023-11-08 00:42:00","2023-11-15 21:45:02" -"444","ultrasoundenhance2023","Ultrasound Image Enhancement challenge 2023","Ultrasound image enhancement challenge 2023","Ultrasound imaging is commonly used for aiding disease diagnosis and treatment, with advantages in noninvasive. Lately, medical ultrasound shows prospects revolving from expensive big-size machines in hospitals to economical hand-held devices in wider use. The barrier is that ultrasound examination with a handheld device has the drawback of low imaging quality due to hardware limitations. Toward this, ultrasound image enhancement provides a potential low-cost solution. Restoring high-quality images from low-quality ones using computer algorithms would exempt requirements for hardware improvements and promote ultrasound device revolutions and wider applications. We propose to hold the challenge of enhancement for ultrasound images in conjunction with MICCAI 2023. We will provide various ultrasound data of five organs, including the thyroid, carotid artery, liver, breast, and kidney. The challenging task is reconstructing high-quality ultrasound images from low-quality ones. A tota...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/727/logo.png","https://ultrasoundenhance2023.grand-challenge.org/","completed","5","","2023-07-10","2023-08-31","2023-11-08 00:42:00","2023-11-15 21:44:45" -"445","multicenteraorta","SEG.A. - Segmentation of the Aorta","Aortic vessel tree segmentation challenge in CT images","Segmentation, modeling and visualization of the arterial tree are still a challenge in medical image analysis. The main track of this challenge deals with the fully automatic segmentation of the aortic vessel tree in computed tomography images. Optionally, teams can submit tailored solutions for meshing and visualization of the vessel tree.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/729/logo_final_miccai.jpg","https://multicenteraorta.grand-challenge.org/","completed","5","https://arxiv.org/abs/2108.02998","2023-06-15","2023-08-15","2023-11-08 00:42:00","2023-11-17 23:34:24" -"446","sppin","Surgical Planning in Pediatric Neuroblastoma","Pediatric neuroblastoma surgical planning challenge","Neuroblastoma: Neuroblastoma is one of the most common cancers in children, accounting for 15% of pediatric cancer related deaths. This tumor originates from the symphatic nervous system, and is often located in the abdomen. Treatment of neuroblastoma includes surgical resection of the tumor, but complete resection of the tumor is often challenging. Surgical planning in Neuroblastoma: Surgical procedures can be complicated due to the neuroblastoma often being in proximity or even encasing organs and vessels in the affected area. These structures can include abdominal organs such as kidneys, liver, pancreas and spleen or big abdominal vessels such as the aorta and renal veins. During surgical planning it is essential to have a clear understanding of the neuroblastoma in relation to the relevant anatomy. Currently, magnetic resonance imaging (MRI) is used as pre-operative imaging. Studying 3D models of the tumor and relevant structures guides surgeons in the pre-operative understan...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/730/SPINN_Logo_SB_2023_03_11-09_TnwZJgK.png","https://sppin.grand-challenge.org/","completed","5","","2023-08-10","2023-09-01","2023-11-08 00:42:00","2023-11-11 01:52:06" -"447","medfm2023","Foundation Model Prompting for Medical Image Classification","Model adaptation for medical image classification challenge","In the past few years, deep learning foundation models have been trendy, especially in computer vision and natural language processing. As a result, many milestone works have been proposed, such as Vision Transformers (ViT), Generative Pretrained Transformer (GPT), and Contrastive Language-Image Pretraining (CLIP). They aim to solve many downstream tasks by utilizing the robust representation learning and generalization abilities of foundation models.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/731/logo640.png","https://medfm2023.grand-challenge.org/","active","5","","2023-07-14","2033-10-15","2023-11-08 00:42:00","2023-11-16 17:39:48" -"448","dentex","DENTEX - MICCAI23","Dental enumeration and diagnosis on panoramic x-rays","Panoramic X-rays are widely used in dental practice to provide a comprehensive view of the oral cavity and aid in treatment planning for various dental conditions. However, interpreting these images can be a time-consuming process that can distract clinicians from essential clinical activities. Moreover, misdiagnosis is a significant concern, as general practitioners may lack specialized training in radiology, and communication errors can occur due to work exhaustion.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/732/logo_diseased.png","https://dentex.grand-challenge.org/","completed","5","https://arxiv.org/abs/2303.06500","2023-04-30","2023-09-01","2023-11-08 00:42:00","2023-11-16 17:39:45" -"449","segrap2023","SegRap 2023","Multi-modal CT image segmentation challenge with 45 OARs","Radiotherapy is one of the most important cancer treatments for killing cancer cells with external beam radiation. Treatment planning is vital for radiotherapy, which sets up the radiation dose distribution for tumors and ordinary organs. The goal of planning is to ensure the cancer cells receive enough radiation and to prevent normal cells in organs-at-risk (OARs) from being damaged too much. For instance, optical nerves and chiasma in the head cannot receive too much radiation. Otherwise, the patient risks losing his/her vision. Gross Target Volume (GTV) is the position and extent of gross tumor imaged by CT scans, i.e., what can be seen. A critical step in radiation treatment planning is to delineate the boundaries of GTV and tens of OARs. However, manual delineation slice-by-slice in CT scans is tedious and time-consuming for radiation oncologists. Automatic delineation of GTV and OARs would substantially reduce the treatment planning time and therefore improve the efficiency ...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/734/segrap-logo_cLCSrpZ.png","https://segrap2023.grand-challenge.org/","completed","5","","2023-07-09","2023-09-13","2023-11-08 00:42:00","2023-11-16 17:39:47" -"450","ldctiqac2023","Low-dose Computed Tomography Perceptual Image Quality Assessment","Low-dose CT perceptual image quality assessment challenge","Image quality assessment (IQA) is extremely important in computed tomography (CT) imaging, since it facilitates the optimization of radiation dose and the development of novel algorithms in medical imaging, such as restoration. In addition, since an excessive dose of radiation can cause harmful effects in patients, generating high-quality images from low-dose images is a popular topic in the medical domain. However, even though peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are the most widely used evaluation metrics for these algorithms, their correlation with radiologists’ opinion of the image quality has been proven to be insufficient in previous studies, since they calculate the image score based on numeric pixel values (1-3). In addition, the need for pristine reference images to calculate these metrics makes them ineffective in real clinical environments, considering that pristine, high-quality images are often impossible to obtain due to th...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/735/IQA_logo_3io1BcW.png","https://ldctiqac2023.grand-challenge.org/","completed","5","","2023-04-19","2023-07-28","2023-11-08 00:42:00","2023-11-16 17:39:58" -"451","cl-detection2023","CL-Detection 2023","Cephalometric landmark detection in lateral x-ray images","We invite you to participate in the CL-Detection 2023 cephalometric landmark detection challenge, which is held with 2023 MICCAI conference. Prof Wang is also hosting another challenge in MICCAI 2023. If you are seeking more publication opportunities, feel free to check the challenge website (Automated prediction of treatment effectiveness in ovarian cancer using histopathological images)","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/737/logo.png","https://cl-detection2023.grand-challenge.org/","completed","5","https://doi.org/10.1109/tmi.2015.2412951","2023-05-31","2023-08-15","2023-11-08 00:42:00","2023-11-16 17:39:52" -"452","surgtoolloc23","Endoscopic surgical tool localization using tool presence labels","Endoscopic surgical tool localization challenge","The ability to automatically detect and track surgical instruments in endoscopic video will enable many transformational interventions. Assessing surgical performance and efficiency, identifying skilled tool use and choreography, and planning operational and logistical aspects of OR resources are just some of the applications that would benefit. The annotations needed to train machine learning models to robustly identify and localize surgical tools, however, are difficult to obtain. Annotating bounding boxes frame-by-frame in video is tedious and time consuming, yet a wide variety of surgical tools and surgeries must be captured for robust training. Moreover, ongoing annotator training is needed to stay up to date with surgical instrument innovation. In robot-assisted surgery, potentially informative data like timestamps of instrument installation and removal, can be programmatically harvested. The ability to use only tool presence labels to localize tools would significantly redu...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/738/grand-challenge_logo_2023.png","https://surgtoolloc23.grand-challenge.org/","completed","5","","2023-06-29","2023-09-20","2023-11-08 00:42:00","2023-11-16 17:39:53" -"453","ocelot2023","OCELOT 2023: Cell Detection from Cell-Tissue Interaction","Cell detection from cell-tissue interaction","Cell detection in histology images is one of the most important tasks in computational pathology. Recently, the OCELOT dataset was released in [1] which provides overlapping cell and tissue annotations on images acquired from multiple organs stained with H&E. [1] showed that understanding the relationship between the surrounding tissue structures and individual cells can boost cell detection performance.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/739/gc_loco_XeBK92j.png","https://ocelot2023.grand-challenge.org/","completed","5","https://arxiv.org/abs/2303.13110","2023-06-04","2023-08-04","2023-11-08 00:42:00","2023-11-16 17:39:56" -"454","thompson-challenge","The Trauma THOMPSON Challenge","Trauma thompson challenge: trauma image analysis","The primary goal of The Trauma THOMPSON Challenge is to find the best algorithms for automatic action recognition and prediction using computer vision from first-person view in the medical domain (refer to egocentric datasets of medical procedures). We offer the first egocentric view dataset of life-saving intervention (LSI) procedures with detailed annotations by medical professionals. We have collected over 200 procedure videos with environment, simulator, and type variability. Based on this dataset, the challenge we propose involves multiple tasks to encourage participants across the globe to design impactful algorithms with applications to medicine. The envisioned algorithms include action recognition, action anticipation, procedure recognition, and visual question answering (VQA).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/742/ttlog_black_-_Copy.png","https://thompson-challenge.grand-challenge.org/","completed","5","","2023-09-01","\N","2023-11-08 00:42:00","2024-01-31 22:34:26" -"455","bonbid-hie2023","Hypoxic Ischemic Encephalopathy Lesion Segmentation Challenge","Hypoxic ischemic encephalopathy lesion segmentation","Hypoxic ischemic encephalopathy (HIE) is a brain injury that occurs in 1 ~ 5/1000 term-born neonates. HIE affects around 200,000 term-born neonates every year worldwide, costing about $2 billion/year in the US alone, let alone family burdens. Although therapeutic hypothermia can reduce mortality and morbidity, yet around 60% of patients still die or develop neurocognitive deficits by 2 years of age. HIE lesion segmentation is a crucial step in clinical care of HIE. It will lead to a more accurate estimation of prognosis, a better understanding of neurological symptoms, and a timely prediction of response to therapy.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/743/hie.png","https://bonbid-hie2023.grand-challenge.org/","completed","5","","2023-07-01","2023-09-21","2023-11-08 00:42:00","2023-11-16 17:40:00" -"456","panorama","PANORAMA","AI and radiologists at pancreatic cancer diagnosis","The PANORAMA (Pancreatic cancer diagnosis: Radiologists meet AI) study is a new prospectively designed multi-center study with over 1500 cases, established in conjunction with an international, multi-disciplinary scientific advisory board (11 experts in pancreas radiology, AI and pancreatic cancer survivor representative) ⁠—to unify and standardize present-day guidelines and to ensure meaningful validation of pancreas-AI towards clinical translation (Reinke et al., 2022).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/746/panorama_logo_square.png","https://panorama.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-16 17:40:02" -"457","cameo-3d","CAMEO-3D","Protein structure prediction quality assessment","Cameo Continuously Applies Quality Assessment Criteria Established By The Protein Structure Prediction Community. Since The Accuracy Requirements For Different Scientific Applications Vary, There Is No ""One Fits All"" Score. Cameo Therefore Offers A Variety Of Scores - Assessing Different Aspects Of A Prediction (Coverage, Local Accuracy, Completeness, Etc.) To Reflect These Requirements.","","https://www.cameo3d.org/modeling/3-months/","active","18","","2023-11-04","\N","2023-11-11 01:29:20","2023-11-16 22:41:57" -"458","cameo-qe-model-quality-estimation","CAMEO-QE: Model Quality Estimation","Protein structure prediction quality assessment","Cameo Continuously Applies Quality Assessment Criteria Established By The Protein Structure Prediction Community. Since The Accuracy Requirements For Different Scientific Applications Vary, There Is No ""One Fits All"" Score. Cameo Therefore Offers A Variety Of Scores - Assessing Different Aspects Of A Prediction (Coverage, Local Accuracy, Completeness, Etc.) To Reflect These Requirements.","","https://www.cameo3d.org/quality-estimation/","active","18","","2023-11-04","\N","2023-11-11 01:29:20","2023-11-16 22:41:57" -"459","cameo-3d-modeling-of-structures-and-complexes-beta","CAMEO-3D: Modeling of Structures & Complexes - BETA","Protein structure prediction quality assessment","Cameo Continuously Applies Quality Assessment Criteria Established By The Protein Structure Prediction Community. Since The Accuracy Requirements For Different Scientific Applications Vary, There Is No ""One Fits All"" Score. Cameo Therefore Offers A Variety Of Scores - Assessing Different Aspects Of A Prediction (Coverage, Local Accuracy, Completeness, Etc.) To Reflect These Requirements.","","https://beta.cameo3d.org/complete-modeling/","active","18","","2023-11-04","\N","2023-11-11 01:29:20","2023-11-16 22:41:58" -"460","pegs-dream-challenge","PEGS DREAM Challenge","","","","https://www.synapse.org/pegs","upcoming","1","","\N","\N","2023-11-13 22:48:02","2023-11-16 16:20:18" -"461","fda-data-centric-challenge","FDA Data-Centric Challenge","","The Food and Drug Administration (FDA) - Center for Devices and Radiological Health (CDRH), Sage Bionetworks, and precisionFDA call on the scientific, industry, and data science communities to develop methods to augment the training data and improve the robustness of a baseline artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD).","","https://www.synapse.org/fda_data_centric","upcoming","1","","\N","\N","2023-11-13 22:49:41","2023-12-12 19:02:40" -"462","ai-institute-for-dynamic-systems","AI Institute for Dynamic Systems","","","","https://www.synapse.org/#!Synapse:syn52052735","upcoming","1","","\N","\N","2023-11-13 22:51:53","2023-11-17 0:13:33" -"463","competition-nih-alzheimers-adrd-1","PREPARE Phase 1 - Find IT!","Help NIH break new ground in early Alzheimer's prediction and related dementias","The goal of the PREPARE Challenge (Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA Challenge) is to inform novel approaches to early detection that might ultimately lead to more accurate tests, tools, and methodologies for clinical and research purposes. Advances in artificial intelligence (AI), machine learning (ML), and computing ecosystems increase possibilities of intelligent data collection and analysis, including better algorithms and methods that could be leveraged for the prediction of biological, psychological (cognitive), socio-behavioral, functional, and clinical changes related to AD/ADRD. This first phase, Find IT!: Data for Early Prediction, is focused on finding, curating, or contributing data to create representative and open datasets that can be used for the early prediction of AD/ADRD.","","https://www.drivendata.org/competitions/253/competition-nih-alzheimers-adrd-1/","completed","19","","2023-09-01","2024-01-31","2023-11-16 21:57:03","2023-12-06 7:15:18" -"464","prepare-phase-2-build-it","PREPARE Phase 2 - Build IT!","Help NIH break new ground in early Alzheimer's prediction and related dementias","The goal of the PREPARE Challenge (Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA Challenge) is to inform novel approaches to early detection that might ultimately lead to more accurate tests, tools, and methodologies for clinical and research purposes. Advances in artificial intelligence (AI), machine learning (ML), and computing ecosystems increase possibilities of intelligent data collection and analysis, including better algorithms and methods that could be leveraged for the prediction of biological, psychological (cognitive), socio-behavioral, functional, and clinical changes related to AD/ADRD. This second phase, Build IT!: Algorithms and Approaches, is focused on advancing algorithms and analytic approaches for early prediction of AD/ADRD, with an emphasis on explainability of predictions.","","","upcoming","19","","2024-09-01","\N","2023-11-17 00:09:25","2023-12-06 7:18:18" -"465","prepare-phase-3-put-it-all-together","PREPARE Phase 3 - Put IT All Together!","Help NIH break new ground in early Alzheimer's prediction and related dementias","The goal of the PREPARE Challenge (Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA Challenge) is to inform novel approaches to early detection that might ultimately lead to more accurate tests, tools, and methodologies for clinical and research purposes. Advances in artificial intelligence (AI), machine learning (ML), and computing ecosystems increase possibilities of intelligent data collection and analysis, including better algorithms and methods that could be leveraged for the prediction of biological, psychological (cognitive), socio-behavioral, functional, and clinical changes related to AD/ADRD. This third phase, Put IT All Together!: Proof of Principle Demonstration, is for the top solvers from Phase 2 demonstrate algorithmic approaches on diverse datasets and share their results at an innovation event.","","","upcoming","19","","2025-03-01","\N","2023-11-17 00:09:26","2023-12-06 7:18:08" -"466","cdc-fall-narratives","Unsupervised Wisdom: Explore Medical Narratives on Older Adult Falls","Extract insights about older adult falls from emergency department narratives","Falls among adults 65 and older is the leading cause of injury-related deaths. Falls can also result in serious injuries to the head and/or broken bones. Some risk factors associated with falls can be reduced through appropriate interventions like treating vision problems, exercising for strength and balance, and removing tripping hazards in your home. Medical record narratives are a rich yet under-explored source of potential insights about how, when, and why people fall. However, narrative data sources can be difficult to work with, often requiring carefully designed, time-intensive manual coding procedures. Modern machine learning approaches to working with narrative data have the potential to effectively extract insights about older adult falls from narrative medical record data at scale. The goal in this challenge is to identify effective methods of using unsupervised machine learning to extract insights about older adult falls from emergency department narratives. Insights...","https://drivendata-public-assets.s3.amazonaws.com/cdc-banner-hands.png","https://www.drivendata.org/competitions/217/cdc-fall-narratives/","completed","19","","\N","2023-10-06","2023-12-06 06:56:06","2023-12-06 7:21:14" -"467","visiomel-melanoma","VisioMel Challenge: Predicting Melanoma Relapse","Use digitized microscopic slides to predict the likelihood of melanoma relapse","Melanoma is a cancer of the skin which develops from cells responsible for skin pigmentation. In 2020, over 325,000 people were diagnosed with skin melanoma, with 57,000 deaths in the same year.1 Melanomas represent 10% of all skin cancers and are the most dangerous due to high likelihood of metastasizing (spreading). Patients are initially diagnosed with melanoma after a pathologist examines a portion of the cancerous tissue under a microscope. At this stage, the pathologist assesses the risk of relapse—a return of cancerous cells after the melanoma has been treated—based on information such as the thickness of the tumor and the presence of an ulceration. Combined with factors such as age, sex, and medical history of the patient, these microscopic observations can help a dermatologist assess the severity of the disease and determine appropriate surgical and medical treatment. Preventative treatments can be administered to patients with high likelihood for relapse. However, these...","https://drivendata-public-assets.s3.amazonaws.com/visiomel_banner_img.jpeg","https://www.drivendata.org/competitions/1481/visiomel-melanoma/","completed","19","","\N","2023-05-11","2023-12-06 07:35:00","2023-12-06 7:52:55" -"468","competition-cervical-biopsy","TissueNet: Detect Lesions in Cervical Biopsies","Detect the most severe epithelial lesions of the uterine cervix","A biopsy is a sample of tissue examined at a microscopic level to diagnose cancer or signs of pre-cancer. While most diagnoses are still made with photonic microscopes, digital pathology has developed considerably over the past decade as it has become possible to digitize slides into ""virtual slides"" or ""whole slide images"" (WSIs). These heavy image files contain all the information required to diagnose lesions as malignant or benign. Making this diagnosis is no easy task. It requires specialized training and careful examination of microscopic tissue. Approaches in machine learning are already able to help analyze WSIs by measuring or counting areas of the image under a pathologist's supervision. In addition, computer vision has shown some potential to classify tumor subtypes, and in time may offer a powerful tool to aid pathologists in making the most accurate diagnoses. This challenge focuses on epithelial lesions of the uterine cervix, and features a unique collection of thou...","https://s3.amazonaws.com/drivendata-public-assets/sfp_comp_image.jpg","https://www.drivendata.org/competitions/67/competition-cervical-biopsy/","completed","19","","\N","2020-10-29","2023-12-06 07:52:34","2023-12-06 7:58:21" -"469","clog-loss-alzheimers-research","Clog Loss: Advance Alzheimer’s Research with Stall Catchers","Automatically classify which blood vessels are flowing and which are stalled","5.8 million Americans live with Alzheimer’s dementia, including 10% of all seniors 65 and older. Scientists at Cornell have discovered links between “stalls,” or clogged blood vessels in the brain, and Alzheimer’s. Stalls can reduce overall blood flow in the brain by 30%. The ability to prevent or remove stalls may transform how Alzheimer’s disease is treated. Stall Catchers is a citizen science project that crowdsources the analysis of Alzheimer’s disease research data provided by Cornell University’s Department of Biomedical Engineering. It resolves a pressing analytic bottleneck: for each hour of data collection it would take an entire week to analyze the results in the lab, which means an entire experimental dataset would take 6-12 months to analyze. Today, the Stall Catchers players are collectively analyzing data 5x faster than the lab while exceeding data quality requirements. The research team has realized there are aspects of this task that are best suited to uniquely h...","","https://www.drivendata.org/competitions/65/clog-loss-alzheimers-research/","completed","19","","\N","2020-08-03","2023-12-06 08:04:52","2023-12-06 8:07:15" -"470","flu-shot-learning","Flu Shot Learning: Predict H1N1 and Seasonal Flu Vaccines","Predict whether people got H1N1 and flu vaccines using information they shared","In this challenge, we will take a look at vaccination, a key public health measure used to fight infectious diseases. Vaccines provide immunization for individuals, and enough immunization in a community can further reduce the spread of diseases through ""herd immunity."" As of the launch of this competition, vaccines for the COVID-19 virus are still under development and not yet available. The competition will instead revisit the public health response to a different recent major respiratory disease pandemic. Beginning in spring 2009, a pandemic caused by the H1N1 influenza virus, colloquially named ""swine flu,"" swept across the world. Researchers estimate that in the first year, it was responsible for between 151,000 to 575,000 deaths globally. A vaccine for the H1N1 flu virus became publicly available in October 2009. In late 2009 and early 2010, the United States conducted the National 2009 H1N1 Flu Survey. This phone survey asked respondents whether they had received the H1N1...","https://drivendata-public-assets.s3.amazonaws.com/flu-vaccine.jpg","https://www.drivendata.org/competitions/66/flu-shot-learning/","active","19","","\N","2024-07-30","2023-12-06 08:10:49","2023-12-06 8:14:49" -"471","machine-learning-with-a-heart","Warm Up: Machine Learning with a Heart","Predict the presence or absence of heart disease in patients","We've all got to start somewhere. This is one of the smallest datasets on DrivenData. That makes it a great place to dive into the world of data science competitions. Get your heart thumping and try your hand at predicting heart disease.","","https://www.drivendata.org/competitions/54/machine-learning-with-a-heart/","completed","19","","\N","2019-10-30","2023-12-06 08:19:47","2023-12-06 8:21:53" -"472","dengai-predicting-disease-spread","DengAI: Predicting Disease Spread","Predict the number of dengue fever cases reported each week in 2 regions","Using environmental data collected by various U.S. Federal Government agencies—from the Centers for Disease Control and Prevention to the National Oceanic and Atmospheric Administration in the U.S. Department of Commerce—can you predict the number of dengue fever cases reported each week in San Juan, Puerto Rico and Iquitos, Peru? This is an intermediate-level practice competition. Your task is to predict the number of dengue cases each week (in each location) based on environmental variables describing changes in temperature, precipitation, vegetation, and more. An understanding of the relationship between climate and dengue dynamics can improve research initiatives and resource allocation to help fight life-threatening pandemics.","","https://www.drivendata.org/competitions/44/dengai-predicting-disease-spread/","active","19","","\N","2024-10-05","2023-12-06 08:28:42","2023-12-06 8:30:39" -"473","senior-data-science-safe-aging-with-sphere","Senior Data Science: Safe Aging with SPHERE","Predict actual activity from sensor data in seniors","This challenge is part of a large research project which centers around using sensors and algorithms to help older people live safely at home while maintaining their privacy and independence. Using passive, automated monitoring, the ultimate goal is to look out for a person's well-being without being burdensome or intrusive. To gather data, researchers in the SPHERE Inter-disciplinary Research Collaboration (IRC) equipped volunteers with accelerometers similar to those found in cell phones or fitness wearables, and then had the subjects go about normal activities of daily living in a home-like environment that was also equipped with motion detectors. After gathering a robust set of sensor data, they had multiple annotators use camera footage to establish the ground truth, labeling chunks of sensor data as one of twenty specifically chosen activities (e.g. walk, sit, stand-to-bend, ascend stairs, descend stairs, etc). Your challenge: help push forward the state of the art by pred...","","https://www.drivendata.org/competitions/42/senior-data-science-safe-aging-with-sphere/","completed","19","","\N","2016-07-31","2023-12-06 08:35:31","2023-12-06 8:44:36" -"474","countable-care-modeling-womens-health-care-decisions","Countable Care: Modeling Women's Health Care Decisions","Predict what drives women’s health care decisions in America","Recent literature suggests that the demand for women’s health care will grow over 6% by 2020. Given how rapidly the health landscape has been changing over the last 15 years, it’s increasingly important that we understand how these changes affect what care people receive, where they go for it, and how they pay. Through the National Survey of Family Growth, the CDC provides one of the few nationally representative datasets that dives deep into the questions that women face when thinking about their health. Can you predict what drives women’s health care decisions in America?","","https://www.drivendata.org/competitions/6/countable-care-modeling-womens-health-care-decisions/","completed","19","","\N","2015-04-14","2023-12-06 08:45:12","2023-12-06 8:46:00" -"475","warm-up-predict-blood-donations","Warm Up: Predict Blood Donations","Predict whether a donor will return to donate blood given their donation history","We've all got to start somewhere. This is the smallest, least complex dataset on DrivenData. That makes it a great place to dive into the world of data science competitions. Get your blood pumping and try your hand at predicting donations.","","https://www.drivendata.org/competitions/2/warm-up-predict-blood-donations/","completed","19","","\N","2019-03-21","2023-12-06 08:52:21","2023-12-06 8:53:13" -"476","genetic-engineering-attribution","Genetic Engineering Attribution Challenge","Identify the lab-of-origin for genetically engineered DNA","our goal is to create an algorithm that identifies the most likely lab-of-origin for genetically engineered DNA. Applications for genetic engineering are rapidly diversifying. Researchers across the world are using powerful new techniques in synthetic biology to solve some of the world’s most pressing challenges in medicine, agriculture, manufacturing and more. At the same time, increasingly powerful genetically engineered systems could yield unintended consequences for people, food crops, livestock, and industry. These incredible advances in capability demand tools that support accountable innovation. Genetic engineering attribution is the process of identifying the source of a genetically engineered piece of DNA. This ability ensures that scientists who have spent countless hours developing breakthrough technology get their due credit, intellectual property is protected, and responsible innovation is promoted. By connecting a genetically engineered system with its designers, s...","https://s3.amazonaws.com/drivendata-public-assets/al-green-homepage.jpg","https://www.drivendata.org/competitions/63/genetic-engineering-attribution/","completed","19","","\N","2020-10-19","2023-12-06 08:54:24","2023-12-06 8:56:29" -"477","neural-latents-benchmark-21","Neural Latents Benchmark '21","A benchmark on co-smoothing or inference of firing rates of unseen neurons","Advances in neural recording present increasing opportunities to study neural activity in unprecedented detail. Latent variable models (LVMs) are promising tools for analyzing this rich activity across diverse neural systems and behaviors, as LVMs do not depend on known relationships between the activity and external experimental variables. To coordinate LVM modeling efforts, we introduce the Neural Latents Benchmark (NLB). The first benchmark suite, NLB 2021, evaluates models on 7 datasets of neural spiking activity spanning 4 tasks and brain areas.","https://neurallatents.github.io/logo.svg","https://eval.ai/web/challenges/challenge-page/1256/overview","completed","16","","\N","2022-04-03","2023-12-12 18:31:00","2023-12-12 22:39:42" -"478","brain-to-text-benchmark-24","Brain-to-Text Benchmark '24","Develop new and improved algorithms for decoding speech from the brain","People with ALS or brainstem stroke can lose the ability to move, rendering them “locked-in” their own bodies and unable to communicate. Speech brain-computer interfaces (BCIs) can restore communication by decoding what someone is trying to say directly from their brain activity. Once deciphered, the person’s intended message can be spoken for them or typed as text on a computer. We recently showed that a speech BCI can decode speech at 62 words per minute with a 23% word error rate, demonstrating the potential of a high-performance speech BCI. Nevertheless, word error rates are not yet low enough for fluent communication. The goal of this competition is to foster the development of new and improved algorithms for decoding speech from the brain. Improved accuracies will make it more likely that a speech BCI can be clinically translated, improving the lives of those with paralysis. We hope that this baseline can also serve as an indicator of progress in the field and provide a st...","https://evalai.s3.amazonaws.com/media/logos/35b2c474-c1be-41ae-97a4-49446766f9b1.png","https://eval.ai/web/challenges/challenge-page/2099/overview","active","16","","2023-06-01","2024-06-01","2023-12-12 21:54:25","2023-12-12 22:38:33" -"479","vqa-answertherapy-2024","VQA-AnswerTherapy 2024","Grounding all answers for each visual question","Visual Question Answering (VQA) is a task of predicting the answer to a question about an image. Given that different people can provide different answers to a visual question, we aim to better understand why with answer groundings. To achieve this goal, we introduce the VQA-AnswerTherapy dataset, the first dataset that visually grounds each unique answer to each visual question. We offer this work as a valuable foundation for improving our understanding and handling of annotator differences. This work can inform how to account for annotator differences for other related tasks such as image captioning, visual dialog, and open-domain VQA (e.g., VQAs found on Yahoo!Answers and Stack Exchange). This work also contributes to ethical AI by enabling revisiting how VQA models are developed and evaluated to consider the diversity of plausible answer groundings rather than a single (typically majority) one.","https://evalai.s3.amazonaws.com/media/logos/e63bc0a0-cd35-4418-b32b-4ef2b9c61ce2.png","https://eval.ai/web/challenges/challenge-page/1910/overview","active","16","","2024-01-30","2199-12-26","2023-12-12 22:41:48","2024-01-31 23:05:00" -"480","vqa-challenge-2021","VQA Challenge 2021","Answer open-ended, free-form natural language questions about images","Recent progress in computer vision and natural language processing has demonstrated that lower-level tasks are much closer to being solved. We believe that the time is ripe to pursue higher-level tasks, one of which is Visual Question Answering (VQA), where the goal is to be able to understand the semantics of scenes well enough to be able to answer open-ended, free-form natural language questions (asked by humans) about images. VQA Challenge 2021 is the 6th edition of the VQA Challenge on the VQA v2.0 dataset introduced in Goyal et al., CVPR 2017. The 2nd, 3rd, 4th and 5th editions of the VQA Challenge were organized in CVPR 2017, CVPR 2018, CVPR 2019 and CVPR 2020 on the VQA v2.0 dataset. The 1st edition of the VQA Challenge was organized in CVPR 2016 on the 1st edition (v1.0) of the VQA dataset introduced in Antol et al., ICCV 2015.","https://evalai.s3.amazonaws.com/media/logos/85d3b99e-b3a7-498a-a142-3325eab17138.png","https://eval.ai/web/challenges/challenge-page/830/overview","completed","16","","2021-02-24","2021-05-07","2023-12-12 22:42:59","2023-12-12 23:00:07" -"481","ntx-hackathon-2023-sleep-states","NTX Hackathon 2023 - Sleep States","Speculate on possible use-cases of Neurotechnology and BCI","This competition is dedicated to advancing the use of machine learning and deep learning techniques in the realm of Brain-Computer Interface (BCI). It focuses on analyzing EEG data obtained from IDUN Guardian Earbuds. Electroencephalography (EEG) is a non-invasive method of recording electrical activity in the brain. Its high-resolution, real-time data is crucial in various clinical and consumer applications. In clinical environments, EEG is instrumental in diagnosing and monitoring neurological disorders like epilepsy, sleep disorders, and brain injuries. It's also used for assessing brain function in patients under anesthesia or in comas. The real-time aspect of EEG data is vital for clinicians to make informed decisions about diagnosis and treatment, such as pinpointing the onset and location of a seizure. Beyond clinical use, EEG has significant applications in understanding human cognition. Researchers utilize EEG to explore cognitive processes including attention, percepti...","https://miniodis-rproxy.lisn.upsaclay.fr/coda-v2-prod-public/logos/2023-12-02-1701542051/06a6dc054e4b/NTXHackathon23-Logo-Black-Blue-2048.png","https://www.codabench.org/competitions/1777/","completed","10","","2023-12-01","2023-12-15","2023-12-12 23:22:24","2023-12-12 23:30:24" -"482","ai2healhackathon","UF AI2Heal 2024","Rebooting medicine with next-gen artificial intelligence","AI2Heal 2024 aims to showcase innovative applications of AI and other emerging technologies, including but not limited to augmented, virtual, and other forms of extended reality (AR/VR/XR), digital twins, and internet of things (IOT), that highlight the potential of digital health solutions and next-generation AI-enabled healthcare to improve patient outcomes and address inefficiencies in the healthcare system. The AI2Heal 2024 Challenges fall under three primary Domains: 1) Building the Health Metaverse; 2) Smart Hospitals and Remote Healthcare Delivery; and 3) Intelligent Cognitive Support","https://d112y698adiu2z.cloudfront.net/photos/production/challenge_photos/002/704/686/datas/full_width.png","https://ai2healhackathon.devpost.com/","active","20","","2024-01-06","2024-02-24","2024-01-09 16:48:56","2024-01-31 22:52:19" -"483","data-hacks","Data Hacks: Equity in Healthcare","DataHacks with women in data science for middle and high schoolers","Hosted by CP Mentorship, 7EDU & Leadways School, Sponsored by WiDS - Women in Data Science & Microsoft, this one full day event is to support the worldwide Datathon competition, led by experts in data science fields. Winners will receive awards from Kaggle. Eligibility: middle to high schoolers (6th - 12th grade)","https://d112y698adiu2z.cloudfront.net/photos/production/challenge_photos/002/707/384/datas/full_width.png","https://data-hacks.devpost.com/","completed","20","","2024-01-26","2024-01-27","2024-01-09 16:58:53","2024-01-11 23:46:37" -"484","mchacksnsbe","McHacks'24: Mario's Odyssey of Innovation","Spark change: innovate for mental health, education, feeding, and transport","Join our dynamic hackathon open to all students, regardless of major. This platform is designed to showcase your critical thinking and innovative prowess, enabling you to craft remarkable designs and comprehensive solutions for real-world challenges. Experience the power of collaboration as you team up with diverse talents, fostering an environment that promotes creative problem-solving. Beyond honing your skills, this hackathon serves as an ideal opportunity to engage with recruiters and companies, boosting your visibility within the workforce. Seize this avenue to shine, connect, and create a lasting impact. Your journey from idea to implementation starts here.","https://d112y698adiu2z.cloudfront.net/photos/production/challenge_photos/002/602/756/datas/full_width.jpg","https://mchacksnsbe.devpost.com/","completed","20","","2024-02-10","2024-02-10","2024-01-09 18:36:27","2024-01-11 23:46:46" -"485","ml-hacks-19785","ML Hacks","Create AI/ML applications to revolutionize the fields of science, art, and more","Machine learning/AI are tools that can be applied to almost every domain, from communications to health. In this hackathon, we want you, the participant, to find unqiue and inspiring ways to leverage machine learning & AI to make projects in these domains, and in doing so, change the world for the better.","","https://ml-hacks-19785.devpost.com/","upcoming","20","","2024-02-16","2024-02-25","2024-01-09 18:45:21","2024-01-09 18:47:51" -"486","frosthacks","Frost Hacks","Embark on a winter innovation odyssey","Welcome to Frost Hacks, Hyderabad's first 30-hour hackathon in collaboration with Major League Hacking (MLH) - an International Student League. Our mission is to cultivate a dynamic and collaborative environment students can immerse themselves in project-based learning, all while having an unforgettable time.","https://d112y698adiu2z.cloudfront.net/photos/production/challenge_photos/002/675/245/datas/full_width.png","https://frosthacks.devpost.com/","completed","20","","2024-01-20","2024-01-21","2024-01-09 18:48:59","2024-01-11 23:46:58" -"487","ncit-hackfest-2024","NCIT Hackfest 2024","Lethargy breaking initiative driven to energize and engage NCIT students","HackFest 2024 is the intitiative from NOSK, Nepal Open Source Klub. HackFest NCIT 2024 is 72 hour long running hackathon will be held on January 11-13 , Hack Fest 2024 is the lethargy breaking initiative driven to energize and engage NCIT to take active role and participation in the Tech domain through innovative and competitive events, competitions, workshops and techno centric activities. We call for hackers, artists, coders, designers, tech evangelists, creatives, and developers from all disciplines to take a part to win awesome prizes and collaborate with other developers .","https://d112y698adiu2z.cloudfront.net/photos/production/challenge_photos/002/707/493/datas/full_width.png","https://ncit-hackfest-2024.devpost.com/","completed","20","","2024-01-11","2024-01-13","2024-01-09 18:51:10","2024-01-11 23:47:12" -"488","spot-the-mask","Spot the Mask Challenge","Can you predict whether a person in an image is wearing a face mask?","Face masks have become a common public sight in the last few months. The Centers for Disease Control (CDC) recently advised the use of simple cloth face coverings to slow the spread of the virus and help people who may have the virus and do not know it from transmitting it to others. Wearing masks is broadly recognised as critical to reducing community transmission and limiting touching of the face. In a time of concern about slowing the transmission of COVID-19, increased surveillance combined with AI solutions can improve monitoring and reduce the human effort needed to limit the spread of this disease. The objective of this challenge is to create an image classification machine learning model to accurately predict the likelihood that an image contains a person wearing a face mask, or not. The total dataset contains 1,800+ images of people either wearing masks or not. Your machine learning solution will help policymakers, law enforcement, hospitals, and even commercial busines...","","https://zindi.africa/competitions/spot-the-mask","active","21","","\N","\N","2024-01-09 18:59:10","2024-01-09 19:08:11" -"489","to-vaccinate-or-not-to-vaccinate","To Vaccinate or Not to Vaccinate: It’s not a Question","Analysing social media sentiment towards vaccines","Work has already begun towards developing a COVID-19 vaccine. From measles to the common flu, vaccines have lowered the risk of illness and death, and have saved countless lives around the world. Unfortunately in some countries, the 'anti-vaxxer' movement has led to lower rates of vaccination and new outbreaks of old diseases. Although it may be many months before we see COVID-19 vaccines available on a global scale, it is important to monitor public sentiment towards vaccinations now and especially in the future when COVID-19 vaccines are offered to the public. The anti-vaccination sentiment could pose a serious threat to the global efforts to get COVID-19 under control in the long term. The objective of this challenge is to develop a machine learning model to assess if a Twitter post related to vaccinations is positive, neutral, or negative. This solution could help governments and other public health actors monitor public sentiment towards COVID-19 vaccinations and help impro...","","https://zindi.africa/competitions/to-vaccinate-or-not-to-vaccinate","active","21","","\N","\N","2024-01-09 19:02:43","2024-01-09 19:08:08" -"490","computer-vision-for-image-classification","Computer Vision for Image Classification","Learning competition for CMU Data Science Club and AI Saturdays Kigali in Rwanda","This challenge was designed by Carnegie Mellon University Africa Data Science Club in Rwanda in partnership with AI Saturdays Kigali, specifically for the students of the Carnegie Mellon University and general AI saturdays Kigali community with a modified dataset from UCI machine Learning repository, which takes place between 16 February - 18 May. Welcome to the CMU students and AI Saturdays Kigali members! CMU Africa Data science club has made this competition open to the Zindi community to learn and test their skills. Anyone is welcome to enter this 'knowledge' challenge.","","https://zindi.africa/competitions/computer-vision-for-image-classification","active","21","","\N","\N","2024-01-09 19:08:58","2024-01-09 19:10:01" -"491","space-medicine","Astropreneurship and Space Medicine","Hacking space issues with a focus on health","This is an event devoted to investigating space problems in healthcare! Due to COVID-19, this hackathon is mostly online, with the exception of the final presentatioN and prize ceremony which is being held at the Harvard Innovation Lab at Harvard Business School.","","https://space-medicine.devpost.com/","completed","20","","2024-01-11","2024-01-17","2024-01-11 23:04:48","2024-01-11 23:07:06" -"492","biomarkers-of-aging-challenge","Biomarkers of Aging Challenge","Systematic evaluation and validation of biomarkers of aging","#bioage Systematic evaluation and validation of biomarkers of aging remains a significant challenge, yet these are essential prerequisites for their ultimate use in clinical trials for longevity interventions. Access to high quality omics datasets and disparate biomarker formulations are key roadblocks to reaching this goal. Moreover, there are disconnects between programming languages and methodologies favored by computational biologists and data scientists, which hinders the formation of transformative collaborations between such researchers. To enable innovation of the next generation of biomarkers of aging, we have built an open-source toolset called Biolearn. Biolearn is an unprecedented, one-stop, open-source platform for evaluation and validation of biomarkers of aging by curating and harmonizing large, high-quality omics and health datasets. We have built-in the ability to simultaneously calculate all currently available biomarkers of aging by harmonizing their formulatio...","banner/boac.png","https://www.synapse.org/#!Synapse:syn52966292/wiki/","active","1","","2023-12-04","\N","2024-01-29 17:48:58","2024-02-05 16:58:24" -"493","pairboneage22","Project AIR - commercial AI for bone age prediction on hand XR","Bone age prediction on hand radiographs on a multicenter dataset","Head-to-head performance evaluation of commercially available AI products. This challenge shows the results for bone age prediction on hand radiographs on a multicenter dataset (seven centers) from the Netherlands.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/715/bone-age-hand-xr-logo.png","https://pairboneage22.grand-challenge.org/","completed","5","","\N","2024-01-08","2024-01-31 22:49:24","2024-02-05 16:56:59" -"494","pairlungnodulexr22","Project AIR - commercial AI for lung nodule detection on CXR","Lung nodule detection on chest radiographs on a multicenter dataset","Head-to-head performance evaluation of commercially available AI products. This challenge shows the results for lung nodule detection on chest radiographs on a multicenter dataset (seven centers) from the Netherlands.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/741/lung-nodules-cxr-logo.png","https://pairlungnodulexr22.grand-challenge.org/","completed","5","","\N","2024-01-08","2024-01-31 22:49:27","2024-02-05 16:57:02" -"495","justraigs","Justified Referral in AI Glaucoma Screening","AI-based screening for glaucoma","This challenge builds upon the success of AIROGS, that we organized in the context of ISBI 2022. This time, we ask participants to not only classify fundus images of eyes as referable glaucoma, but also to identify up to 10 different glaucomatous features in these images. The data set contains more than 110.000 carefully labeled fundus images. Participants can win up to € 3000 in this challenge!","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/749/Logo_XlR2vwf.png","https://justraigs.grand-challenge.org/","active","5","","2024-01-08","2024-04-21","2024-01-31 22:49:29","2024-02-05 16:57:42" -"496","lightmycells","Light My Cells: Bright Field to Fluorescence Imaging Challenge","Enhance biology and microscopy","Join the Light My Cells France-Bioimaging challenge! Enhance biology and microscopy by contributing to the development of new image-to-image deep labelling methods. The task: predict the best-focused output images of several fluorescently labelled organelles from label-free transmitted light input images. Dive into the future of imaging with us! #LightMyCellsChallenge","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/750/logo_light_my_cells.png","https://lightmycells.grand-challenge.org/","upcoming","5","","\N","\N","2024-01-31 22:49:33","2024-02-05 16:58:06" -"497","hack-rare-disease","Harvard Rare Disease Hackathon 2024","Are you a student interested in using AI/ML to tackle rare diseases? Join us!","This March 2-3, join us for the 2024 Harvard Rare Disease Hackathon, where students will gather on Harvard’s campus to set forth their own data-driven solutions for rare diseases. Participants will have the chance to analyze public and patient-sourced genomic and clinical datasets, and will be challenged to produce deliverables for participating patient organizations. These deliverables may take the form of a data report, computational tool, or web/mobile application that improves the lives of patients or furthers research progress. Participation is free and open to all undergraduate and graduate students who register with their .edu email address.","","https://www.harvard-rarediseases.org/","upcoming","\N","","2024-03-02","2024-03-03","2024-02-06 00:12:34","2024-02-06 0:41:24" -"498","dreaming","Diminished Reality for Emerging Applications in Medicine through Inpainting","","The Diminished Reality for Emerging Applications in Medicine through Inpainting (DREAMING) challenge seeks to pioneer the integration of Diminished Reality (DR) into oral and maxillofacial surgery. While Augmented Reality (AR) has been extensively explored in medicine, DR remains largely uncharted territory. DR involves virtually removing real objects from the environment by replacing them with their background. Recent inpainting methods present an opportunity for real-time DR applications without scene knowledge. DREAMING focuses on implementing such methods to fill obscured regions in surgery scenes with realistic backgrounds, emphasizing the complex facial anatomy and patient diversity. The challenge provides a dataset of synthetic yet photorealistic surgery scenes featuring humans, simulating an operating room setting. Participants are tasked with developing algorithms that seamlessly remove disruptions caused by medical instruments and hands, offering surgeons an unimpeded ...","https://rumc-gcorg-p-public.s3.amazonaws.com/b/752/isbi_dreaming_banner_gc_297CU3H.x10.jpeg","https://dreaming.grand-challenge.org/","active","5","","2024-01-08","2024-04-27","2024-02-12 21:56:27","2024-02-12 22:00:06" +"id","slug","name","headline","description","avatar_url","website_url","status","platform","doi","operation","start_date","end_date","created_at","updated_at" +"1","network-topology-and-parameter-inference","Network Topology and Parameter Inference","Optimize methods to estimate biology model parameters","Participants are asked to develop and/or apply optimization methods, including the selection of the most informative experiments, to accurately estimate parameters and predict outcomes of perturbations in Systems Biology models.","","https://www.synapse.org/#!Synapse:syn2821735","completed","1","","","2012-06-01","2012-10-01","2023-11-15 22:40:15","2023-11-16 18:31:42" +"2","breast-cancer-prognosis","Breast Cancer Prognosis","Predict breast cancer survival from clinical and genomic data","The goal of the breast cancer prognosis Challenge is to assess the accuracy of computational models designed to predict breast cancer survival, based on clinical information about the patient's tumor as well as genome-wide molecular profiling data including gene expression and copy number profiles.","","https://www.synapse.org/#!Synapse:syn2813426","completed","1","","","2012-07-12","2012-10-15","2023-11-14 20:36:32","2023-11-14 20:17:33" +"3","phil-bowen-als-prediction-prize4life","Phil Bowen ALS Prediction Prize4Life","Seeking treatment to halt ALS's fatal loss of motor function","Amyotrophic Lateral Sclerosis (ALS), or Lou Gehrig's disease, is a fatal neurological condition causing the death of nerve cells in the brain and spinal cord, resulting in a progressive loss of motor function while cognitive functions persist. Typically emerging around age 50, it affects about five in 100,000 people worldwide, with familial hereditary forms as the only known risk factors (5-10% of cases). There is currently no cure for ALS. The FDA-approved drug Riluzole extends life by a few months. ALS patients, on average, have a life expectancy of 2-5 years, with 10% experiencing slower disease progression. Astrophysicist Stephen Hawking, living with ALS for 49 years, is an exceptional case. The DREAM-Phil Bowen ALS Prediction Prize4Life, or ""ALS Prediction Prize,"" utilizes the PRO-ACT database with clinical data from over 7,500 ALS patients. This collaboration with DREAM aims to expedite ALS treatment discovery. Prize4Life, a non-profit, collaborates with NEALS and ALS Ther...","","https://www.synapse.org/#!Synapse:syn2826267","completed","1","","","2012-06-01","2012-10-01","2023-11-01 22:09:02","2023-11-13 17:16:16" +"4","drug-sensitivity-and-drug-synergy-prediction","Drug Sensitivity and Drug Synergy Prediction","Predicting drug sensitivity in human cell lines","Development of new cancer therapeutics currently requires a long and protracted process of experimentation and testing. Human cancer cell lines represent a good model to help identify associations between molecular subtypes, pathways, and drug response. In recent years there have been several efforts to generate genomic profiles of collections of cell lines and to determine their response to panels of candidate therapeutic compounds. These data provide the basis for the development of in silico models of sensitivity based either on the unperturbed genetic potential of a cancer cell, or by using perturbation data to incorporate knowledge of actual cell response. Making predictions from either of these data profiles will be beneficial in identifying single and combinatorial chemotherapeutic response in patients. To that end, the present challenge seeks computational methods, derived from the molecular profiling of cell lines both in a static state and in response to perturbation of ...","","https://www.synapse.org/#!Synapse:syn2785778","completed","1","","operation_3207","2012-06-01","2012-10-01","2023-11-01 22:08:36","2023-11-16 17:58:39" +"5","niehs-ncats-unc-toxicogenetics","NIEHS-NCATS-UNC Toxicogenetics","Predicting cytotoxicity from genomic and chemical data","This challenge is designed to build predictive models of cytotoxicity as mediated by exposure to environmental toxicants and drugs. To approach this question, we will provide a dataset containing cytotoxicity estimates as measured in lymphoblastoid cell lines derived from 884 individuals following in vitro exposure to 156 chemical compounds. In subchallenge 1, participants will be asked to model interindividual variability in cytotoxicity based on genomic profiles in order to predict cytotoxicity in unknown individuals. In subchallenge 2, participants will be asked to predict population-level parameters of cytotoxicity across chemicals based on structural attributes of compounds in order to predict median cytotoxicity and mean variance in toxicity for unknown compounds.","","https://www.synapse.org/#!Synapse:syn1761567","completed","1","","","2013-06-10","2013-09-15","2023-11-01 22:08:45","2023-11-01 22:06:01" +"6","whole-cell-parameter-estimation","Whole-Cell Parameter Estimation","Seeking innovative parameter estimation methods for large models","The goal of this challenge is to explore and compare innovative approaches to parameter estimation of large, heterogeneous computational models. Participants are encouraged to develop and/or apply optimization methods, including the selection of the most informative experiments. The organizers encourage participants to form teams to collaboratively solve the challenge.","","https://www.synapse.org/#!Synapse:syn1876068","completed","1","","","2013-06-10","2013-09-23","2023-06-23 00:00:00","2023-11-01 22:06:23" +"7","hpn-dream-breast-cancer-network-inference","HPN-DREAM Breast Cancer Network Inference","Inferring causal signaling networks in breast cancer","The overall goal of the Heritage-DREAM breast cancer network inference challenge is to quickly and effectively advance our ability to infer causal signaling networks and predict protein phosphorylation dynamics in cancer. We provide extensive training data from experiments on four breast cancer cell lines stimulated with various ligands. The data comprise protein abundance time-courses under inhibitor perturbations.","","https://www.synapse.org/#!Synapse:syn1720047","completed","1","","","2013-06-10","2013-09-16","2023-06-23 00:00:00","2023-11-13 17:15:59" +"8","rheumatoid-arthritis-responder","Rheumatoid Arthritis Responder","Unlocking Anti-TNF response predictors in RA therapy","The goal of this project is to use a crowd-based competition framework to develop a validated molecular predictor of anti-TNF response in RA. There is an increasing need for predictors of response to therapy in inflammatory disease driven by the observation that most clinically defined diseases show variable response and the growing availability of alternative therapies. Anti-TNF drugs in Rheumatoid Arthritis represent a prototypical example of this opportunity. A number of studies have tried, over the past decade, to develop a robust predictor of response. We believe the time is right to try a different approach to developing such a biomarker with a crowd-sourced collaborative competition. This is based on DREAM and Sage Bionetworks' experience with running competitions and the availability of new unpublished large-scale data relating to RA treatment response.THIS CHALLENGE RAN FROM FEBRUARY TO OCTOBER 2014 AND IS NOW CLOSED.","","https://www.synapse.org/#!Synapse:syn1734172","completed","1","","","2014-02-10","2014-06-04","2023-06-23 00:00:00","2023-11-15 22:42:17" +"9","icgc-tcga-dream-mutation-calling","ICGC-TCGA DREAM Mutation Calling","Crowdsourcing challenge to improve cancer mutation detection","The ICGC-TCGA DREAM Genomic Mutation Calling Challenge (herein, The Challenge) is an international effort to improve standard methods for identifying cancer-associated mutations and rearrangements in whole-genome sequencing (WGS) data. Leaders of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cancer genomics projects are joining with Sage Bionetworks and IBM-DREAM to initiate this innovative open crowd-sourced Challenge [1-3].","","https://www.synapse.org/#!Synapse:syn312572","completed","1","","","2013-12-14","2016-04-22","2023-06-23 00:00:00","2023-10-14 05:38:15" +"10","acute-myeloid-leukemia-outcome-prediction","Acute Myeloid Leukemia Outcome Prediction","Uncover drivers of AML using clinical and proteomic data","The AML Outcome Prediction Challenge provides a unique opportunity to access and interpret a rich dataset for AML patients that includes clinical covariates, select gene mutation status and proteomic data. Capitalizing on a unique AML reverse phase protein array (RPPA) dataset obtained at M.D. Anderson Cancer Center that captures 271 measurements for each patient, participants of the DREAM 9 Challenge will help uncover what drives AML. Outcomes of this Challenge have the potential to be used immediately to tailor therapies for newly diagnosed leukemia patients and to accelerate the development of new drugs for leukemia.","","https://www.synapse.org/#!Synapse:syn2455683","completed","1","","","2014-06-02","2014-09-15","2023-06-23 00:00:00","2023-10-14 05:38:16" +"11","broad-dream-gene-essentiality-prediction","Broad-DREAM Gene Essentiality Prediction","Crowdsourcing models to predict cancer cell gene dependencies","The goal of this project is to use a crowd-based competition to develop predictive models that can infer gene dependency scores in cancer cells (genes that are essential to cancer cell viability when suppressed) using features of those cell lines. An additional goal is to find a small set of biomarkers (gene expression, copy number, and mutation features) that can best predict a single gene or set of genes.","","https://www.synapse.org/#!Synapse:syn2384331","completed","1","","","2014-06-02","2014-09-29","2023-06-23 00:00:00","2023-10-14 05:38:16" +"12","alzheimers-disease-big-data","Alzheimer's Disease Big Data","Seeking accurate predictive biomarkers","The goal of the Alzheimer's Disease Big Data DREAM Challenge #1 (AD#1) was to apply an open science approach to rapidly identify accurate predictive AD biomarkers that can be used by the scientific, industrial and regulatory communities to improve AD diagnosis and treatment. AD#1 will be the first in a series of AD Data Challenges to leverage genetics and brain imaging in combination with cognitive assessments, biomarkers and demographic information from cohorts ranging from cognitively normal to mild cognitively impaired to individuals with AD.","","https://www.synapse.org/#!Synapse:syn2290704","completed","1","","","2014-06-02","2014-10-17","2023-06-23 00:00:00","2023-10-14 05:38:17" +"13","olfaction-prediction","Olfaction Prediction","Predicting smell from molecule features","The goal of the DREAM Olfaction Prediction Challenge is to find models that can predict how a molecule smells from its physical and chemical features. A model that allows us to predict a smell from a molecule will provide fundamental insights into how odor chemicals are transformed into a smell percept in the brain. Further, being able to predict how a chemical smells will greatly accelerate the design of new molecules to be used as fragrances. Currently, fragrance chemists synthesize many molecules to obtain a new ingredient, but most of these will not have the desired qualities.","","https://www.synapse.org/#!Synapse:syn2811262","completed","1","","","2015-01-15","2015-05-01","2023-11-01 22:11:08","2023-10-14 05:38:17" +"14","prostate-cancer","Prostate Cancer","Predict survival of docetaxel treatment in mCRPC patients","This challenge will attempt to improve the prediction of survival and toxicity of docetaxel treatment in patients with metastatic castration-resistant prostate cancer (mCRPC). The primary benefit of this Challenge will be to establish new quantitative benchmarks for prognostic modeling in mCRPC, with a potential impact for clinical decision making and ultimately understanding the mechanism of disease progression. Participating teams will be asked to submit predictive models based on clinical variables from the comparator arms of four phase III clinical trials with over 2,000 mCRPC patients treated with first-line docetaxel. The comparator arm of a clinical trial represents the patients that receive a treatment that is considered to be effective. This arm of the clinical trial is used to evaluate the effectiveness of the new therapy being tested.","","https://www.synapse.org/#!Synapse:syn2813558","completed","1","","","2015-03-16","2015-07-27","2023-06-23 00:00:00","2023-10-14 05:38:18" +"15","als-stratification-prize4life","ALS Stratification Prize4Life","Predicting ALS progression and survival with data","As illustrated by the overview figure below, (a) Challenge Data includes data from ALS clinical trials and ALS registries. ALS clinical trials consist of patients from clinical trials available open access on the PRO-ACT database and patients from 6 clinical trials not yet added into the database. Data from ALS registries was collected from patients in national ALS registries. (b) Data is divided into three subsets-training data provided to solvers in full, leaderboard, and validation data that is available only to the organizers and is reserved for the scoring of the challenge. (c) The goal of this challenge is then to predict the Clinical Targets, i.e. the disease progression as ALSFRS slope as well as survival. (d) For Building the Models, participants create two algorithms-one that selects features and one that predicts outcomes. To perform predictions, data from a given patient is fed into the selector . The selector selects 6 features and a cluster/model ID (3), e.g. from a...","","https://www.synapse.org/#!Synapse:syn2873386","completed","1","","","2015-06-22","2015-10-04","2023-06-23 00:00:00","2023-10-14 05:38:19" +"16","astrazeneca-sanger-drug-combination-prediction","AstraZeneca-Sanger Drug Combination Prediction","Predict effective drug combinations using genomic data","To accelerate the understanding of drug synergy, AstraZeneca has partnered with the European Bioinformatic Institute, the Sanger Institute, Sage Bionetworks, and the distributed DREAM community to launch the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. This Challenge is designed to explore fundamental traits that underlie effective combination treatments and synergistic drug behavior using baseline genomic data, i.e. data collected pretreatment. As the basis of the Challenge, AstraZeneca is releasing ~11.5k experimentally tested drug combinations measuring cell viability over 118 drugs and 85 cancer cell lines (primarily colon, lung, and breast), and monotherapy drug response data for each drug and cell line. Moreover, in coordination with the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Sanger Institute, genomic data including gene expression, mutations (whole exome), copy-number alterations, and methylation data will be released into the publ...","","https://www.synapse.org/#!Synapse:syn4231880","completed","1","","","2015-09-03","2016-03-14","2023-06-23 00:00:00","2023-10-14 05:38:19" +"17","smc-dna-meta","SMC-DNA Meta","Seeking most accurate somatic mutation detection pipeline","The goal of this Challenge is to identify the most accurate meta-pipeline for somatic mutation detection, and establish the state-of-the-art. The algorithms in this Challenge must use as input mutations predicted by one or more variant callers and output mutation calls associated with cancer. An additional goal is to highlight the complementarity of the calling algorithms and help understand their individual advantages/deficiencies.","","https://www.synapse.org/#!Synapse:syn4588939","completed","1","","","2015-08-17","2016-04-10","2023-06-23 00:00:00","2023-10-14 05:38:20" +"18","smc-het","SMC-Het","Crowdsourcing challenge to improve tumor subclonal reconstruction","The ICGC-TCGA DREAM Somatic Mutation Calling-Tumour Heterogeneity Challenge (SMC-Het) is an international effort to improve standard methods for subclonal reconstruction-to quantify and genotype each individual cell population present within a tumor. Leaders of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cancer genomics projects are joining with Sage Bionetworks and IBM-DREAM to initiate this innovative open crowd-sourced Challenge [1-3].","","https://www.synapse.org/#!Synapse:syn2813581","completed","1","","","2015-11-16","2016-06-30","2023-11-01 22:21:29","2023-10-14 05:38:21" +"19","respiratory-viral","Respiratory Viral","Early predictors of respiratory infection and contagiousness","Respiratory viruses are highly infectious and cause acute illness in millions of people every year. However, there is wide variation in the physiologic response to exposure at the individual level. Some people that are exposed to virus are able to completely avoid infection. Others contract virus but are able to fight it off without exhibiting any symptoms of illness such as coughing, sneezing, sore throat or fever. It is not well understood what characteristics may protect individuals from respiratory viral infection. These individual responses are likely influenced by multiple processes including both the basal state of the human host upon exposure and the dynamics of host immune response in the early hours immediately following exposure. Many of these processes play out in the peripheral blood through activation and recruitment of circulating immune cells. Global gene expression patterns measured in peripheral blood at the time of symptom onset-several days after viral exposure...","","https://www.synapse.org/#!Synapse:syn5647810","completed","1","","","2016-05-16","2016-09-28","2023-06-23 00:00:00","2023-11-14 20:16:42" +"20","disease-module-identification","Disease Module Identification","Crowdsourcing challenge to find disease modules in genomic networks","The Disease Module Identification DREAM Challenge is an open community effort to systematically assess module identification methods on a panel of state-of-the-art genomic networks and leverage the “wisdom of crowds” to discover novel modules and pathways underlying complex diseases.","","https://www.synapse.org/#!Synapse:syn6156761","completed","1","https://doi.org/10.1038/s41592-019-0509-5","","2016-06-24","2016-10-01","2023-11-01 22:21:32","2023-10-16 21:17:48" +"21","encode","ENCODE","Predict transcription factor binding sites from limited data","Transcription factors (TFs) are regulatory proteins that bind specific DNA sequence patterns (motifs) in the genome and affect transcription rates of target genes. Binding sites of TFs differ across cell types and experimental conditions. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an experimental method that is commonly used to obtain the genome-wide binding profile of a TF of interest in a specific cell type/condition. However, profiling the binding landscape of every TF in every cell type/condition is infeasible due to constraints on cost, material and effort. Hence, accurate computational prediction of in vivo TF binding sites is critical to complement experimental results.","","https://www.synapse.org/#!Synapse:syn6131484","completed","1","","","2016-07-07","2017-01-11","2023-11-01 22:21:32","2023-10-14 05:38:26" +"22","idea","Idea","Fostering collaborative solutions in health: the DREAM IDEA challenge","The DREAM Idea Challenge is designed to collaboratively shape and enable the solution of a question fundamental to improving human health. In the process, all proposals and their evaluation will be made publicly available for the explicit purpose of connecting modelers and experimentalists who want to address the same question. This Wall of Models will enable new collaborations, and help turn every good modeling idea into a success story. It will further serve as a basis for new DREAM challenges.","","https://www.synapse.org/#!Synapse:syn5659209","completed","1","","","2016-06-15","2017-04-30","2023-06-23 00:00:00","2023-11-20 20:18:36" +"23","smc-rna","SMC-RNA","Crowdsourcing challenge to improve cancer mutation detection from rna data","The ICGC-TCGA DREAM Somatic Mutation Calling-RNA Challenge (SMC-RNA) is an international effort to improve standard methods for identifying cancer-associated rearrangements in RNA sequencing (RNA-seq) data. Leaders of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cancer genomics projects are joining with Sage Bionetworks and IBM-DREAM to initiate this innovative open crowd-sourced Challenge [1-3].","","https://www.synapse.org/#!Synapse:syn2813589","completed","1","","","2016-06-29","2017-05-02","2023-06-23 00:00:00","2023-10-14 05:38:29" +"24","digital-mammography-dream-challenge","Digital Mammography DREAM Challenge","Improve mammography prediction to detect breast cancer early","The Digital Mammography DREAM Challenge will attempt to improve the predictive accuracy of digital mammography for the early detection of breast cancer. The primary benefit of this Challenge will be to establish new quantitative tools-machine learning, deep learning or other-that can help decrease the recall rate of screening mammography, with a potential impact on shifting the balance of routine breast cancer screening towards more benefit and less harm. Participating teams will be asked to submit predictive models based on over 640,000 de-identified digital mammography images from over 86000 subjects, with corresponding clinical variables.","","https://www.synapse.org/#!Synapse:syn4224222","completed","1","https://doi.org/10.1001/jamanetworkopen.2020.0265","","2016-11-18","2017-05-16","2023-06-23 00:00:00","2023-10-14 05:38:29" +"25","multiple-myeloma","Multiple Myeloma","Develop precise risk model for myeloma patients","Multiple myeloma (MM) is a cancer of the plasma cells in the bone marrow, with about 25,000 newly diagnosed patients per year in the United States alone. The disease's clinical course depends on a complex interplay of clinical traits and molecular characteristics of the plasma cells.1 Since risk-adapted therapy is becoming standard of care, there is an urgent need for a precise risk stratification model to assist in therapeutic decision-making and research. While progress has been made, there remains a significant opportunity to improve patient stratification to optimize treatment and to develop new therapies for high-risk patients. A DREAM Challenge represents a chance not only to integrate available data and analytical approaches to tackle this important problem, but also provides the ability to benchmark potential methods to identify those with the greatest potential to yield patient care benefits in the future.","","https://www.synapse.org/#!Synapse:syn6187098","completed","1","","","2017-06-30","2017-11-08","2023-06-23 00:00:00","2023-10-14 05:38:31" +"26","ga4gh-dream-workflow-execution","GA4GH-DREAM Workflow Execution","Develop technologies to enable distributed genomic data analysis","The highly distributed and disparate nature of genomic and clinical data generated around the world presents an enormous challenge for those scientists who wish to integrate and analyze these data. The sheer volume of data often exceeds the capacity for storage at any one site and prohibits the efficient transfer between sites. To address this challenge, researchers must bring their computation to the data. Numerous groups are now developing technologies and best practice methodologies for running portable and reproducible genomic analysis pipelines as well as tools and APIs for discovering genomic analysis resources. Software development, deployment, and sharing efforts in these groups commonly rely on the use of modular workflow pipelines and virtualization based on Docker containers and related tools.","","https://www.synapse.org/#!Synapse:syn8507133","completed","1","","","2017-07-21","2017-12-31","2023-06-23 00:00:00","2023-10-14 05:38:31" +"27","parkinsons-disease-digital-biomarker","Parkinson's Disease Digital Biomarker","Develop Parkinson's digital signatures from sensor data for Parkinson's disease","The Parkinson's Disease Digital Biomarker DREAM Challenge is a first of it's kind challenge, designed to benchmark methods for the processing of sensor data for development of digital signatures reflective of Parkinson's Disease. Participants will be provided with raw sensor (accelerometer, gyroscope, and magnetometer) time series data recorded during the performance of pre-specified motor tasks, and will be asked to extract data features which are predictive of PD pathology. In contrast to traditional DREAM challenges, this one will focus on feature extraction rather than predictive modeling, and submissions will be evaluated based on their ability to predict disease phenotype using an array of standard machine learning algorithms.","","https://www.synapse.org/#!Synapse:syn8717496","completed","1","","","2017-07-06","2017-11-10","2023-06-23 00:00:00","2023-11-14 19:10:32" +"28","nci-cptac-proteogenomics","NCI-CPTAC Proteogenomics","Develop tools to extract insights from cancer proteomics data","Cancer is driven by aberrations in the genome [1,2], and these alterations manifest themselves largely in the changes in the structure and abundance of proteins, the main functional gene products. Hence, characterization and analyses of alterations in the proteome has the promise to shed light into cancer development and may improve development of both biomarkers and therapeutics. Measuring the proteome is very challenging, but recent rapid technology developments in mass spectrometry are enabling deep proteomics analysis [3]. Multiple initiatives have been launched to take advantage of this development to characterize the proteome of tumours, such as the Clinical Proteomic Tumor Analysis Consortium (CPTAC). These efforts hold the promise to revolutionize cancer research, but this will only be possible if the community develops computational tools powerful enough to extract the most information from the proteome, and to understand the association between genome, transcriptome and ...","","https://www.synapse.org/#!Synapse:syn8228304","completed","1","","","2017-06-26","2017-11-20","2023-11-01 22:21:37","2023-10-14 05:38:33" +"29","multi-targeting-drug","Multi-Targeting Drug","Seeking generalizable methods to predict multi-target compound binding","The objective of this challenge is to incentivize development of methods for predicting compounds that bind to multiple targets. In particular, methods that are generalizable to multiple prediction problems are sought. To achieve this, participants will be asked to predict 2 separate compounds, each having specific targets to which they should bind, and a list of anti-targets to avoid. Participants should use the same methods to produce answers for questions 1 and 2.","","https://www.synapse.org/#!Synapse:syn8404040","completed","1","","","2017-10-05","2018-02-26","2023-06-23 00:00:00","2023-10-14 05:38:33" +"30","single-cell-transcriptomics","Single Cell Transcriptomics","Reconstructing cell locations in Drosophila embryo from transcripts","In this Challenge on Single-Cell Transcriptomics, participants will reconstruct the location of single cells in the Drosophila embryo using single-cell transcriptomic data. Data will be made available in late August and participating challenge teams can work on the data and submit their results previous to the DREAM Conference. The best performers will be announced at the DREAM conference on Dec 8.","","https://www.synapse.org/#!Synapse:syn15665609","completed","1","","","2018-09-04","2018-11-21","2023-06-23 00:00:00","2023-11-16 18:38:46" +"31","idg-drug-kinase-binding","IDG Drug-Kinase Binding","Drug-kinase binding prediction for IDG drug-kinase binding","This IDG-DREAM Drug-Kinase Binding Prediction Challenge seeks to evaluate the power of statistical and machine learning models as a systematic and cost-effective means for catalyzing compound-target interaction mapping efforts by prioritizing most potent interactions for further experimental evaluation. The Challenge will focus on kinase inhibitors, due to their clinical importance [2], and will be implemented in a screening-based, pre-competitive drug discovery project in collaboration with theIlluminating the Druggable Genome (IDG) Kinase-focused Data and Resource Generation Center, consortium, with the aim to establish kinome-wide target profiles of small-molecule agents, with the goal of extending the druggability of the human kinome space.","","https://www.synapse.org/#!Synapse:syn15667962","completed","1","","","2018-10-01","2019-04-18","2023-06-23 00:00:00","2023-11-14 19:07:18" +"32","malaria","Malaria","Predict malaria drug resistance from parasite gene expression for malaria","The Malaria DREAM Challenge is open to anyone interested in contributing to the development of computational models that address important problems in advancing the fight against malaria. The overall goal of the first Malaria DREAM Challenge is to predict Artemisinin (Art) drug resistance level of a test set of malaria parasites using their in vitro transcription data and a training set consisting of published in vivo and unpublished in vitrotranscriptomes. The in vivodataset consists of ~1000 transcription samples from various geographic locations covering a wide range of life cycles and resistance levels, with other accompanying data such as patient age, geographic location, Art combination therapy used, etc [Mok et al (2015) Science]. The in vitro transcription dataset consists of 55 isolates, with transcription collected at two timepoints (6 and 24 hours post-invasion), in the absence or presence of an Art perturbation, for two biological replicates using a custom microarray a...","","https://www.synapse.org/#!Synapse:syn16924919","completed","1","","","2019-04-30","2019-08-15","2023-06-23 00:00:00","2023-10-14 05:38:35" +"33","preterm-birth-prediction-transcriptomics","Preterm Birth Prediction - Transcriptomics","Determine gestational age for preterm birth prediction","A basic need in pregnancy care is to establish gestational age, and inaccurate estimates may lead to unnecessary interventions and sub-optimal patient management. Current approaches to establish gestational age rely on patient's recollection of her last menstrual period and/or ultrasound, with the latter being not only costly but also less accurate if not performed during the first trimester of pregnancy. Therefore development of an inexpensive and accurate molecular clock of pregnancy would be of benefit to patients and health care systems. Participants in sub-challenge 1 (Prediction of gestational age) will be given whole blood gene topic_3170 collected from pregnant women to develop prediction models for the gestational age at blood draw. Another challenge in obstetrics, in both low and high-income countries, is identification and treatment of women at risk of developing the ‘great obstetrical syndromes‘. Of these, preterm birth (PTB), defined as giving birth prior to completio...","","https://www.synapse.org/#!Synapse:syn18380862","completed","1","","","2019-05-04","2019-12-05","2023-06-23 00:00:00","2023-11-14 19:07:28" +"34","single-cell-signaling-in-breast-cancer","Single-Cell Signaling in Breast Cancer","Exploring heterogeneous signaling in single cancer cells","Signaling underlines nearly every cellular event. Individual cells, even if genetically identical, respond to perturbation in different ways. This underscores the relevance of cellular heterogeneity, in particular in how cells respond to drugs. This is of high relevance since the fact that a subset of cells do not respond (or only weakly) to drugs can render this drug an ineffective treatment. In spite of its relevance to many diseases, comprehensive studies on the heterogeneous signaling in single cells are still lacking. We have generated the, to our knowledge, currently largest single cell signaling dataset on a panel of 67 well-characterized breast cancer cell lines by mass cytometry (3'015 conditions, ~80 mio single cells, 38 markers; Bandura et al. 2009; Bendall et al., 2011; Bodenmiller et al., 2012; Lun et al., 2017; Lun et al., 2019). These cell lines are, among others, also characterized at the genomic, transcriptomic, and proteomic level (Marcotte et al., 2016). We ask ...","","https://www.synapse.org/#!Synapse:syn20366914","completed","1","","","2018-08-20","2019-11-15","2023-06-23 00:00:00","2023-10-14 05:38:37" +"35","ehr-dream-challenge-patient-mortality-prediction","EHR DREAM Challenge: Patient Mortality Prediction","New tools to reconstruct cell lineages from CRISPR mutations","The recent advent of new CRISPR-based molecular tools allows the reconstruction of cell lineages based on the phylogenetical analysis of DNA mutations induced by CRISPR during development and promises to solve the lineage of complex model organisms at single-cell resolution (see image from McKenna et al Science 2016). To date, however, no lineage reconstruction algorithms have been rigorously examined for their performance/robustness across diverse molecular tools, datasets, and number of cells/size of lineage trees. It also remains unclear whether new Machine-Learning algorithms that go beyond the classical ones developed for reconstructing phylogenetic trees, could consistently reconstruct cell lineages to a high degree of accuracy. The challenge-a partnership between The Allen Institute and DREAM-will comprise 3 subchallenges that consist of reconstructing cell lineage trees of different sizes and nature. In subchallenge 1, participants will be given experimental molecular data...","","https://www.synapse.org/#!Synapse:syn18405991","completed","1","https://doi.org/10.1093/jamia/ocad159","operation_0231","2019-09-09","2020-01-23","2023-06-23 00:00:00","2023-11-02 18:25:23" +"36","allen-institute-cell-lineage-reconstruction","Allen Institute Cell Lineage Reconstruction","New tools enable reconstructing complex cell lineages at single-cell resolution","The recent advent of new CRISPR-based molecular tools allows the reconstruction of cell lineages based on the phylogenetical analysis of DNA mutations induced by CRISPR during development and promises to solve the lineage of complex model organisms at single-cell resolution. To date, however, no lineage reconstruction algorithms have been rigorously examined for their performance/robustness across diverse molecular tools, datasets, and number of cells/size of lineage trees. It also remains unclear whether new Machine-Learning algorithms that go beyond the classical ones developed for reconstructing phylogenetic trees, could consistently reconstruct cell lineages to a high degree of accuracy. The challenge-a partnership between The Allen Institute and DREAM-will comprise 3 subchallenges that consist of reconstructing cell lineage trees of different sizes and nature. In subchallenge 1, participants will be given experimental molecular data to reconstruct in vitro cell lineages of l...","","https://www.synapse.org/#!Synapse:syn20692755","completed","1","","operation_0231","2019-10-15","2020-02-06","2023-06-23 00:00:00","2023-11-02 18:25:24" +"37","tumor-deconvolution","Tumor Deconvolution","Deconvolve bulk tumor data into immune components","The extent of stromal and immune cell infiltration within solid tumors has prognostic and predictive significance. Unfortunately, expression profiling of tumors has, until very recently, largely been undertaken using bulk techniques (e.g., microarray and RNA-seq). Unlike single-cell methods (e.g., single-cell RNA-seq, FACS, mass cytometry, or immunohistochemistry), bulk approaches average expression across all cells (cancer, stromal, and immune) within the sample and, hence, do not directly quantitate tumor infiltration. This information can be recovered by computational tumor deconvolution methods, which would thus allow interrogation of immune subpopulations across the large collection of public bulk topic_3170sets. The goal of this Challenge is to evaluate the ability of computational methods to deconvolve bulk topic_3170, reflecting a mixture of cell types, into individual immune components. Methods will be assessed based on in vitro and in silico admixtures specifically gener...","","https://www.synapse.org/#!Synapse:syn15589870","completed","1","","","2019-06-26","2020-04-30","2023-06-23 00:00:00","2023-11-14 19:07:39" +"38","ctd2-pancancer-drug-activity","CTD2 Pancancer Drug Activity","Benchmark algorithms predicting drug targets from gene data","Over the last two years, the Columbia CTD2 Center developed PANACEA (Pancancer Analysis of Chemical Entity Activity), a comprehensive repertoire of dose response curves and molecular profiles representative of cellular responses to drug perturbations. PANACEA covers a broad spectrum of cellular contexts representative of poor outcome malignancies, including rare ones such as GIST sarcoma and gastroenteropancreatic neuroendocrine tumors (GEP-NETs). PANACEA is uniquely suited to support DREAM Challenges related to the elucidation of drug mechanism of action (MOA), drug sensitivity, and drug synergy. The goal of the CTD2 Pancancer Drug Activity DREAM Challenge is to foster the development and benchmarking of algorithms to predict targets of chemotherapeutic compounds from post-treatment transcriptional data.","","https://www.synapse.org/#!Synapse:syn20968331","completed","1","","","2019-12-02","2020-02-13","2023-06-23 00:00:00","2023-10-20 23:11:10" +"39","ctd2-beataml","CTD2 BeatAML","Seeking new drug targets for precision AML treatment","In the era of precision medicine, AML patients have few therapeutic options, with “7 + 3” induction chemotherapy having been the standard for decades (Bertoli et al. 2017). While several agents targeting the myeloid marker CD33 or alterations in FLT3 or IDH2 have demonstrated efficacy in patients (Wei and Tiong 2017), responses are uncertain in some populations (Castaigne et al. 2012) and relapse remains prevalent (Stone et al. 2017). These drugs highlight both the promise of targeted therapies in AML and the urgent need for additional treatment options that are tailored to more refined patient subpopulations in order to achieve durable responses. The BeatAML initiative was launched as a comprehensive study of the relationship between molecular alterations and ex-vivo drug sensitivity in patients with AML. One of the primary goals of this multi-center study was to develop a discovery cohort that could yield new drug target hypotheses and predictive biomarkers of therapeutic respon...","","https://www.synapse.org/#!Synapse:syn20940518","completed","1","","","2019-12-19","2020-04-28","2023-06-23 00:00:00","2023-10-14 05:38:42" +"40","metadata-automation","Metadata Automation","Semi-automating metadata annotation for enhanced data sharing in cancer research","The Cancer Research Data Commons (CRDC) will collate data across diverse groups of cancer researchers, each collecting biomedical data in different formats. This means the data must be retrospectively harmonized and transformed to enable this data to be submitted. In addition, to be findable by the broader scientific community, coherent information (metadata) is necessary about the data fields and values. Coherent metadata annotation of the data fields and their values can enable computational data transformation, query, and analysis. Creation of this type of descriptive metadata can require biomedical expertise to determine the best annotations and thus is a time-consuming and manual task which is both an obstacle and a bottleneck in data sharing and submissions. Goal-Using structured biomedical data files, challenge participants will develop tools to semi-automate annotation of metadata fields and values, using available research data annotations (e.g. caDSR CDEs) as well as es...","","https://www.synapse.org/#!Synapse:syn18065891","completed","1","","","2020-01-14","2020-06-02","2023-06-23 00:00:00","2023-10-14 05:38:42" +"41","automated-scoring-of-radiographic-joint-damage","Automated Scoring of Radiographic Joint Damage","Develop automated method to quantify rheumatoid arthritis joint damage","The purpose of the RA2-DREAM Challenge is to develop an automated method to quickly and accurately quantify the degree of joint damage associated with rheumatoid arthritis (RA). Based on radiographs of the hands and feet, a novel, automated scoring method could be applied broadly for patient care and research. We challenge participants to develop algorithms to automatically assess joint space narrowing and erosions using a large set of existing radiographs with damage scores generated by visual assessment of images by trained readers using standard protocols. The end result will be a generalizable, publicly available, automated method to generate accurate, reproducible and unbiased RA damage scores to replace the current tedious, expensive, and non-scalable method of scoring by human visual inspection.","","https://www.synapse.org/#!Synapse:syn20545111","completed","1","","","2019-11-04","2020-05-21","2023-06-23 00:00:00","2023-10-18 00:38:55" +"42","beat-pd","BEAT-PD","Develop mobile sensors to remotely monitor Parkinson's disease","Recent advances in mobile health have demonstrated great potential to leverage sensor-based technologies for quantitative, remote monitoring of health and disease-particularly for diseases affecting motor function such as Parkinson's disease. Such approaches have been rolled out using research-grade wearable sensors and, increasingly, through the use of smartphones and consumer wearables, such as smart watches and fitness trackers. These devices not only provide the ability to measure much more detailed disease phenotypes but also provide the ability to follow patients longitudinally with much higher frequency than is possible through clinical exams. However, the conversion of sensor-based data streams into digital biomarkers is complex and no methodological standards have yet evolved to guide this process. Parkinson's disease (PD) is a neurodegenerative disease that primarily affects the motor system but also exhibits other symptoms. Typical motor symptoms of the disease include...","","https://www.synapse.org/#!Synapse:syn20825169","completed","1","","","2020-01-13","2020-05-13","2023-06-23 00:00:00","2023-10-14 05:38:45" +"43","ctd2-pancancer-chemosensitivity","CTD2 Pancancer Chemosensitivity","Predict drug sensitivity from cell line gene expression","Over the last two years, the Columbia CTD2 Center developed PANACEA (Pancancer Analysis of Chemical Entity Activity), a comprehensive repertoire of dose response curves and molecular profiles representative of cellular responses to drug perturbations. PANACEA covers a broad spectrum of cellular contexts representative of poor outcome malignancies, including rare ones such as GIST sarcoma and gastroenteropancreatic neuroendocrine tumors (GEP-NETs). PANACEA is uniquely suited to support DREAM Challenges related to the elucidation of drug mechanism of action (MOA), drug sensitivity, and drug synergy. The goal of this Challenge is to foster development and benchmarking of algorithms to predict the sensitivity, as measured by the area under the dose-response curve, of a cell line to a compound based on the baseline transcriptional profiles of the cell line. The drug perturbational RNAseq profiles of 11 cell lines for 30 chosen compounds will be provided to challenge participants, with...","","https://www.synapse.org/#!Synapse:syn21763589","completed","1","","","2020-04-28","2020-07-27","2023-06-23 00:00:00","2023-10-14 05:38:45" +"44","ehr-dream-challenge-covid-19","EHR DREAM Challenge: COVID-19","Develop tools to predict COVID-19 risk without sharing data","The rapid rise of COVID-19 has challenged healthcare globally. The underlying risks and outcomes of infection are still incompletely characterized even as the world surpasses 4 million infections. Due to the importance and emergent need for better understanding of the condition and the development of patient specific clinical risk scores and early warning tools, we have developed a platform to support testing analytic and machine learning hypotheses on clinical data without data sharing as a platform to rapidly discover and implement approaches for care. We have previously applied this approach in the successful EHR DREAM Challenge focusing on Patient Mortality Prediction with UW Medicine. We have the goal of incorporating machine learning and predictive algorithms into clinical care and COVID-19 is an important and highly urgent challenge. In our first iteration, we will facilitate understanding risk factors that lead to a positive test utilizing electronic health recorded dat...","","https://www.synapse.org/#!Synapse:syn21849255","completed","1","https://doi.org/10.1001/jamanetworkopen.2021.24946","","2020-04-30","2021-07-01","2023-06-23 00:00:00","2023-11-01 14:57:29" +"45","anti-pd1-response-prediction","Anti-PD1 Response Prediction","Predicting lung cancer response to immuno-oncology therapy","While durable responses and prolonged survival have been demonstrated in some lung cancer patients treated with immuno-oncology (I-O) anti-PD-1 therapy, there remains a need to improve the ability to predict which patients are more likely to receive benefit from treatment with I-O. The goal of this challenge is to leverage clinical and biomarker data to develop predictive models of response to I-O therapy in lung cancer and ultimately gain insights that may facilitate potential novel monotherapies or combinations with I-O.","","https://www.synapse.org/#!Synapse:syn18404605","completed","1","","","2020-11-17","2021-02-25","2023-06-23 00:00:00","2023-11-02 18:25:16" +"46","brats-2021-challenge","BraTS 2021 Challenge","Developing ML methods to analyze brain tumor MRI scans","Glioblastoma, and diffuse astrocytic glioma with molecular features of glioblastoma (WHO Grade 4 astrocytoma), are the most common and aggressive malignant primary tumor of the central nervous system in adults, with extreme intrinsic heterogeneity in appearance, shape, and histology. Glioblastoma patients have very poor prognosis, and the current standard of care treatment comprises surgery, followed by radiotherapy and chemotherapy. The International Brain Tumor Segmentation (BraTS) Challenges —which have been running since 2012— assess state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans.","","https://www.synapse.org/#!Synapse:syn25829067","completed","1","","","2021-07-07","2021-10-15","2023-06-23 00:00:00","2023-10-14 05:38:48" +"47","cancer-data-registry-nlp","Cancer Data Registry NLP","Predicting lung cancer response to immuno-oncology therapy","A critical bottleneck in translational and clinical research is access to large volumes of high-quality clinical data. While structured data exist in medical EHR systems, a large portion of patient information including patient status, treatments, and outcomes is contained in unstructured text fields. Research in Natural Language Processing (NLP) aims to unlock this hidden and often inaccessible information. However, numerous challenges exist in developing and evaluating NLP methods, much of it centered on having “gold-standard” metrics for evaluation, and access to data that may contain personal health information (PHI). This DREAM Challenge will focus on the development and evaluation of of NLP algorithms that can improve clinical trial matching and recruitment.","","https://www.synapse.org/#!Synapse:syn18361217","upcoming","1","","","\N","\N","2023-06-23 00:00:00","2023-10-14 05:38:49" +"48","barda-community-challenge-pediatric-covid-19-data-challenge","BARDA Community Challenge - Pediatric COVID-19 Data Challenge","Models to predict severe COVID-19 in children sought","While most children with COVID-19 are asymptomatic or have mild symptoms, healthcare providers have difficulty determining which among their pediatric patients will progress to moderate or severe COVID-19 early in the progression. Some of these patients develop multisystem inflammatory syndrome in children (MIS-C), a life-threatening inflammation of organs and tissues. Methods to distinguish children at risk for severe COVID-19 complications, including conditions such as MIS-C, are needed for earlier interventions to improve pediatric patient outcomes. Multiple HHS divisions are coming together for a data challenge competition that will leverage de-identified electronic health record data to develop, train and validate computational models that can predict severe COVID-19 complications in children, equipping healthcare providers with the information and tools they need to identify pediatric patients at risk.","","https://www.synapse.org/#!Synapse:syn25875374/wiki/611225","completed","1","","","2021-08-19","2021-12-17","2023-06-23 00:00:00","2023-10-14 05:38:50" +"49","brats-continuous-evaluation","BraTS Continuous Evaluation","Seeking innovations to improve brain tumor diagnosis and treatment","Brain tumors are among the deadliest types of cancer. Specifically, glioblastoma, and diffuse astrocytic glioma with molecular features of glioblastoma (WHO Grade 4 astrocytoma), are the most common and aggressive malignant primary tumor of the central nervous system in adults, with extreme intrinsic heterogeneity in appearance, shape, and histology, with a median survival of approximately 15 months. Brain tumors in general are challenging to diagnose, hard to treat and inherently resistant to conventional therapy because of the challenges in delivering drugs to the brain, as well as the inherent high heterogeneity of these tumors in their radiographic, morphologic, and molecular landscapes. Years of extensive research to improve diagnosis, characterization, and treatment have decreased mortality rates in the U.S by 7% over the past 30 years. Although modest, these research innovations have not translated to improvements in survival for adults and children in low-and middle-income...","","https://www.synapse.org/brats_ce","completed","1","","","2022-01-01","\N","2023-06-23 00:00:00","2023-10-14 05:38:51" +"50","fets-2022","FeTS 2022","Federated Learning Challenge 2022: advancing brain tumor segmentation algorithms","FeTS 2022 focuses on benchmarking methods for federated learning (FL), and particularly i) weight aggregation methods for federated training, and ii) algorithmic generalizability on out-of-sample data based on federated evaluation. In line with its last instance (FeTS 2021-the 1st FL challenge ever organized), FeTS 2022 targets the task of brain tumor segmentation and builds upon i) the centralized dataset of >8,000 clinically-acquired multi-institutional MRI scans (from the RSNA-ASNR-MICCAI BraTS 2021 challenge) with their real-world partitioning, and ii) the collaborative network of remote independent institutions included in a real-world federation. Participants are welcome to compete in either of the two challenge tasks- Task 1 (“Federated Training”) seeks effective weight aggregation methods for the creation of a consensus model given a pre-defined segmentation algorithm for training, while also (optionally) accounting for network outages. Task 2 (“Federated Evaluation”) see...","","https://www.synapse.org/#!Synapse:syn28546456/wiki/617093","completed","1","","","2022-04-08","2022-08-15","2023-06-23 00:00:00","2023-10-18 00:36:14" +"51","random-promotor","Random Promotor","Deciphering gene regulation: training models to predict gene expression patterns","Decoding how gene expression is regulated is critical to understanding disease. Regulatory DNA is decoded by the cell in a process termed “cis-regulatory logic”, where proteins called Transcription Factors (TFs) bind to specific DNA sequences within the genome and work together to produce as output a level of gene expression for downstream adjacent genes. This process is exceedingly complex to model as a large number of parameters is needed to fully describe the process (see Rationale, de Boer et al. 2020; Zeitingler J. 2020). Understanding the cis-regulatory logic of the human genome is an important goal and would provide insight into the origins of many diseases. However, learning models from human data is challenging due to limitations in the diversity of sequences present within the human genome (e.g. extensive repetitive DNA), the vast number of cell types that differ in how they interpret regulatory DNA, limited reporter assay data, and substantial technical biases present ...","","https://www.synapse.org/#!Synapse:syn28469146/wiki/617075","completed","1","","","2022-05-02","2022-08-07","2023-06-23 00:00:00","2023-10-14 05:38:53" +"52","preterm-birth-prediction-microbiome","Preterm Birth Prediction - Microbiome","Seeking innovations to improve brain tumor diagnosis and treatment","Globally, about 11% of infants every year are born preterm, defined as birth prior to 37 weeks of gestation, totaling nearly 15 million births.(5) In addition to the emotional and financial toll on families, preterm births have higher rates of neonatal death, nearly 1 million deaths each year, and long-term health consequences for some children. Infants born preterm are at risk for a variety of adverse outcomes, such as respiratory illnesses, cerebral palsy, infections, and blindness, with infants born very preterm (i.e., before 32 weeks) at increased risk of these conditions.(6) The ability to accurately predict which women are at a higher risk for preterm birth would help healthcare providers to treat in a timely manner those at higher risk of delivering preterm. Currently available treatments for pregnant women at risk of preterm delivery include corticosteroids for fetal maturation and magnesium sulfate provided prior to 32 weeks to prevent cerebral palsy.(7) There are several...","","https://www.synapse.org/#!Synapse:syn26133770/wiki/612541","completed","1","","","2022-07-19","2022-09-16","2023-06-23 00:00:00","2023-10-14 05:38:54" +"53","finrisk","FINRISK - Heart Failure and Microbiome","Predict incident risk for heart failure in a finnish adults","Cardiovascular diseases are the leading cause of death both in men and women worldwide. Heart failure (HF) is the most common form of heart disease, characterised by the heart's inability to pump a sufficient supply of blood to meet the needs of the body. The lifetime risk of developing HF is roughly 20%, yet, it remains difficult to diagnose due to its and a lack of agreement of diagnostic criteria. As the diagnosis of HF is dependent on ascertainment of clinical histories and appropriate screening of symptomatic individuals, identifying those at risk of HF is essential. This DREAM challenge focuses on the prediction of HF using a combination of gut microbiome and clinical variables. This challenge is designed to predict incident risk for heart failure in a large human population study of Finnish adults, FINRISK 2002 (Borodulin et al., 2018). The FINRISK study has been conducted in Finland to investigate the risk factors for cardiovascular disease every 5 years since 1972. A rand...","","https://www.synapse.org/#!Synapse:syn27130803/wiki/616705","completed","1","","","2022-09-20","2023-01-30","2023-06-23 00:00:00","2023-11-14 19:07:49" +"54","scrna-seq-and-scatac-seq-data-analysis","scRNA-seq and scATAC-seq Data Analysis","Assess computational methods for scrna-seq and scatac-seq analysis","Understanding transcriptional regulation at individual cell resolution is fundamental to understanding complex biological systems such as tissues and organs. Emerging high-throughput sequencing technologies now allow for transcript quantification and chromatin accessibility at the single cell level. These technologies present unique challenges due to inherent data sparsity. Proper signal correction is key to accurate gene expression quantification via scRNA-seq, which propagates into downstream analyses such as differential gene expression analysis and cell-type identification. In the even more sparse scATAC-seq data, the correct identification of informative features is key to assessing cell heterogeneity at the chromatin level. The aims of this challenge will be two-fold- 1) To evaluate computational methods for signal correction and peak identification in scRNA-seq and scATAC-seq, respectively; 2) To assess the impact of these methods on downstream analysis","","https://www.synapse.org/#!Synapse:syn26720920/wiki/615338","completed","1","","","2022-11-29","2023-02-08","2023-06-23 00:00:00","2023-10-14 05:38:56" +"55","cough-diagnostic-algorithm-for-tuberculosis","COugh Diagnostic Algorithm for Tuberculosis","Predict TB status using features extracted from audio of elicited coughs","Tuberculosis (TB), a communicable disease caused by Mycobacterium tuberculosis, is a major cause of ill health and one of the leading causes of death worldwide. Until the COVID-19 pandemic, TB was the leading cause of death from a single infectious agent, ranking even above HIV/AIDS. In 2020, an estimated 9.9 million people fell ill with TB and 1.3 million died of TB worldwide. However, approximately 40% of people with TB were not diagnosed or reported to public health authorities because of challenges in accessing health facilities or failure to be tested or treated when they do. The development of low-cost, non-invasive digital screening tools may improve some of the gaps in diagnosis. As cough is a common symptom of TB, it has the potential to be used as a biomarker for diagnosis of disease. Several previous studies have demonstrated the potential for cough sounds to be used to screen for TB[1-3], though these were typically done in small samples or limited settings. Further de...","","https://www.synapse.org/#!Synapse:syn31472953/wiki/617828","active","1","","","2022-10-16","\N","2023-06-23 00:00:00","2023-12-06 00:58:30" +"56","nih-long-covid-computational-challenge","NIH Long COVID Computational Challenge","Understanding prevalence and outcomes of post-COVID syndrome","The overall prevalence of post-acute sequelae of SARS-CoV-2 (PASC) is currently unknown, but there is growing evidence that more than half of COVID-19 survivors experience at least one symptom of PASC/Long COVID at six months after recovery of the acute illness. Reports also reflect an underlying heterogeneity of symptoms, multi-organ involvement, and persistence of PASC/Long COVID in some patients. Research is ongoing to understand prevalence, duration, and clinical outcomes of PASC/Long COVID. Symptoms of fatigue, cognitive impairment, shortness of breath, and cardiac damage, among others, have been observed in patients who had only mild initial disease. The breadth and complexity of data created in today's health care encounters require advanced analytics to extract meaning from longitudinal data on symptoms, laboratory results, images, functional tests, genomics, mobile health/wearable devices, written notes, electronic health records (EHR), and other relevant data types. Adva...","","https://www.synapse.org/#!Synapse:syn33576900/wiki/618451","completed","1","","","2022-08-25","2022-12-15","2023-06-23 00:00:00","2023-10-18 00:39:03" +"57","bridge2ai","Bridge2AI","What makes a good color palette?","What makes a good color palette?","","","upcoming","1","","","\N","\N","2023-06-23 00:00:00","2023-11-20 20:19:26" +"58","rare-x-open-data-science","RARE-X Open Data Science","Unlocking rare disease mysteries through open science collaboration","The Xcelerate RARE-A Rare Disease Open Science Data Challenge is bringing together researchers and data scientists in a collaborative and competitive environment to make the best use of patient-provided data to solve big unknowns in healthcare. The Challenge will launch to researchers in late May 2023, focused on rare pediatric neurodevelopmental diseases.","","https://www.synapse.org/#!Synapse:syn51198355/wiki/621435","completed","1","","","2023-05-17","2023-08-16","2023-06-23 00:00:00","2023-10-14 05:38:59" +"59","cagi5-regulation-saturation","CAGI5: Regulation saturation","Predicting effects of variants in disease-linked enhancers and promoters","17,500 single nucleotide variants (SNVs) in 5 human disease associated enhancers (including IRF4, IRF6, MYC, SORT1) and 9 promoters (including TERT, LDLR, F9, HBG1) were assessed in a saturation mutagenesis massively parallel reporter assay. Promoters were cloned into a plasmid upstream of a tagged reporter construct, and reporter expression was measured relative to the plasmid DNA to determine the impact of promoter variants. Enhancers were placed upstream of a minimal promoter and assayed similarly. The challenge is to predict the functional effects of these variants in the regulatory regions as measured from the reporter expression.","","https://genomeinterpretation.org/cagi5-regulation-saturation.html","completed","\N","","","2018-01-04","2018-05-03","2023-06-23 00:00:00","2023-12-06 01:09:41" +"60","cagi5-calm1","CAGI5: CALM1","Predicting effects of calmodulin variants on yeast growth","Calmodulin is a calcium-sensing protein that modulates the activity of a large number of proteins in the cell. It is involved in many cellular processes, and is especially important for neuron and muscle cell function. Variants that affect calmodulin function have been found to be causally associated with cardiac arrhythmias. A large library of calmodulin missense variants was assessed with respect to their effects on protein function using a high-throughput yeast complementation assay. The challenge is to predict the functional effects of these calmodulin variants on competitive growth in a high-throughput yeast complementation assay.","","https://genomeinterpretation.org/cagi5-calm1.html","completed","\N","","","2017-10-21","2017-12-20","2023-06-23 00:00:00","2023-10-18 15:35:49" +"61","cagi5-pcm1","CAGI5: PCM1","Assessing PCM1 variants' impact on zebrafish ventricle","The PCM1 (Pericentriolar Material 1) gene is a component of centriolar satellites occurring around centrosomes in vertebrate cells. Several studies have implicated PCM1 variants as a risk factor for schizophrenia. Ventricular enlargement is one of the most consistent abnormal structural brain findings in schizophrenia Therefore 38 transgenic human PCM1 missense mutations implicated in schizophrenia were assayed in a zebrafish model to determine their impact on the posterior ventricle area. The challenge is to predict whether variants implicated in schizophrenia impact zebrafish ventricular area.","","https://genomeinterpretation.org/cagi5-pcm1.html","completed","\N","","","2017-11-09","2018-04-19","2023-06-23 00:00:00","2023-10-18 15:35:49" +"62","cagi5-frataxin","CAGI5: Frataxin","Predicting ΔΔGH20 for Frataxin Variants","Fraxatin is a highly-conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Humans with a frataxin deficiency have the cardio-and neurodegenerative disorder Friedreich's ataxia. A library of eight missense variants was assessed by near and far-UV circular dichroism and intrinsic fluorescence spectra to determine thermodynamic stability at different concentration of denaturant. These were used to calculate a ΔΔGH20 value, the difference in unfolding free energy (ΔGH20) between the mutant and wild-type proteins for each variant. The challenge is to predict ΔΔGH20 for each frataxin variant.","","https://genomeinterpretation.org/cagi5-frataxin.html","completed","\N","","","2017-11-30","2018-04-18","2023-06-23 00:00:00","2023-10-18 15:35:50" +"63","cagi5-tpmt","CAGI5: TPMT and p10","Predicting TPMT and PTEN protein stability variants","The gene p10 encodes for PTEN (Phosphatase and TEnsin Homolog), an important secondary messenger molecule promoting cell growth and survival through signaling cascades including those controlled by AKT and mTOR. Thiopurine S-methyl transferase (TPMT) is a key enzyme involved in the metabolism of thiopurine drugs and functions by catalyzing the S-methylation of aromatic and heterocyclic sulfhydryl groups. A library of thousands of PTEN and TPMT mutations was assessed to measure the stability of the variant protein using a multiplexed variant stability profiling (VSP) assay, which detects the presence of EGFP fused to the mutated PTEN and TPMT protein respectively. The stability of the variant protein dictates the abundance of the fusion protein and thus the EGFP level of the cell. The challenge is to predict the effect of each variant on TPMT and/or PTEN protein stability.","","https://genomeinterpretation.org/cagi5-tpmt.html","completed","\N","","","2017-11-30","2017-12-01","2023-06-23 00:00:00","2023-10-14 05:39:03" +"64","cagi5-annotate-all-missense","CAGI5: Annotate all nonsynonymous variants","Annotate all nonsynonymous variants","dbNSFP describes 810,848,49 possible protein-altering variants in the human genome. The challenge is to predict the functional effect of every such variant. For the vast majority of these missense variants, the functional impact is not currently known, but experimental and clinical evidence are accruing rapidly. Rather than drawing upon a single discrete dataset as typical with CAGI, predictions will be assessed by comparing with experimental or clinical annotations made available after the prediction submission date, on an ongoing basis. if predictors assent, predictions will also incorporated into dbNSFP.","","https://genomeinterpretation.org/cagi5-annotate-all-missense.html","completed","\N","","","2017-11-30","2018-05-09","2023-06-23 00:00:00","2023-10-14 05:39:04" +"65","cagi5-gaa","CAGI5: GAA","Predict enzyme activity of GAA mutants in Pompe disease","Acid alpha-glucosidase (GAA) is a lysosomal alpha-glucosidase. Some mutations in GAA cause a rare disorder, Pompe disease, (Glycogen Storage Disease II). Rare GAA missense variants found in a human population sample have been assayed for enzymatic activity in transfected cell lysates. The assessment of this challenge will include evaluations that recognize novelty of approach. The challenge is to predict the fractional enzyme activity of each mutant protein compared to the wild-type enzyme.","","https://genomeinterpretation.org/cagi5-gaa.html","completed","\N","","","2017-11-09","2018-04-25","2023-06-23 00:00:00","2023-10-14 05:39:04" +"66","cagi5-chek2","CAGI5: CHEK2","Estimate CHEK2 gene variant probabilities in Latino breast cancer cases","Variants in the CHEK2 gene are associated with breast cancer. This challenge includes CHEK2 gene variants from approximately 1200 Latino breast cancer cases and 1200 ethnically matched controls. This challenge is to estimate the probability of each gene variant occurring in an individual from the cancer affected cohort.","","https://genomeinterpretation.org/cagi5-chek2.html","completed","\N","","","2017-12-20","2018-04-24","2023-06-23 00:00:00","2023-10-14 05:39:07" +"67","cagi5-enigma","CAGI5: ENIGMA","Predict cancer risk from BRCA1/2 gene variants","Breast cancer is the most prevalent cancer among women worldwide. The association between germline mutations in the BRCA1 and BRCA2 genes and the development of cancer has been well established. The most common high-risk mutations associated with breast cancer are those in the autosomal dominant breast cancer genes 1 and 2 (BRCA1 and BRCA2). Mutations in these genes are found in 1-3% of breast cancer cases. The challenge is to predict which variants are associated with increased risk for breast cancer.","","https://genomeinterpretation.org/cagi5-enigma.html","completed","\N","","","2017-12-20","2018-05-01","2023-06-23 00:00:00","2023-10-14 05:39:08" +"68","cagi5-mapsy","CAGI5: MaPSy","Predict the impact of genetic variants on splicing mechanisms","The Massively Parallel Splicing Assay (MaPSy) approach was used to screen 797 reported exonic disease mutations using a mini-gene system, assaying both in vivo via transfection in tissue culture, and in vitro via incubation in cell nuclear extract. The challenge is to predict the degree to which a given variant causes changes in splicing.","","https://genomeinterpretation.org/cagi5-mapsy.html","completed","\N","","","2017-11-29","2018-05-07","2023-06-23 00:00:00","2023-10-14 05:39:08" +"69","cagi5-vex-seq","CAGI5: Vex-seq","Predict splicing changes from variants in globin gene","A barcoding approach called Variant exon sequencing (Vex-seq) was applied to assess effect of 2,059 natural single nucleotide variants and short indels on splicing of a globin mini-gene construct transfected into HepG2 cells. This is reported as ΔΨ (delta PSI, or Percent Spliced In), between the variant Ψand the reference Ψ. The challenge is to predict ΔΨ for each variant.","","https://genomeinterpretation.org/cagi5-vex-seq.html","completed","\N","","","2017-12-14","2018-05-02","2023-06-23 00:00:00","2023-10-16 17:51:58" +"70","cagi5-sickkids5","CAGI5: SickKids clinical genomes","Predict genetic disorders from 30 child genomes and phenotypes","This challenge involves 30 children with suspected genetic disorders who were referred for clinical genome sequencing. Predictors are given the 30 genome sequences, and are also provided with the phenotypic descriptions as shared with the diagnostic laboratory. The challenge is to predict what class of disease is associated with each genome, and which genome corresponds to which clinical description. Predictors may additionally identify the diagnostic variant(s) underlying the predictions, and identify predictive secondary variants conferring high risk of other diseases whose phenotypes are not reported in the clinical descriptions.","","https://genomeinterpretation.org/cagi5-sickkids5.html","completed","\N","","","2017-12-22","2018-04-26","2023-06-23 00:00:00","2023-10-14 05:39:10" +"71","cagi5-intellectual-disability","CAGI5: ID Panel","Predict phenotypes and variants from gene panel sequences","The challenge presented here is to use computational methods to predict a patient's clinical phenotype and the causal variant(s) based on analysis of their gene panel sequence data. Sequence data for 74 genes associated with intellectual disability (ID) and/or Autism spectrum disorders (ASD) from a cohort of 150 patients with a range of neurodevelopmental presentations (ID, autism, epilepsy, etc..) have been made available for this challenge. For each patient, predictors must report the causative variants and which of seven phenotypes are present.","","https://genomeinterpretation.org/cagi5-intellectual-disability.html","completed","\N","","","2017-12-22","2018-04-30","2023-06-23 00:00:00","2023-10-18 15:28:06" +"72","cagi5-clotting-disease","CAGI5: Clotting disease exomes","Predict venous thromboembolism risk in African Americans","African Americans have a higher incidence of developing venous thromboembolisms (VTE), which includes deep vein thrombosis (DVT) and pulmonary embolism (PE), than people of European ancestry. Participants are provided with exome data and clinical covariates for a cohort of African Americans who have been prescribed Warfarin either because they had experienced a VTE event or had been diagnosed with atrial fibrillation (which predisposes to clotting). The challenge is to distinguish between these conditions. At present, in contrast to European ancestry, there are no genetic methods for anticipating which African Americans are most at risk of a venous thromboembolism, and the results of this challenge may contribute to the development of such tools.","","https://genomeinterpretation.org/cagi5-clotting-disease.html","completed","\N","","","2017-11-23","2018-04-28","2023-06-23 00:00:00","2023-10-18 15:30:55" +"73","cagi6-sickkids","CAGI6: SickKids clinical genomes and transcriptomes","Identify genes causing rare diseases using transcriptomics","This challenge involves data from 79 children who were referred to The Hospital for Sick Children's (SickKids) Genome Clinic for genome sequencing because of suspected but undiagnosed genetic disorders. Research subjects are consented for sharing of their sequence data and phenotype information with researchers working to understand the molecular causes of rare disease. When a candidate disease variant believed to be related to the phenotype is identified, the variant is adjudicated and confirmed in a clinical setting. In this challenge, transcriptomic and phenotype data from a subset of the “solved” (diagnosed) and “unsolved” SickKids patients will be provided, along with corresponding genomic sequence data. The challenge is to use a transcriptome-driven approach to identify the gene(s) and molecular mechanisms underlying the phenotypic descriptions in each case. For the unsolved cases, prioritized variants from the participating teams will be examined to see if additional diagno...","","https://genomeinterpretation.org/cagi6-sickkids.html","completed","1","","","2021-08-04","2021-12-31","2023-06-23 00:00:00","2023-11-02 18:02:23" +"74","cagi6-cam","CAGI6: CaM","Predict the impact of point mutations on calmodulin stability","Calmodulin (CaM) is a ubiquitous calcium (Ca2+) sensor protein interacting with more than 200 molecular partners, thereby regulating a variety of biological processes. Missense point mutations in the genes encoding CaM have been associated with ventricular tachycardia and sudden cardiac death. A library encompassing up to 17 point mutations was assessed by far-UV circular dichroism (CD) by measuring melting temperature (Tm) and percentage of unfolding (%unfold) upon thermal denaturation at pH and salt concentration that mimic the physiological conditions. The challenge is to predict: the Tm and %unfold values for isolated CaM variants under Ca2+-saturating conditions (Ca2+-CaM) and in the Ca2+-free (apo) state; whether the point mutation stabilizes or destabilizes the protein (based on Tm and %unfold).","","https://genomeinterpretation.org/cagi6-cam.html","completed","1","","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-18 15:32:37" +"75","cami-ii","CAMI II","Assemble and classify microbial genomes in complex samples","CAMI II offers several challenges-an assembly, a genome binning, a taxonomic binning and a taxonomic profiling challenge, on several multi-sample data sets from different environments, including long and short read data. This includes a marine data set and a high-strain diversity data set, with a third data set to follow later. A pathogen detection challenge on a clinical sample is also provided.","","https://www.microbiome-cosi.org/cami/cami/cami2","completed","3","","","2019-01-14","2021-01-31","2023-06-23 00:00:00","2023-10-17 23:15:00" +"76","camda18-metasub-forensics","CAMDA18-MetaSUB Forensics","Build a metagenomic map of mass-transit systems globally","The MetaSUB International Consortium is building a longitudinal metagenomic map of mass-transit systems and other public spaces across the globe. The consortium maintains a strategic partnership with CAMDA and this year provides data from global City Sampling Days for the first-ever multi-city forensic analyses.","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#metasub_forensics_challenge","completed","\N","","","\N","\N","2023-06-23 00:00:00","2023-11-01 20:37:34" +"77","camda18-cmap-drug-safety","CAMDA18-CMap Drug Safety","Predict drug toxicity using cell-based gene expression data","Attrition in drug discovery and development due to safety / toxicity issues remains a significant concern, and there are strong efforts to identify and mitigate risk as early as possible. Drug-induced liver injury (DILI) is one of the primary problems in drug development and regulatory clearance due to the poor performance of existing preclinical models. There is a pressing need to evaluate alternative methods for predicting DILI, with great hopes being placed in modern approaches from statistics and machine learning applied to genome scale profiling data. A critical question thus is if we can better integrate, understand, and exploit information from cell-based screens like the Broad Institute Connectivity Map (CMap, Science 313, Nature Reviews Cancer 7). This CAMDA challenge focuses on understanding or predicting drug induced liver injury in humans from cell-based screens, specifically the CMap gene expression responses of two different cancer cell lines (MCF7 and PC3) to 276 d...","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#cmap_drug_safety_challenge","completed","\N","","","\N","\N","2023-06-23 00:00:00","2023-11-01 20:37:35" +"78","camda18-cancer-data-integration","CAMDA18-Cancer Data Integration","Unify data integration approaches for breast cancer and neuroblastoma","Examine the power of data integration in a real-world clinical settings. Many approaches work well on some data-sets yet not on others. We here challenge you to demonstrate a unified single approach to data-integration that matches or outperforms the current state of the art on two different diseases, breast cancer and neuroblastoma. Breast cancer affects about 3 million women every year (McGuire et al, Cancers 7), and this number is growing fast, especially in developed countries. Can you improve on the large Metabric study (Curtis et al., Nature 486, and Dream Challenge, Margolin et al, Sci Transl Med 5)? The cohort is biologically heterogeneous with all five distinct PAM50 breast cancer subtypes represented. Matched profiles for microarray and copy number data as well as clinical information (survival times, multiple prognostic markers, therapy data) are available for about 2,000 patients. Neuroblastoma is the most common extracranial solid tumor in children. The base study com...","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#cancer_data_integration_challenge","completed","\N","","","\N","\N","2023-06-23 00:00:00","2023-11-01 20:37:36" +"79","cafa-4","CAFA 4","Assess algorithms for predicting protein function","The goal of the Critical Assessment of Functional Annotation(CAFA) challenge is to evaluate automated protein function prediction algorithms in the task of predicting Gene Ontology and Human Phenotype Ontology terms for a given set of protein sequences. For the GO-based predictions, the evaluation will be carried out for the Molecular Function Ontology, Biological Process Ontology and Cellular Component Ontology. Participants develop protein function prediction algorithms using training protein sequence data and submit their predictions on target protein sequence data.","","https://www.biofunctionprediction.org/cafa/","completed","1","","","2019-10-21","2020-02-12","2023-06-23 00:00:00","2023-10-14 05:39:20" +"80","casp13","CASP13","CASP assesses protein structure prediction methods","CASP (Critical Assessment of Structure Prediction) is a community wide experiment to determine and advance the state of the art in modeling protein structure from amino acid sequence. Every two years, participants are invited to submit models for a set of proteins for which the experimental structures are not yet public. Independent assessors then compare the models with experiment. Assessments and results are published in a special issue of the journal PROTEINS. In the most recent CASP round, CASP12, nearly 100 groups from around the world submitted more than 50,000 models on 82 modeling targets","","https://predictioncenter.org/casp13/index.cgi","completed","\N","","","2018-04-18","2018-08-20","2023-06-23 00:00:00","2023-10-17 22:52:29" +"81","casp14","CASP14","Assess progress in protein structure prediction","CASP (Critical Assessment of Structure Prediction) is a community wide experiment to determine and advance the state of the art in modeling protein structure from amino acid sequence. Every two years, participants are invited to submit models for a set of proteins for which the experimental structures are not yet public. Independent assessors then compare the models with experiment. Assessments and results are published in a special issue of the journal PROTEINS. In the most recent CASP round, CASP14, nearly 100 groups from around the world submitted more than 67,000 models on 90 modeling targets.","","https://predictioncenter.org/casp14/index.cgi","completed","\N","","","2020-05-04","2020-09-07","2023-06-23 00:00:00","2023-10-17 22:47:26" +"82","cfsan-pathogen-detection","CFSAN Pathogen Detection","Rapidly identify food sources of outbreaks","In the U.S. alone, one in six individuals, an estimated 48 million people, fall prey to foodborne illness, resulting in 128,000 hospitalizations and 3,000 deaths per year. Economic burdens are estimated cumulatively at $152 billion dollars annually, including $39 billion due to contamination of fresh and processed produce. One longstanding problem is the ability to rapidly identify the food-source associated with the outbreak being investigated. The faster an outbreak is identified and the increased certainty that a given source (e.g., papayas from Mexico) and patients are linked, the faster the outbreak can be stopped, limiting morbidity and mortality. In the last few years, the application of next-generation sequencing (NGS) technology for whole genome sequencing (WGS) of foodborne pathogens has revolutionized food pathogen outbreak surveillance. WGS of foodborne pathogens enables high-resolution identification of pathogens isolated from food or environmental samples. These pat...","","https://precision.fda.gov/challenges/2","completed","6","","","2018-02-15","2018-04-26","2023-06-23 00:00:00","2023-10-14 05:39:23" +"83","cdrh-biothreat","CDRH Biothreat","Identify infectious diseases from clinical samples using sequencing technology","Many infectious diseases have similar signs and symptoms, making it challenging for healthcare providers to identify the disease-causing agent. Clinical samples are often tested by multiple test methods to help reveal the microbe that is causing the infectious disease. The results of these test methods can help healthcare professionals determine the best treatment for patients. Today, High-Throughput Sequencing (HTS) or Next Generation Sequencing (NGS) technology has the capability, as a single test, to accomplish what might have required several different tests in the past. NGS technology may allow the diagnosis of infections without prior knowledge of disease(s) cause. NGS technology can potentially reveal the presence of all microorganisms in a patient sample. Using infectious disease NGS (ID-NGS) technology, each microbial pathogen may be identified by its unique genomic fingerprint. The vision of ID-NGS technology is to further improve patient care by delivering diagnostics ...","","https://precision.fda.gov/challenges/3","completed","6","","","2018-08-03","2018-10-18","2023-06-23 00:00:00","2023-10-14 05:39:24" +"84","multi-omics-enabled-sample-mislabeling-correction","Multi-omics Enabled Sample Mislabeling Correction","Identify and correct sample and data mislabeling events","In biomedical research, sample mislabeling (accidental swapping of patient samples) or data mislabeling (accidental swapping of patient omics data) has been a long-standing problem that contributes to irreproducible results and invalid conclusions. These problems are particularly prevalent in large scale multi-omics studies, in which multiple different omics experiments are carried out at different time periods and/or in different labs. Human errors could arise during sample transferring, sample tracking, large-scale data generation, and data sharing/management. Thus, there is a pressing need to identify and correct sample and data mislabeling events to ensure the right data for the right patient. Simultaneous use of multiple types of omics platforms to characterize a large set of biological samples, as utilized in The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects, has been demonstrated as a powerful approach to understanding the ...","","https://precision.fda.gov/challenges/4","completed","6","https://doi.org/10.1038/s41591-018-0180-x","","2018-09-24","2018-12-19","2023-06-23 00:00:00","2023-11-14 19:07:58" +"85","biocompute-object-app-a-thon","BioCompute Object App-a-thon","Seeking standards for reproducible bioinformatics analysis","Like scientific laboratory experiments, bioinformatics analysis results and interpretation are faced with reproducibility challenges due to the variability in multiple computational parameters, including input format, prerequisites, platform dependencies, and more. Even small changes in these computational parameters may have a large impact on the results and carry big implications for their scientific validity. Because there are currently no standardized schemas for reporting computational scientific workflows and parameters together with their results, the ways in which these workflows are communicated is highly variable, incomplete, and difficult or impossible to reproduce. The US Food and Drug Administration (FDA) High Performance Virtual Environment (HIVE) group and George Washington University (GW) have partnered to establish a framework for community-based standards development and harmonization of high-throughput sequencing (HTS) computations and data formats based arou...","","https://precision.fda.gov/challenges/7/","completed","6","https://doi.org/10.1101/2020.11.02.365528","","2019-05-14","2019-10-18","2023-06-23 00:00:00","2023-10-14 05:39:25" +"86","brain-cancer-predictive-modeling-and-biomarker-discovery","Brain Cancer Predictive Modeling and Biomarker Discovery","Seeking novel biomarkers to advance precision medicine for brain tumors","An estimated 86,970 new cases of primary brain and other central nervous system tumors are expected to be diagnosed in the US in 2019. Brain tumors comprise a particularly deadly subset of all cancers due to limited treatment options and the high cost of care. Only a few prognostic and predictive markers have been successfully implemented in the clinic so far for gliomas, the most common malignant brain tumor type. These markers include MGMT promoter methylation in high-grade astrocytomas, co-deletion of 1p/19q in oligodendrogliomas, and mutations in IDH1 or IDH2 genes (Staedtke et al. 2016). There remains significant potential for identifying new clinical biomarkers in gliomas. Clinical investigators at Georgetown University are seeking to advance precision medicine techniques for the prognosis and treatment of brain tumors through the identification of novel multi-omics biomarkers. In support of this goal, precisionFDA and the Georgetown Lombardi Comprehensive Cancer Center and ...","","https://precision.fda.gov/challenges/8/","completed","6","","","2019-11-01","2020-02-14","2023-06-23 00:00:00","2023-10-14 05:39:25" +"87","gaining-new-insights-by-detecting-adverse-event-anomalies","Gaining New Insights by Detecting Adverse Event Anomalies","Seeking algorithms to detect adverse events in FDA data","The Food and Drug Administration (FDA) calls on the public to develop computational algorithms for automatic detection of adverse event anomalies using publicly available data.","","https://precision.fda.gov/challenges/9/","completed","6","","","2020-01-17","2020-05-18","2023-06-23 00:00:00","2023-10-14 05:39:27" +"88","calling-variants-in-difficult-to-map-regions","Calling Variants in Difficult-to-Map Regions","Precision benchmarking: evaluating variant calling in complex genomic regions","This challenge calls on the public to assess variant calling pipeline performance on a common frame of reference, with a focus on benchmarking in difficult-to-map regions, segmental duplications, and the Major Histocompatibility Complex (MHC).","","https://precision.fda.gov/challenges/10/","completed","6","https://doi.org/10.1016/j.xgen.2022.100129","","2020-05-01","2020-06-15","2023-06-23 00:00:00","2023-10-14 05:39:28" +"89","vha-innovation-ecosystem-and-covid-19-risk-factor-modeling","VHA Innovation Ecosystem and COVID-19 Risk Factor Modeling","AI for COVID-19: predicting health outcomes in the veteran population","The novel coronavirus disease 2019 (COVID-19) is a respiratory disease caused by a new type of coronavirus, known as “severe acute respiratory syndrome coronavirus 2,” or SARS-CoV-2. On March 11, 2020, the World Health Organization (WHO) declared the outbreak a global pandemic. As of Monday, June 1, the Johns Hopkins University COVID-19 dashboard reports over 6.21 million total confirmed cases worldwide, including over 1.79 million cases in the United States. Although most people have mild to moderate symptoms, the disease can cause severe medical complications leading to death in some people. The Centers for Disease Control and Prevention (CDC) have identified several groups at elevated risk for severe illness, including people 65 years and older, individuals living in nursing homes or long term care facilities, and those with serious underlying medical conditions, such as severe obesity, diabetes, chronic lung disease or moderate to severe asthma, chronic kidney or liver disease...","","https://precision.fda.gov/challenges/11/","completed","6","","","2020-06-02","2020-07-03","2023-06-23 00:00:00","2023-10-14 05:39:28" +"90","covid-19-precision-immunology-app-a-thon","COVID-19 Precision Immunology App-a-thon","Seeking insights on COVID-19 pathophysiology to enable effective strategies","The novel coronavirus disease 2019 (COVID-19), a respiratory disease caused by a new type of coronavirus, known as “severe acute respiratory syndrome coronavirus 2” or SARS-CoV-2, was declared a global pandemic by the World Health Organization on March 11, 2020. To date, the Johns Hopkins University COVID-19 dashboard reports over 62 million confirmed cases worldwide, with a wide range of disease severity from asymptomatic to deaths (over 1.46 million). To effectively combat the widespread transmission of COVID-19 infection and save lives especially of those vulnerable individuals, it is imperative to better understand its pathophysiology to enable effective diagnosis, prognosis and treatment strategies using rapidly shared data.","","https://precision.fda.gov/challenges/12/","completed","6","","","2020-11-30","2021-01-29","2023-06-23 00:00:00","2023-10-14 05:39:29" +"91","smarter-food-safety-low-cost-tech-enabled-traceability","Smarter Food Safety Low Cost Tech-Enabled Traceability","Seeking affordable tech solutions for food traceability","The motivation is tapping into new technologies and integrating data streams will help to advance the widespread, consistent implementation of traceability systems across the food industry. However, the affordability of such technologies, particularly for smaller companies, can be a barrier to implementing tech-enabled traceability systems. FDA's New Era of Smarter Food Safety initiative strives to work with stakeholders to explore low-cost or no-cost options so that our approaches are inclusive of and viable for human and animal food operations of all sizes. Democratizing the benefits of digitizing data will allow the entire food system to move more rapidly towards digital traceability systems. The primary goal is to encourage stakeholders, including technology providers, public health advocates, entrepreneurs, and innovators from all disciplines and around the world, to develop traceability hardware, software, or data analytics platforms that are low-cost or no-cost to the en...","","https://precision.fda.gov/challenges/13","completed","6","","","2021-06-01","2021-07-30","2023-06-23 00:00:00","2023-10-17 23:05:49" +"92","tumor-mutational-burden-tmb-challenge-phase-1","Tumor Mutational Burden (TMB) Challenge Phase 1","Standardize tumor mutational burden (TMB) calculation in cancer research","Tumor mutational burden (TMB) is generally defined as the number of mutations detected in a patient's tumor sample per megabase of DNA sequenced. However different algorithms use different methods for calculating TMB. Mutations in genes in tumor cells may lead to the creation of neoantigens, which have the potential to activate an immune system response against the tumor, and the likelihood of an immune system response may increase with the number of mutations. Thus, TMB is a biomarker for some immunotherapy drugs, called immune checkpoint inhibitors, such as those that target the PD-1 and PD-L1 pathways (Chan et al., 2019). An outstanding problem is the lack of standardization for TMB calculation and reporting between different assays. To address this problem, the Friends of Cancer Research convened a working group of industry and regulatory stakeholders to develop guidance and tools for TMB harmonization. Results from the first phase of this effort were presented at AACR 2020 (...","","https://precision.fda.gov/challenges/17","completed","6","","","2021-06-21","2021-09-13","2023-06-23 00:00:00","2023-11-02 18:28:46" +"93","kits21","KiTS21","Contest seeks best kidney tumor segmentation system","The 2021 Kidney and Kidney Tumor Segmentation challenge (abbreviated KiTS21) is a competition in which teams compete to develop the best system for automatic semantic segmentation of renal tumors and surrounding anatomy. Kidney cancer is one of the most common malignancies in adults around the world, and its incidence is thought to be increasing [1]. Fortunately, most kidney tumors are discovered early while they're still localized and operable. However, there are important questions concerning management of localized kidney tumors that remain unanswered [2], and metastatic renal cancer remains almost uniformly fatal [3]. Kidney tumors are notorious for their conspicuous appearance in computed tomography (CT) imaging, and this has enabled important work by radiologists and surgeons to study the relationship between tumor size, shape, and appearance and its prospects for treatment [4,5,6]. It's laborious work, however, and it relies on assessments that are often subjective and impr...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/594/rendering_dimmed.png","https://kits21.grand-challenge.org/","completed","5","","","2021-08-23","2021-09-17","2023-06-23 00:00:00","2023-11-28 00:32:11" +"94","realnoisemri","RealNoiseMRI","Brain MRI reconstruction challenge with realistic noise","In recent years, there is a growing focus on the application of fast magnetic resonance imaging (MRI) based on prior knowledge. In the 1980s and 2000s the community used either purely mathematical models such as the partial Fourier transform or solutions derived through advanced engineering such as parallel imaging to speed up MRI acquisition. Since the mid-2000's, compressed sensing and artificial intelligence have been employed to speed up MRI acquisition. These newer methods rely on under sampling the data acquired in Fourier (aka k-) space and then interpolating or augmenting k-space data based on training data content. One of the underlying problems for the development of fast imaging techniques, that just as in e.g. [1], it is common to use a fully sampled image as ground truth and then under sample it in k-space in order to simulate under sampled data. The problem with this approach is that in cases were the under sampled data is corrupted, through e.g. motion, this under s...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/597/Logo_Challenge.png","https://realnoisemri.grand-challenge.org/","completed","5","","","2021-09-21","2021-12-06","2023-06-23 00:00:00","2023-11-27 20:40:05" +"95","deep-generative-model-challenge-for-da-in-surgery","Deep Generative Model Challenge for DA in Surgery","Challenge aims to adapt algorithms from simulation to mitral valve surgery","Mitral regurgitation (MR) is the second most frequent indication for valve surgery in Europe and may occur for organic or functional causes [1]. Mitral valve repair, although considerably more difficult, is prefered over mitral valve replacement, since the native tissue of the valve is preserved. It is a complex on-pump heart surgery, often conducted only by a handful of surgeons in high-volume centers. Minimally invasive procedures, which are performed with endoscopic video recordings, became more and more popular in recent years. However, data availability and data privacy concerns are still an issue for the development of automatic scene analysis algorithms. The AdaptOR challenge aims to address these issues by formulating a domain adaptation problem from simulation to surgery. We provide a smaller number of datasets from real surgeries, and a larger number of annotated recordings of training and planning sessions from a physical mitral valve simulator. The goal is to reduce th...","","https://adaptor2021.github.io/","completed","1","","","2021-04-01","2021-07-16","2023-06-23 00:00:00","2023-10-14 05:39:34" +"96","aimdatathon","AIM Datathon 2020","AI in Medicine (AIM) Datathon 2020","Join the AI in Medicine ( AIM ) Datathon 2020","","https://www.kaggle.com/competitions/aimdatathon","completed","8","","","2020-11-09","2020-11-22","2023-06-23 00:00:00","2023-11-15 22:43:19" +"97","opc-recurrence","Oropharynx Cancer (OPC) Radiomics Challenge :: Local Recurrence Prediction","Determine whether a tumor will be controlled by definitive radiation therapy","Determine from CT data whether a tumor will be controlled by definitive radiation therapy.","","https://www.kaggle.com/competitions/opc-recurrence","completed","8","","","2016-07-26","2016-09-12","2023-06-23 00:00:00","2023-11-14 19:11:07" +"98","oropharynx-radiomics-hpv","Oropharynx Cancer (OPC) Radiomics Challenge :: Human Papilloma Virus (HPV) Status Prediction","Predict hpv phenotype of oropharynx tumors; compare to ground truth data","Predict from CT data the HPV phenotype of oropharynx tumors; compare to ground-truth results previously obtained by p16 or HPV testing.","","https://www.kaggle.com/competitions/oropharynx-radiomics-hpv","completed","8","","","2016-07-26","2016-09-12","2023-06-23 00:00:00","2023-11-14 19:11:17" +"99","data-science-bowl-2017","Data Science Bowl 2017","Can you improve lung cancer detection?","Can you improve lung cancer detection?","","https://www.kaggle.com/competitions/data-science-bowl-2017","completed","8","","","2017-01-12","2017-04-12","2023-06-23 00:00:00","2023-10-14 05:39:38" +"100","predict-impact-of-air-quality-on-death-rates","Predict impact of air quality on mortality rates","Predict CVD and cancer caused mortality rates in England using air quality data","Predict CVD and cancer caused mortality rates in England using air quality data available from Copernicus Atmosphere Monitoring Service","","https://www.kaggle.com/competitions/predict-impact-of-air-quality-on-death-rates","completed","8","","","2017-02-13","2017-05-05","2023-06-23 00:00:00","2023-10-14 05:39:38" +"101","intel-mobileodt-cervical-cancer-screening","Intel & MobileODT Cervical Cancer Screening","Which cancer treatment will be most effective?","Which cancer treatment will be most effective?","","https://www.kaggle.com/competitions/intel-mobileodt-cervical-cancer-screening","completed","8","","","2017-03-15","2017-06-21","2023-06-23 00:00:00","2023-10-14 05:39:39" +"102","msk-redefining-cancer-treatment","Personalized Medicine-Redefining Cancer Treatment","Predict the effect of genetic variants to enable personalized medicine","Predict the effect of Genetic Variants to enable Personalized Medicine","","https://www.kaggle.com/competitions/msk-redefining-cancer-treatment","completed","8","","","2017-06-26","2017-10-02","2023-06-23 00:00:00","2023-11-02 18:32:51" +"103","mubravo","Predicting Cancer Diagnosis","Bravo's machine learning competition!","Bravo's machine learning competition!","","https://www.kaggle.com/competitions/mubravo","completed","8","","","2018-07-31","2018-08-13","2023-06-23 00:00:00","2023-10-14 05:39:41" +"104","histopathologic-cancer-detection","Histopathologic Cancer Detection","Identify metastatic tissue in histopathologic scans of lymph node sections","Identify metastatic tissue in histopathologic scans of lymph node sections","","https://www.kaggle.com/competitions/histopathologic-cancer-detection","completed","8","","","2018-11-16","2019-03-30","2023-06-23 00:00:00","2023-10-14 05:39:41" +"105","tjml1920-decision-trees","TJML 2019-20 Breast Cancer Detection Competition","Use a decision tree to identify malignant breast cancer tumors","Use a decision tree to identify malignant breast cancer tumors","","https://www.kaggle.com/competitions/tjml1920-decision-trees","completed","8","","","2019-09-22","2019-10-16","2023-06-23 00:00:00","2023-10-14 05:39:42" +"106","prostate-cancer-grade-assessment","Prostate cANcer graDe Assessment (PANDA) Challenge","Prostate cancer diagnosis using the gleason grading system","Prostate cancer diagnosis using the Gleason grading system","","https://www.kaggle.com/competitions/prostate-cancer-grade-assessment","completed","8","","","2020-04-21","2020-07-22","2023-06-23 00:00:00","2023-10-14 05:39:43" +"107","breast-cancer","Breast Cancer","Use cell nuclei categories to predict breast cancer tumor","Use cell nuclei categories to predict breast cancer tumor.","","https://www.kaggle.com/competitions/breast-cancer","completed","8","","","2020-08-12","2020-08-13","2023-06-23 00:00:00","2023-10-14 05:39:43" +"108","breast-cancer-detection","Breast Cancer Detection","Breast cancer detection","breast cancer detection","","https://www.kaggle.com/competitions/breast-cancer-detection","completed","8","","","2020-09-25","2020-12-31","2023-06-23 00:00:00","2023-10-14 05:39:44" +"109","hrpred","Prediction of High Risk Patients","Classification of high and low risk cancer patients","Classification of high and low risk cancer patients","","https://www.kaggle.com/competitions/hrpred","completed","8","","","2020-11-25","2020-12-05","2023-06-23 00:00:00","2023-10-14 05:39:44" +"110","ml4moleng-cancer","MIT ML4MolEng-Predicting Cancer Progression","MIT 3.100, 10.402, 20.301 In class ML competition (Spring 2021)","MIT 3.100, 10.402, 20.301 In class ML competition (Spring 2021)","","https://www.kaggle.com/competitions/ml4moleng-cancer","completed","8","","","2021-05-06","2021-05-21","2023-06-23 00:00:00","2023-11-16 18:41:14" +"111","uw-madison-gi-tract-image-segmentation","UW-Madison GI Tract Image Segmentation","Track healthy organs in medical scans to improve cancer treatment","Track healthy organs in medical scans to improve cancer treatment","","https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation","completed","8","","","2022-04-14","2022-07-14","2023-06-23 00:00:00","2023-10-14 05:39:46" +"112","rsna-miccai-brain-tumor-radiogenomic-classification","RSNA-MICCAI Brain Tumor Radiogenomic Classification","Predict the status of a genetic biomarker important for brain cancer treatment","The Brain Tumor Segmentation (BraTS) challenge celebrates its 10th anniversary, and this year is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society. The RSNA-ASNR-MICCAI BraTS 2021 challenge utilizes multi-institutional pre-operative baseline multi-parametric magnetic resonance imaging (mpMRI) scans, and focuses on the evaluation of state-of-the-art methods for (Task 1) the segmentation of intrinsically heterogeneous brain glioblastoma sub-regions in mpMRI scans. Furthemore, this BraTS 2021 challenge also focuses on the evaluation of (Task 2) classification methods to predict the MGMT promoter methylation status. Participants are free to choose whether they want to focus only on one or both tasks.","","https://www.kaggle.com/competitions/rsna-miccai-brain-tumor-radiogenomic-classification","completed","8","","","2021-07-13","2021-10-15","2023-06-23 00:00:00","2023-10-14 05:39:46" +"113","breastcancer","Breast Cancer - Beginners ML","Beginners hands-on experience with ML basics","Beginners hands-on experience with ML basics","","https://www.kaggle.com/competitions/breastcancer","completed","8","","","2021-12-21","2022-02-12","2023-06-23 00:00:00","2023-10-18 21:18:15" +"114","ml-olympiad-health-and-education","ML Olympiad -Let's Fight lung cancer","Use your ml expertise to help us step another step toward defeating cancer","Use your ML expertise to help us step another step toward defeating cancer [ Starts on the 14th February ]","","https://www.kaggle.com/competitions/ml-olympiad-health-and-education","completed","8","","","2022-01-31","2022-03-19","2023-06-23 00:00:00","2023-11-15 18:45:55" +"115","cs98-22-dl-task1","CS98X-22-DL-Task1","This competition is related to task 1 in coursework-breast cancer classification","This competition is related to Task 1 in coursework-breast cancer classification","","https://www.kaggle.com/competitions/CS98-22-DL-Task1","completed","8","","","2022-02-28","2022-04-11","2023-06-23 00:00:00","2023-10-14 05:39:48" +"116","parasitedetection-iiitb2019","Parasite detection","Detect if cell image has parasite or is uninfected","detect if cell image has parasite or is uninfected","","https://www.kaggle.com/competitions/parasitedetection-iiitb2019","completed","8","","","2019-10-13","2019-11-25","2023-06-23 00:00:00","2023-10-14 05:39:49" +"117","hpa-single-cell-image-classification","Human Protein Atlas -Single Cell Classification","Find individual human cell differences in microscope images","Find individual human cell differences in microscope images","","https://www.kaggle.com/competitions/hpa-single-cell-image-classification","completed","8","","","2021-01-26","2021-05-11","2023-06-23 00:00:00","2023-10-14 05:39:50" +"118","stem-cell-predcition","Stem Cell Predcition","Classify stem and non-stem cells using RNA-seq data","Classify stem and non-stem cells using RNA-seq data","","https://www.kaggle.com/competitions/stem-cell-predcition","completed","8","","","2021-04-01","2021-07-01","2023-06-23 00:00:00","2023-10-14 05:39:50" +"119","sartorius-cell-instance-segmentation","Sartorius - Cell Instance Segmentation","Detect single neuronal cells in microscopy images","In this competition, you’ll detect and delineate distinct objects of interest in biological images depicting neuronal cell types commonly used in the study of neurological disorders. More specifically, you'll use phase contrast microscopy images to train and test your model for instance segmentation of neuronal cells. Successful models will do this with a high level of accuracy. If successful, you'll help further research in neurobiology thanks to the collection of robust quantitative data. Researchers may be able to use this to more easily measure the effects of disease and treatment conditions on neuronal cells. As a result, new drugs could be discovered to treat the millions of people with these leading causes of death and disability.","","https://www.kaggle.com/competitions/sartorius-cell-instance-segmentation","completed","8","","","2021-10-14","2021-12-30","2023-06-23 00:00:00","2023-10-16 18:05:17" +"120","pvelad","Photovoltaic cell anomaly detection","Photovoltaic cell anomaly detection","Hosted by Hebei University of Technology (AIHebut research group) and Beihang University (NAVE research group)","","https://www.kaggle.com/competitions/pvelad","completed","8","","","2022-03-15","2022-07-30","2023-06-23 00:00:00","2023-10-14 05:39:51" +"121","blood-mnist","Blood-MNIST","Classifying blood cell types using weights and biases","Classifying blood cell types using Weights and Biases","","https://www.kaggle.com/competitions/blood-mnist","completed","8","","","2022-03-19","2022-03-19","2023-06-23 00:00:00","2023-11-14 20:33:37" +"122","insilicomolhack","MolHack","Apply deep learning to speedup drug validation","Apply deep learning to speedup drug validation","","https://www.kaggle.com/competitions/insilicomolhack","completed","8","","","2018-04-02","2018-05-25","2023-06-23 00:00:00","2023-10-14 05:39:53" +"123","codata2019challenge","Cell Response Classification","From recorded timeseries of many cells in a well, predict which drug treatme","From recorded timeseries of many cells in a well, predict which drug treatment has been applied","","https://www.kaggle.com/competitions/codata2019challenge","completed","8","","","2019-04-08","2019-05-07","2023-06-23 00:00:00","2023-10-14 05:39:53" +"124","drug-solubility-challenge","Drug solubility challenge","Crucial role of solubility in drug formulation for optimal efficacy","Solubility is vital to achieve desired concentration of drug for anticipated pharmacological response.","","https://www.kaggle.com/competitions/drug-solubility-challenge","completed","8","","","2019-05-18","2019-10-18","2023-06-23 00:00:00","2023-10-14 05:39:54" +"125","kinase-inhibition-challenge","Kinase inhibition challenge","Unlocking the therapeutic potential of protein kinases: big data insights","Protein kinases have become a major class of drug targets, accumulating a huge amount of data","","https://www.kaggle.com/competitions/kinase-inhibition-challenge","completed","8","","","2019-05-20","2019-12-28","2023-06-23 00:00:00","2023-10-14 05:39:54" +"126","ai-drug-discovery","AI Drug Discovery Workshop and Coding Challenge","Fostering core AI programming proficiency for drug discovery advancements","Developing Fundamental AI Programming Skills for Drug Discovery","","https://www.kaggle.com/competitions/ai-drug-discovery","completed","8","","","2021-11-12","2021-12-31","2023-06-23 00:00:00","2023-11-02 18:41:48" +"127","protein-compound-affinity","Structure-free protein-ligand affinity prediction - Task 1 Fitting","Developing new AI models for drug discovery","Developing new AI models for drug discovery, main portal (Task-1 fitting)","","https://www.kaggle.com/competitions/protein-compound-affinity","completed","8","","","2021-12-06","2021-12-31","2023-06-23 00:00:00","2023-11-14 20:34:30" +"128","cisc873-dm-f21-a5","CISC873-DM-F21-A5","Anti-cancer drug activity prediction","Anti-Cancer Drug Activity Prediction","","https://www.kaggle.com/competitions/cisc873-dm-f21-a5","completed","8","","","2021-11-26","2021-12-10","2023-06-23 00:00:00","2023-10-14 05:39:56" +"129","pro-lig-aff-task2-mse","Structure-free protein-ligand affinity prediction - Task 2 Fitting","Developing new AI models for drug discovery","Developing new AI models for drug discovery (Task-2 fitting)","","https://www.kaggle.com/competitions/pro-lig-aff-task2-mse","completed","8","","","2021-12-08","2021-12-31","2023-06-23 00:00:00","2023-11-15 22:42:37" +"130","pro-lig-aff-task1-pearsonr","Structure-free protein-ligand affinity prediction - Task 1 Ranking","Developing new AI models for drug discovery","Developing new AI models for drug discovery (Task-1 ranking)","","https://www.kaggle.com/competitions/pro-lig-aff-task1-pearsonr","completed","8","","","2021-12-08","2021-12-31","2023-06-23 00:00:00","2023-11-15 22:42:40" +"131","pro-lig-aff-task2-pearsonr","Structure-free protein-ligand affinity prediction - Task 2 Ranking","Developing new AI models for drug discovery","Developing new AI models for drug discovery (Task-2 ranking)","","https://www.kaggle.com/competitions/pro-lig-aff-task2-pearsonr","completed","8","","","2021-12-08","2021-12-31","2023-06-23 00:00:00","2023-11-15 22:42:43" +"132","pro-lig-aff-task3-spearmanr","Structure-free protein-ligand affinity prediction - Task 3 Ranking","Developing new AI models for drug discovery","Developing new AI models for drug discovery (Task-3 ranking)","","https://www.kaggle.com/competitions/pro-lig-aff-task3-spearmanr","completed","8","","","2021-12-08","2021-12-31","2023-06-23 00:00:00","2023-11-15 22:42:44" +"133","hhp","Heritage Health Prize","Identify patients who will be admitted to a hospital within the next year","Identify patients who will be admitted to a hospital within the next year using historical claims data. (Enter by 06-59-59 UTC Oct 4 2012)","","https://www.kaggle.com/competitions/hhp","completed","8","","","2011-04-04","2013-04-04","2023-06-23 00:00:00","2023-11-14 19:11:25" +"134","pf2012","Practice Fusion Analyze This! 2012 - Prediction Challenge","Propose innovative predictive modeling challenges","Start digging into electronic health records and submit your ideas for the most promising, impactful or interesting predictive modeling competitions","","https://www.kaggle.com/competitions/pf2012","completed","8","","","2012-06-07","2012-06-30","2023-06-23 00:00:00","2023-11-14 19:11:32" +"135","pf2012-at","Practice Fusion Analyze This! 2012 - Open Challenge","Propose innovative predictive modeling challenges","Start digging into electronic health records and submit your creative, insightful, and visually striking analyses.","","https://www.kaggle.com/competitions/pf2012-at","completed","8","","","2012-06-07","2012-09-10","2023-06-23 00:00:00","2023-11-14 19:21:00" +"136","seizure-detection","UPenn and Mayo Clinic's Seizure Detection Challenge","Detect seizures in intracranial EEG recordings","Detect seizures in intracranial EEG recordings","","https://www.kaggle.com/competitions/seizure-detection","completed","8","","","2014-05-19","2014-08-19","2023-06-23 00:00:00","2023-10-14 05:40:02" +"137","seizure-prediction","American Epilepsy Society Seizure Prediction Challenge","Predict seizures in intracranial EEG recordings","Predict seizures in intracranial EEG recordings","","https://www.kaggle.com/competitions/seizure-prediction","completed","8","","","2014-08-25","2014-11-17","2023-06-23 00:00:00","2023-10-14 05:40:03" +"138","deephealth-1","Deep Health - alcohol","Find correlations and patterns with medical data","Find Correlations and patterns with Medical data","","https://www.kaggle.com/competitions/deephealth-1","completed","8","","","2017-02-13","2017-02-19","2023-06-23 00:00:00","2023-10-16 18:14:48" +"139","deep-health-3","Deep Health - Diabetes 2","Deep health hackathon: predicting future diabetes occurrences challenge","This competition is for those attending the Deep Health Hackathon. Predict the next occurrence of diabetes","","https://www.kaggle.com/competitions/deep-health-3","completed","8","","","2017-02-15","2017-02-19","2023-06-23 00:00:00","2023-10-16 18:14:50" +"140","d012554-2021","D012554 - 2021","Classify the health of a fetus using CTG data","Classify the health of a fetus using CTG data","","https://www.kaggle.com/competitions/d012554-2021","completed","8","","","2021-04-11","2021-05-09","2023-06-23 00:00:00","2023-10-16 18:15:04" +"141","idao-2022-bootcamp-insomnia","IDAO 2022. ML Bootcamp - Insomnia","Predict sleep disorder on given human health data","Predict sleep disorder on given human health data","","https://www.kaggle.com/competitions/idao-2022-bootcamp-insomnia","completed","8","","","2021-12-04","2021-12-05","2023-06-23 00:00:00","2023-10-16 18:15:12" +"142","tweet-mental-health-classification","Tweet Mental Health Classification","Build models to classify tweets to determine mental health","Build Models to classify tweets to determine mental health","","https://www.kaggle.com/competitions/tweet-mental-health-classification","completed","8","","","2021-12-27","2022-01-31","2023-06-23 00:00:00","2023-10-14 05:40:07" +"143","ml-olympiad-good-health-and-well-being","ML Olympiad - GOOD HEALTH AND WELL BEING","Use your ML expertise to classify if a patient has heart disease or not","Use your ML expertise to classify if a patient has heart disease or not","","https://www.kaggle.com/competitions/ml-olympiad-good-health-and-well-being","completed","8","","","2022-02-03","2022-03-01","2023-06-23 00:00:00","2023-10-16 18:15:20" +"144","rsna-breast-cancer-detection","RSNA Screening Mammography Breast Cancer Detection","Find breast cancers in screening mammograms","Find breast cancers in screening mammograms","","https://www.kaggle.com/competitions/rsna-breast-cancer-detection","completed","8","","","2022-11-28","2023-02-27","2023-06-23 00:00:00","2023-10-14 05:40:12" +"145","biocreative-vii-text-mining-drug-and-chemical-protein-interactions-drugprot","BioCreative VII: Text mining drug and chemical-protein interactions (DrugProt)","Develop systems to extract drug-gene relations from text","With the rapid accumulation of biomedical literature, it is getting increasingly challenging to exploit efficiently drug-related information described in the scientific literature. One of the most relevant aspects of drugs and chemical compounds are their relationships with certain biomedical entities, in particular genes and proteins. The aim of the DrugProt track (similar to the previous CHEMPROT task of BioCreative VI) is to promote the development and evaluation of systems that are able to automatically detect in relations between chemical compounds/drug and genes/proteins. There are a range of different types of drug-gene/protein interactions, and their systematic extraction and characterization is essential to analyze, predict and explore key biomedical properties underlying high impact biomedical applications. These application scenarios include use cases related to drug discovery, drug repurposing, drug design, metabolic engineering, modeling drug response, pharmacogenet...","","https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/","completed","\N","","","2021-06-15","2021-09-22","2023-06-23 00:00:00","2023-11-01 20:37:37" +"146","extended-literature-ai-for-drug-induced-liver-injury","Extended Literature AI for Drug Induced Liver Injury","Develop ML tools to analyze drug texts for liver injury data","Unexpected Drug-Induced Liver Injury (DILI) still is one of the main killers of promising novel drug candidates. It is a clinically significant disease that can lead to severe outcomes such as acute liver failure and even death. It remains one of the primary liabilities in drug development and regulatory clearance due to the limited performance of mandated preclinical models even today. The free text of scientific publications is still the main medium carrying DILI results from clinical practice or experimental studies. The textual data still has to be analysed manually. This process, however, is tedious and prone to human mistakes or omissions, as results are very rarely available in a standardized form or organized form. There is thus great hope that modern techniques from machine learning or natural language processing could provide powerful tools to better process and derive the underlying knowledge within free form texts. The pressing need to faster process potential drug can...","","http://camda2022.bioinf.jku.at/contest_dataset#extended_literature_ai_for_drug_induced_liver_injury","completed","\N","","","\N","2022-05-20","2023-06-23 00:00:00","2023-11-01 20:37:38" +"147","anti-microbial-resistance-forensics","Anti-Microbial Resistance Forensics","Classifying bacteriophages to understand microbial evolution","Bacteriophages, being the re-occuring mystery in the history of science are believed to be they key for understanding of microbial evolution and the transfer of AMR genes. Recent studies show that there is a significant correlation between occurence of Phages and AMR genes, indicating that they are indeed taking part in the spread of them. While taking part in AMR dissemination the phages are also considered as the potential alternative to antibiotics. In such contradictory world there is a huge potential as well as urgent need for precise classification, description and analysis of capabilities. Due to pandemic of SARS-CoV-2, advance in phylogenetic algorithms and k-mer based methods have been extremely rapid and those improvements are witing to be adapted to different branches of life sciences.","","http://camda2022.bioinf.jku.at/contest_dataset#anti-microbial_resistance_forensics","completed","\N","","","\N","2022-05-20","2023-06-23 00:00:00","2023-10-14 05:40:14" +"148","disease-maps-to-modelling-covid-19","Disease Maps to Modelling COVID-19","Suggest drugs candidate for repurposing","The Disease Maps to modeling COVID-19 Challenge provides highly detailed expert-curated molecular mechanistic maps for COVID-19. Combine them with available omic data to expand the current biological knowledge on COVID-19 mechanism of infection and downstream consequences. The main topic for this year's challenge is drug repurposing with the possibility of Real World Data based validation of the most promising candidates suggested.","","http://camda2022.bioinf.jku.at/contest_dataset#disease_maps_to_modelling_covid-19","completed","\N","","","\N","2022-05-20","2023-06-23 00:00:00","2023-11-14 19:23:47" +"149","crowdsourced-evaluation-of-inchi-based-tautomer-identification","Crowdsourced Evaluation of InChI-based Tautomer Identification","Test a modified InChi algorithm","This challenge focuses on the International Chemical Identifier (InChI), which was developed and is maintained under the auspices of the International Union of Pure and Applied Chemistry (IUPAC) and the InChI Trust. The InChI Trust, the IUPAC Working Group on Tautomers, and the U.S. Food and Drug Administration (FDA) call on the scientific community dealing with chemical repositories/data sets and analytics of compounds to test the recently modified InChI algorithm, which was designed for advanced recognition of tautomers. Participants will evaluate this algorithm against real chemical samples in this Crowdsourced Evaluation of InChI-based Tautomer Identification.","","https://precision.fda.gov/challenges/29","completed","6","","","2022-11-01","2023-03-01","2023-06-23 00:00:00","2023-11-14 19:21:10" +"150","nctr-indel-calling-from-oncopanel-sequencing-challenge-phase-2","NCTR Indel Calling from Oncopanel Sequencing Challenge Phase 2","Calling from oncopanel sequencing data","The high value of clinically actionable information obtained by oncopanel sequencing makes it a crucial tool for precision oncology[1,2]. With the surge in availability of oncopanels, it is critical to ensure that they have been thoroughly tested and are properly used. FDA has initiated the Sequencing Quality Control phase II (SEQC2) project[3] to develop standard analysis protocols and quality control metrics for fit-for-purpose use of Next Generation Sequencing (NGS) data including oncopanel sequencing to inform regulatory science research and precision medicine. The Oncopanel Sequencing Working Group of FDA-led SEQC2 has developed a reference sample[4] suitable for benchmarking oncopanels and comprehensively assessed the analytical performance of several oncopanels[1,2]. The genomic deoxyribonucleic acid (gDNA) reference sample was derived from ten Universal Human Reference RNA (UHRR, Agilent Technologies, Inc) cell-lines and made publicly available by Agilent. Substantial gen...","","https://precision.fda.gov/challenges/22","completed","6","","","2022-07-11","2022-07-26","2023-06-23 00:00:00","2023-11-15 22:53:48" +"151","nctr-indel-calling-from-oncopanel-sequencing-data-challenge-phase-1","NCTR Indel Calling from Oncopanel Sequencing Data Challenge Phase 1","Identify indels in oncopanel sequencing datasets","The high value of clinically actionable information obtained by oncopanel sequencing makes it a crucial tool for precision oncology[1,2]. With the surge in availability of oncopanels, it is critical to ensure that they have been thoroughly tested and are properly used. FDA has initiated the Sequencing Quality Control phase II (SEQC2) project[3] to develop standard analysis protocols and quality control metrics for fit-for-purpose use of Next Generation Sequencing (NGS) data including oncopanel sequencing to inform regulatory science research and precision medicine. The Oncopanel Sequencing Working Group of FDA-led SEQC2 has developed a reference sample[4] suitable for benchmarking oncopanels and comprehensively assessed the analytical performance of several oncopanels[1,2]. The genomic deoxyribonucleic acid (gDNA) reference sample was derived from ten Universal Human Reference RNA (UHRR, Agilent Technologies, Inc) cell-lines and made publicly available by Agilent. Substantial gen...","","https://precision.fda.gov/challenges/21","completed","6","","","2022-05-02","2022-07-08","2023-06-23 00:00:00","2023-11-14 19:24:33" +"152","vha-innovation-ecosystem-and-precisionfda-covid-19-risk-factor-modeling-challenge-phase-2","VHA Innovation Ecosystem and precisionFDA COVID-19 Risk Factor Modeling Challenge Phase 2","Validate the top performing models on two additional VA sites' data","The novel coronavirus disease 2019 (COVID-19) is a respiratory disease caused by a new type of coronavirus, known as “severe acute respiratory syndrome coronavirus 2,” or SARS-CoV-2. On March 11, 2020, the World Health Organization (WHO) declared the outbreak a global pandemic. As of January 22nd, 2022, the Johns Hopkins University COVID-19 dashboard reports over 338 million total confirmed cases worldwide. Although most people have mild to moderate symptoms, the disease can cause severe medical complications leading to death in some people. The Centers for Disease Control and Prevention (CDC) have identified several risk factors for severe COVID-19 illness, including people 65 years and older, individuals living in nursing homes or long-term care facilities, and those with serious underlying medical conditions. The Veteran population has a higher prevalence of several of the known risk factors for severe COVID-19 illness, such as advanced age, heart disease, and diabetes. Identif...","","https://precision.fda.gov/challenges/20","completed","6","","","2021-04-14","2022-01-28","2023-06-23 00:00:00","2023-11-14 19:24:37" +"153","tumor-mutational-burden-tmb-challenge-phase-2","Tumor Mutational Burden (TMB) Challenge Phase 2","Evaluating various computational pipelines for TMB estimation","Tumor mutational burden (TMB) is generally defined as the number of mutations detected in a patient's tumor sample per megabase of DNA sequenced. However different algorithms use different methods for calculating TMB. Mutations in genes in tumor cells may lead to the creation of neoantigens, which have the potential to activate an immune system response against the tumor, and the likelihood of an immune system response may increase with the number of mutations. Thus, TMB is a biomarker for some immunotherapy drugs, called immune checkpoint inhibitors, such as those that target the PD-1 and PD-L1 pathways (Chan et al., 2019). An outstanding problem is the lack of standardization for TMB calculation and reporting between different assays. To address this problem, the Friends of Cancer Research convened a working group of industry and regulatory stakeholders to develop guidance and tools for TMB harmonization. Results from the first phase of this effort were presented at AACR 2020 (s...","","https://precision.fda.gov/challenges/18","completed","6","","","2021-07-19","2021-09-12","2023-06-23 00:00:00","2023-11-14 19:24:44" +"154","predicting-gene-expression-using-millions-of-random-promoter-sequences","Predicting Gene Expression Using Millions of Random Promoter Sequences","Decoding gene expression regulation to understand disease","Decoding how gene expression is regulated is critical to understanding disease. Regulatory DNA is decoded by the cell in a process termed “cis-regulatory logic”, where proteins called Transcription Factors (TFs) bind to specific DNA sequences within the genome and work together to produce as output a level of gene expression for downstream adjacent genes. This process is exceedingly complex to model as a large number of parameters is needed to fully describe the process (see Rationale, de Boer et al. 2020; Zeitingler J. 2020). Understanding the cis-regulatory logic of the human genome is an important goal and would provide insight into the origins of many diseases. However, learning models from human data is challenging due to limitations in the diversity of sequences present within the human genome (e.g. extensive repetitive DNA), the vast number of cell types that differ in how they interpret regulatory DNA, limited reporter assay data, and substantial technical biases present i...","","https://www.synapse.org/#!Synapse:syn28469146/wiki/617075","completed","1","","","2022-06-15","2022-08-07","2023-06-23 00:00:00","2023-10-14 05:40:21" +"155","brats-2023","BraTS 2023","Benchmarking brain tumor segmentation with expanded dataset","The International Brain Tumor Segmentation (BraTS) challenge. BraTS, since 2012, has focused on the generation of a benchmarking environment and dataset for the delineation of adult brain gliomas. The focus of this year’s challenge remains the generation of a common benchmarking environment, but its dataset is substantially expanded to ~4,500 cases towards addressing additional i) populations (e.g., sub-Saharan Africa patients), ii) tumors (e.g., meningioma), iii) clinical concerns (e.g., missing data), and iv) technical considerations (e.g., augmentations). Specifically, the focus of BraTS 2023 is to identify the current state-of-the-art algorithms for addressing (Task 1) the same adult glioma population as in the RSNA-ANSR-MICCAI BraTS challenge, as well as (Task 2) the underserved sub-Saharan African brain glioma patient population, (Task 3) intracranial meningioma, (Task 4) brain metastasis, (Task 5) pediatric brain tumor patients, (Task 6) global & local missing data, (Task 7...","","https://www.synapse.org/brats","completed","1","","","2023-06-01","2023-08-25","2023-06-23 00:00:00","2023-10-26 23:20:21" +"156","cagi7","CAGI7","The seventh round of CAGI","There have been six editions of CAGI experiments, held between 2010 and 2022. The seventh round of CAGI is planned to take place over the Summer of 2024.","","https://genomeinterpretation.org/challenges.html","upcoming","1","","","\N","\N","2023-08-04 21:47:38","2023-11-20 20:19:08" +"157","casp15","CASP15","Establish the state-of-art in modeling proteins and protein complexes","CASP14 (2020) saw an enormous jump in the accuracy of single protein and domain models such that many are competitive with experiment. That advance is largely the result of the successful application of deep learning methods, particularly by the AlphaFold and, since that CASP, RosettaFold. As a consequence, computed protein structures are becoming much more widely used in a broadening range of applications. CASP has responded to this new landscape with a revised set of modeling categories. Some old categories have been dropped (refinement, contact prediction, and aspects of model accuracy estimation) and new ones have been added (RNA structures, protein ligand complexes, protein ensembles, and accuracy estimation for protein complexes). We are also strengthening our interactions with our partners CAPRI and CAMEO. We hope that these changes will maximize the insight that CASP15 provides, particularly in new applications of deep learning.","","https://predictioncenter.org/casp15/index.cgi","completed","\N","","","2022-04-18","\N","2023-08-04 21:52:12","2023-09-28 23:09:59" +"158","synthrad2023","SynthRAD2023","Automatic generation of synthetic computed tomography (sCT) for radiotherapy","This challenge aims to provide the first platform offering public data evaluation metrics to compare the latest developments in sCT generation methods. The accepted challenge design approved by MICCAI can be found at https://doi.org/10.5281/zenodo.7746019. A type 2 challenge will be run, where the participant needs to submit their algorithm packaged in a docker both for validation and test.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/678/SynthRAD_square_logo_MnAqldI.png","https://synthrad2023.grand-challenge.org/","active","5","","","2023-04-01","2028-09-20","2023-08-04 21:54:31","2024-01-31 22:38:07" +"159","synthetic-data-for-instrument-segmentation-in-surgery-syn-iss","Synthetic Data for Instrument Segmentation in Surgery (Syn-ISS)","Surgical instrument segmentation with synthetic data","A common limitation noted by the surgical data science community is the size of datasets and the resources needed to generate training data at scale for building reliable and high-performing machine learning models. Beyond unsupervised and self-supervised approaches another solution within the broader machine learning community has been a growing volume of literature in the use of synthetic data (simulation) for training algorithms than can be applied to real world data. Synthetic data has multiple benefits like free groundtruth at large scale, possibility to collect larger sample of rare events, include anatomical variations, etc. A first step towards proving the validity of using synthetic data for real world applications is to demonstrate the feasibility within the simulation world itself. Our proposed challenge is to train machine learning methods for instrument segmentation using synthetic datasets and test their performance on synthetic datasets. That is, the challenge parti...","","https://www.synapse.org/#!Synapse:syn50908388/wiki/620516","completed","1","","","2023-07-19","2023-09-07","2023-08-04 23:49:44","2023-12-06 07:16:20" +"160","pitvis","PitVis","Surgical workflow and instrument recognition in endonasal surgery","The pituitary gland, found just off the base of the brain, is commonly known as “the master gland”, performing essential functions required for sustaining human life. Clinically relevant tumours that have grown on the pituitary gland have an estimated prevalence of 1 in 1000 of the population, and if left untreated can be life-limiting. The “gold standard” treatment is endoscopic pituitary surgery, where the tumour is directly removed by entering through a nostril. This surgery is particularly challenging due to the small working space which limits both vision and instrument manoeuvrability and thus can lead to poor surgical technique causing adverse outcomes for the patient. Computer-assisted intervention can help overcome these challenges by providing guidance for senior surgeons and operative staff during surgery, and for junior surgeons during training.","","https://www.synapse.org/#!Synapse:syn51232283/wiki/","completed","1","","","2023-06-29","2023-09-10","2023-08-04 23:58:01","2023-10-26 23:20:30" +"161","mvseg2023","MVSEG2023","Single frame 3D trans-esophageal echocardiography","Mitral valve (MV) disease is a common pathologic problem occurring in approximately 2 % of the general population but climbing to 10 % in those over the age of 75. The preferred intervention for mitral regurgitation is valve repair, due to superior patient outcomes compared to those following valve replacement. Mitral valve interventions are technically challenging due to the functional and anatomical complexity of mitral pathologies. Repair must be tailored to the patient-specific anatomy and pathology, which requires considerable expert training and experience. Automatic segmentation of the mitral valve leaflets from 3D transesophageal echocardiography (TEE) may play an important role in treatment planning, as well as physical and computational modelling of patient-specific valve pathologies and potential repair approaches. This may have important implications in the drive towards personalized care and has the potential to impact clinical outcomes for those undergoing mitral val...","","https://www.synapse.org/#!Synapse:syn51186045/wiki/621356","completed","1","","","2023-05-29","2023-08-07","2023-08-05 0-04-36","2023-11-14 19:25:13" +"162","crossmoda23","crossMoDA23","Medical imaging benchmark for unsupervised domain adaptation","Domain Adaptation (DA) has recently raised strong interest in the medical imaging community. By encouraging algorithms to be robust to unseen situations or different input data domains, Domain Adaptation improves the applicability of machine learning approaches to various clinical settings. While a large variety of DA techniques has been proposed, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly address single-class problems. To tackle these limitations, the crossMoDA challenge introduced the first large and multi-class dataset for unsupervised cross-modality Domain Adaptation. From an application perspective, crossMoDA focuses on MRI segmentation for Vestibular Schwannoma. Compared to the previous crossMoDA instance, which made use of multi-institutional data acquired in controlled conditions for radiosurgery planning and focused on a 2 class segmentation task (tumour and cochlea), the...","","https://www.synapse.org/#!Synapse:syn51236108/wiki/621615","completed","1","","","2023-04-15","2023-07-10","2023-08-05 0-13-23","2023-11-14 19:27:00" +"163","icr-identify-age-related-conditions","ICR - Identifying Age-Related Conditions","Detect conditions with measurements of anonymous characteristics of a subject","The goal of this competition is to predict if a person has any of three medical conditions. You are being asked to predict if the person has one or more of any of the three medical conditions (Class 1), or none of the three medical conditions (Class 0). You will create a model trained on measurements of health characteristics. To determine if someone has these medical conditions requires a long and intrusive process to collect information from patients. With predictive models, we can shorten this process and keep patient details private by collecting key characteristics relative to the conditions, then encoding these characteristics.","","https://www.kaggle.com/competitions/icr-identify-age-related-conditions","completed","8","","","2023-05-11","2023-08-10","2023-08-05 0-32-01","2023-11-14 19:25:37" +"164","cafa-5-protein-function-prediction","CAFA 5: Protein Function Prediction","Predict the biological function of a protein","The goal of this competition is to predict the function of a set of proteins. You will develop a model trained on the amino-acid sequences of the proteins and on other data. Your work will help ​​researchers better understand the function of proteins, which is important for discovering how cells, tissues, and organs work. This may also aid in the development of new drugs and therapies for various diseases.","","https://www.kaggle.com/competitions/cafa-5-protein-function-prediction","completed","8","","operation_1777","2023-04-18","2023-08-21","2023-08-05 5-18-40","2023-10-19 00:13:14" +"165","rsna-2023-abdominal-trauma-detection","RSNA 2023 Abdominal Trauma Detection","Detect and classify traumatic abdominal injuries","Traumatic injury is the most common cause of death in the first four decades of life and a major public health problem around the world. There are estimated to be more than 5 million annual deaths worldwide from traumatic injury. Prompt and accurate diagnosis of traumatic injuries is crucial for initiating appropriate and timely interventions, which can significantly improve patient outcomes and survival rates. Computed tomography (CT) has become an indispensable tool in evaluating patients with suspected abdominal injuries due to its ability to provide detailed cross-sectional images of the abdomen. Interpreting CT scans for abdominal trauma, however, can be a complex and time-consuming task, especially when multiple injuries or areas of subtle active bleeding are present. This challenge seeks to harness the power of artificial intelligence and machine learning to assist medical professionals in rapidly and precisely detecting injuries and grading their severity. The development...","","https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection","completed","8","","","2023-07-26","2023-10-13","2023-08-05 5-24-09","2023-09-28 23:14:12" +"166","hubmap-hacking-the-human-vasculature","HuBMAP: Hacking the Human Vasculature","Microvascular structures from healthy human kidney tissue images","The goal of this competition is to segment instances of microvascular structures, including capillaries, arterioles, and venules. You'll create a model trained on 2D PAS-stained histology images from healthy human kidney tissue slides. Your help in automating the segmentation of microvasculature structures will improve researchers' understanding of how the blood vessels are arranged in human tissues.","","https://www.kaggle.com/competitions/hubmap-hacking-the-human-vasculature","completed","8","","","2023-05-22","2023-07-31","2023-08-05 5-31-12","2023-11-14 19:25:45" +"167","amp-parkinsons-disease-progression-prediction","AMP(R)-Parkinson's Disease Progression Prediction","Predict clinical and molecular progression of the disease","The goal of this competition is to predict MDS-UPDR scores, which measure progression in patients with Parkinson's disease. The Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is a comprehensive assessment of both motor and non-motor symptoms associated with Parkinson's. You will develop a model trained on data of protein and peptide levels over time in subjects with Parkinson’s disease versus normal age-matched control subjects. Your work could help provide important breakthrough information about which molecules change as Parkinson’s disease progresses.","","https://www.kaggle.com/competitions/amp-parkinsons-disease-progression-prediction","completed","8","","","2023-02-16","2023-05-18","2023-08-05 5-37-12","2023-12-06 22:44:19" +"168","open-problems-multimodal","Open Problems -Multimodal Single-Cell Integration","Predict how DNA, RNA & protein measurements co-vary in single cells","The goal of this competition is to predict how DNA, RNA, and protein measurements co-vary in single cells as bone marrow stem cells develop into more mature blood cells. You will develop a model trained on a subset of 300,000-cell time course dataset of CD34+ hematopoietic stem and progenitor cells (HSPC) from four human donors at five time points generated for this competition by Cellarity, a cell-centric drug creation company. In the test set, taken from an unseen later time point in the dataset, competitors will be provided with one modality and be tasked with predicting a paired modality measured in the same cell. The added challenge of this competition is that the test data will be from a later time point than any time point in the training data. Your work will help accelerate innovation in methods of mapping genetic information across layers of cellular state. If we can predict one modality from another, we may expand our understanding of the rules governing these complex re...","","https://www.kaggle.com/competitions/open-problems-multimodal","completed","8","","","2022-08-15","2022-11-15","2023-08-05 5-43-25","2023-10-10 19:52:41" +"169","multi-atlas-labeling-beyond-the-cranial-vault","Multi-Atlas Labeling Beyond the Cranial Vault","Innovative multi-atlas labeling for soft tissue segmentation on clinical CT","Multi-atlas labeling has proven to be an effective paradigm for creating segmentation algorithms from training data. These approaches have been extraordinarily successful for brain and cranial structures (e.g., our prior MICCAI workshops-MLSF’11, MAL’12, SATA’13). After the original challenges closed, the data continue to drive scientific innovation; 144 groups have registered for the 2012 challenge (brain only) and 115 groups for the 2013 challenge (brain/heart/canine leg). However, innovation in application outside of the head and to soft tissues has been more limited. This workshop will provide a snapshot of the current progress in the field through extended discussions and provide researchers an opportunity to characterize their methods on a newly created and released standardized dataset of abdominal anatomy on clinically acquired CT. The datasets will be freely available both during and after the challenge. We have two separate new challenges-abdomen and cervix on routinely ...","","https://www.synapse.org/#!Synapse:syn3193805/wiki/89480","active","1","","","2015-04-15","\N","2023-08-07 20:21:22","2023-10-10 19:52:39" +"170","hubmap-organ-segmentation","HuBMAP + HPA: Hacking the Human Body","Segment multi-organ functional tissue units","In this competition, you’ll identify and segment functional tissue units (FTUs) across five human organs. You'll build your model using a dataset of tissue section images, with the best submissions segmenting FTUs as accurately as possible. If successful, you'll help accelerate the world’s understanding of the relationships between cell and tissue organization. With a better idea of the relationship of cells, researchers will have more insight into the function of cells that impact human health. Further, the Human Reference Atlas constructed by HuBMAP will be freely available for use by researchers and pharmaceutical companies alike, potentially improving and prolonging human life.","","https://www.kaggle.com/competitions/hubmap-organ-segmentation","completed","8","","","2022-06-22","2022-09-22","2023-08-08 16:30:22","2023-11-02 18:44:27" +"171","hubmap-kidney-segmentation","HuBMAP: Hacking the Kidney","Identify glomeruli in human kidney tissue images","This competition, “Hacking the Kidney, starts by mapping the human kidney at single cell resolution. Your challenge is to detect functional tissue units (FTUs) across different tissue preparation pipelines. An FTU is defined as a “three-dimensional block of cells centered around a capillary, such that each cell in this block is within diffusion distance from any other cell in the same block” ([de Bono, 2013](https://www.ncbi.nlm.nih.gov/pubmed/24103658)). The goal of this competition is the implementation of a successful and robust glomeruli FTU detector. You will also have the opportunity to present your findings to a panel of judges for additional consideration. Successful submissions will construct the tools, resources, and cell atlases needed to determine how the relationships between cells can affect the health of an individual. Advancements in HuBMAP will accelerate the world’s understanding of the relationships between cell and tissue organization and function and human health.","","https://www.kaggle.com/competitions/hubmap-kidney-segmentation","completed","8","","","2020-11-16","2021-05-10","2023-08-08 17:31:46","2023-10-12 18:14:16" +"172","ventilator-pressure-prediction","Google Brain: Ventilator Pressure Prediction","Simulate a ventilator connected to a sedated patient's lung","In this competition, you’ll simulate a ventilator connected to a sedated patient's lung. The best submissions will take lung attributes compliance and resistance into account. If successful, you'll help overcome the cost barrier of developing new methods for controlling mechanical ventilators. This will pave the way for algorithms that adapt to patients and reduce the burden on clinicians during these novel times and beyond. As a result, ventilator treatments may become more widely available to help patients breathe.","","https://www.kaggle.com/competitions/ventilator-pressure-prediction","completed","8","","","2021-09-22","2021-11-03","2023-08-08 17:53:33","2023-11-02 18:44:22" +"173","stanford-covid-vaccine","OpenVaccine - COVID-19 mRNA Vaccine Degradation Prediction","Urgent need to bring the COVID-19 vaccine to mass production","In this competition, we are looking to leverage the data science expertise of the Kaggle community to develop models and design rules for RNA degradation. Your model will predict likely degradation rates at each base of an RNA molecule, trained on a subset of an Eterna dataset comprising over 3000 RNA molecules (which span a panoply of sequences and structures) and their degradation rates at each position. We will then score your models on a second generation of RNA sequences that have just been devised by Eterna players for COVID-19 mRNA vaccines. These final test sequences are currently being synthesized and experimentally characterized at Stanford University in parallel to your modeling efforts--Nature will score your models!","","https://www.kaggle.com/competitions/stanford-covid-vaccine","completed","8","","","2020-09-10","2020-10-06","2023-08-08 18:06:17","2023-10-12 18:14:27" +"174","openvaccine","OpenVaccine","To develop mRNA vaccines stable enough to be deployed to everyone in the world","mRNA vaccines are a relatively new technology that have come into the limelight with the onset of COVID-19. They were the first COVID-19 vaccines to start clinical trials (initially formulated in a matter of days) and the first to be approved and distributed. mRNA vaccines have the potential to transform immunization, being significantly faster to formulate and produce, cheaper, and more effective-including against mutant strains. However, there is one key bottleneck to their widespread viability and our ability to immunize the entire world-poor refrigerator stability in prefilled syringes. The OpenVaccine challenge aims to allow a worldwide community of game players to create an enhanced vaccine to be injected into millions of people. The challenge-design an mRNA that codes for the same amino acid sequence of the spike protein, but is 2x-10x+ more stable. Through a number of academic partnerships and the launch of a Kaggle machine learning challenge to create best-in-class algori...","","https://eternagame.org/challenges/10845741","completed","13","https://doi.org/10.1038/s41467-022-28776-w","","\N","2021-12-12","2023-08-08 18:22:49","2023-11-14 19:26:10" +"175","opentb","OpenTB","Detect a gene sequence found to be present only in people with active TB","OpenTB used a recently reported gene signature for active tuberculosis based on three RNAs in the blood. This signature could form the basis for a fast, color-based test for TB, similar to an over-the-counter pregnancy test. What was needed was a sensor that could detect the concentrations of three RNAs, carry out the needed calculation, and report the result by binding another molecule. Over four rounds, players designed RNA sensors that can do the math on these 3 genes. Through experimental feedback, they honed their skills and techniques, which resulted in the creation of multiple designs that have been shown to be successful. These findings are being prepared to be published, and future work will be done to develop diagnostic devices integrating these designs","","https://eternagame.org/challenges/10845742","completed","13","","","2016-05-04","2018-04-15","2023-08-08 18:43:09","2023-11-14 19:32:31" +"176","opencrispr","OpenCRISPR","Discover RNAs to make gene editing more precisely controllable","CRISPR gene editing is a RNA-based method that can target essentially any gene in a living organism for genetic changes. Since its first demonstration, CRISPR has been revolutionizing biology and promises to change how we tackle numerous human diseases from malaria to cancer. Stanford's Center for Personal Dynamic Regulomes and UC Berkeley's Innovative Genomics Institute have challenged Eterna players to solve a remaining hurdle in making this technology safe for use. Scientists want the power to turn on and off CRISPR on demand with small molecules. This is almost a perfect match to the small-molecule switches that the Eterna community has worked on. In fact, the MS2 RNA hairpin often used in Eterna is routinely used to recruit new functionality to CRISPR complexes through other molecules tethered to the MS2 protein. The puzzles began with OpenCRISPR Controls, looking for solutions to lock in or lock out the MS2 RNA hairpin within a special loop in the CRISPR RNA. We hope the res...","","https://eternagame.org/challenges/10845743","completed","13","https://doi.org/10.1021/acssynbio.9b00142","","2017-08-26","\N","2023-08-08 18:43:14","2023-11-14 19:33:25" +"177","openknot","OpenKnot","Build a diverse library of RNAs that form pseudoknot structures","RNA pseudoknots have significant biological importance in various processes. They participate in gene regulation by influencing translation initiation or termination in mRNA molecules. Pseudoknots also play a role in programmed ribosomal frameshifting, leading to the production of different protein products from a single mRNA. RNA viruses, including SARS-CoV-2 and Dengue virus, utilize pseudoknots to regulate their replication and control the synthesis of viral proteins. Additionally, certain RNA molecules with pseudoknot structures exhibit enzymatic activity, acting as ribozymes and catalyzing biochemical reactions. These functions highlight the crucial role of RNA pseudoknots in gene expression, proteomic diversity, viral replication, and enzymatic processes. Several unanswered scientific questions surround RNA pseudoknots. One key area of inquiry is understanding the folding pathways of pseudoknots and how they form from linear RNA sequences. Elucidating the structural dynamics...","","https://eternagame.org/challenges/11843006","active","13","","","2022-06-17","\N","2023-08-08 18:43:22","2023-11-14 19:32:46" +"178","openaso","OpenASO","Design principles for RNA-based therapeutics","The DNA genome is the blueprint for building and operating cells, but this information must be decoded into RNA molecules to be useful. Transcription is the process of decoding DNA genomic information into RNA, resulting in RNA transcripts. Genes are specific sequences of DNA that contain information to produce a specific RNA transcript. The fate of most mRNA molecules in the cell is to be translated by ribosomes into protein molecules. However, mRNA splicing is a crucial step that occurs between the formation of an RNA transcript and protein translation. This step is essential because genes contain non-protein coding introns and protein-coding exons. Splicing removes introns and joins exons to produce a mature mRNA molecule that can be decoded into the correct protein molecule. When the splicing process is corrupted due to genetic mutations, the resulting RNA can become toxic, leading to the synthesis of non-functional proteins or no protein at all, causing various human diseases...","","https://eternagame.org/challenges/11546273","active","13","","","2023-02-20","\N","2023-08-08 18:43:25","2023-11-14 19:32:51" +"179","openribosome","OpenRibosome","Learn and change the ribosome's RNAs","Our modern world has many challenges-challenges like climate change, increasing waste production, and human health. Imagine-we could replace petrochemistry with biology, single-use plastics with selectively degradable polymers, broad chemotherapeutics with targeted medicines for fighting specific cancer cells, and complex health equipment with point-of-care diagnostics. These innovations and many more can empower us to confront the challenges affecting humanity, our world, and beyond. But how do we actually create these smart materials and medicines? Is it possible to do so by repurposing one of Nature's molecular machines? We think we can. The answer? Customized ribosomes. In Nature, ribosomes are the catalysts for protein assembly. And proteins are more or less similar, chemically, to the smart materials and medicines we want to synthesize. If we could modify ribosomes to build polymers with diverse components-beyond the canonical amino acids us","","https://eternagame.org/challenges/11043833","active","13","https://doi.org/10.1038/s41467-023-35827-3","","2019-01-31","\N","2023-08-08 18:43:27","2023-11-14 19:33:01" +"180","lish-moa","Mechanisms of Action (MoA) Prediction","Improve the algorithm that classifies drugs based on their biological activity","Can you improve the algorithm that classifies drugs based on their biological activity?","","https://www.kaggle.com/competitions/lish-moa","completed","8","","","2020-09-03","2020-11-30","2023-08-08 19:09:31","2023-11-14 19:33:49" +"181","recursion-cellular-image-classification","Recursion Cellular Image Classification","CellSignal-Disentangling biological signal in cellular images","This competition will have you disentangling experimental noise from real biological signals. Your entry will classify images of cells under one of 1,108 different genetic perturbations. You can help eliminate the noise introduced by technical execution and environmental variation between experiments. If successful, you could dramatically improve the industry’s ability to model cellular images according to their relevant biology. In turn, applying AI could greatly decrease the cost of treatments, and ensure these treatments get to patients faster.","","https://www.kaggle.com/competitions/recursion-cellular-image-classification","completed","8","","","2019-06-27","2019-09-26","2023-08-08 19:38:42","2023-11-14 19:34:11" +"182","tlvmc-parkinsons-freezing-gait-prediction","Parkinson's Freezing of Gait Prediction","Event detection from wearable sensor data","The goal of this competition is to detect freezing of gait (FOG), a debilitating symptom that afflicts many people with Parkinson’s disease. You will develop a machine learning model trained on data collected from a wearable 3D lower back sensor. Your work will help researchers better understand when and why FOG episodes occur. This will improve the ability of medical professionals to optimally evaluate, monitor, and ultimately, prevent FOG events.","","https://www.kaggle.com/competitions/tlvmc-parkinsons-freezing-gait-prediction","completed","8","","","2023-03-09","2023-06-08","2023-08-08 19:47:54","2023-10-10 19:53:08" +"183","chaimeleon","CHAIMELEON Open Challenges","AI-powered solutions driving innovation in cancer diagnosis and treatment","The CHAIMELEON Open Challenges is a competition designed to train and refine AI models to answer clinical questions about five types of cancer-prostate, lung, breast, colon, and rectal. Participants are challenged to collaborate and develop innovative AI-powered solutions that can significantly impact cancer diagnosis, management, and treatment. They will be evaluated considering a balance between the performance of their AI algorithms to predict different clinical endpoints such as disease staging, treatment response or progression free survival and their trustworthiness. The challenges are open to the whole scientific and tech community interested in AI. They are a unique opportunity to showcase how AI can be used to advance medical research and improve patient outcomes within the CHAIMELEON project.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/744/Logo_Grand_Challenge_-_2.png","https://chaimeleon.grand-challenge.org/","completed","5","","","2023-11-02","2023-11-30","2023-08-09 17:13:09","2023-11-14 19:34:50" +"184","topcow23","Topology-Aware Anatomical Segmentation of the Circle of Willis","Segment the Circle of Willis (CoW) vessel components for both CTA and MRA","The aim of the challenge is to extract the CoW angio-architecture from 3D angiographic imaging by segmentation of the vessel components. There are two sub-tasks-binary segmentation of CoW vessels, and multi-class CoW anatomical segmentation. We release a new dataset of joint-modalities, CTA and MRA of the same patient cohort, both with annotations of the anatomy of CoW. Our challenge has two tracks for the same segmentation task, namely CTA track and MRA track. We made use of the clinical information from both modalities during our annotation. And participants can pick whichever modality they want, both CTA and MRA, and choose to tackle the task for either modality.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/733/TopCow_logo.jpg","https://topcow23.grand-challenge.org/","completed","5","","","2023-08-20","2023-09-25","2023-08-09 17:16:22","2024-01-31 22:42:32" +"185","circle-of-willis-intracranial-artery-classification-and-quantification-challenge-2023","Circle of Willis Intracranial Artery Classification and Quantification Challenge 2023","Classify the circle of Willis (CoW) configuration and quantification","The purpose of this challenge is to compare automatic methods for classification of the circle of Willis (CoW) configuration and quantification of the CoW major artery diameters and bifurcation angles.","","https://crown.isi.uu.nl/","completed","\N","","","2023-05-01","2023-08-15","2023-08-09 22:13:24","2023-09-28 23:24:54" +"186","making-sense-of-electronic-health-record-ehr-race-and-ethnicity-data","Making Sense of Electronic Health Record (EHR) Race and Ethnicity Data","Make sense of electronic health record race and ethnicity data","The urgency of the coronavirus disease 2019 (COVID-19) pandemic has heightened interest in the use of real-world data (RWD) to obtain timely information about patients and populations and has focused attention on EHRs. The pandemic has also heightened awareness of long-standing racial and ethnic health disparities along a continuum from underlying social determinants of health, exposure to risk, access to insurance and care, quality of care, and responses to treatments. This highlighted the potential that EHRs can be used to describe and contribute to our understanding of racial and ethnic health disparities and their solutions. The OMB Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity provides minimum standards for maintaining, collecting, and presenting data on race and ethnicity for all Federal reporting purposes, and defines the two separate constructs of race and ethnicity.","","https://precision.fda.gov/challenges/30","completed","6","","","2023-05-31","2023-06-23","2023-08-10 18:28:06","2023-11-14 19:34:58" +"187","the-veterans-cardiac-health-and-ai-model-predictions-v-champs","The Veterans Cardiac Health and AI Model Predictions (V-CHAMPS)","Predict cardiovascular health related outcomes in veterans","To better understand the risk and protective factors in the Veteran population, the VHA IE and its collaborating partners are calling upon the public to develop AI/ML models to predict cardiovascular health outcomes, including readmission and mortality, using synthetically generated Veteran health records. The Challenge consists of two Phases-Phase 1 is focused on synthetic data. In this Phase of the Challenge, AI/ML models will be developed by Challenge participants and trained and tested on the synthetic data sets provided to them, with a view towards predicting outcome variables for Veterans who have been diagnosed with chronic heart failure (please note that in Phase 1, the data is synthetic Veteran health records). Phase 2 will focus on validating and further exploring the limits of the AI/ML models. During this Phase, high-performing AI/ML models from Phase 1 will be brought into the VA system and validated on the real-world Veterans health data within the VHA. These models...","","https://precision.fda.gov/challenges/31","completed","6","","","2023-05-25","2023-08-02","2023-08-10 21:41:10","2023-11-14 19:35:53" +"188","predicting-high-risk-breast-cancer-phase-1","Predicting High Risk Breast Cancer - Phase 1","Predicting High Risk Breast Cancer-a Nightingale OS & AHLI data challenge","Every year, 40 million women get a mammogram; some go on to have an invasive biopsy to better examine a concerning area. Underneath these routine tests lies a deep—and disturbing—mystery. Since the 1990s, we have found far more ‘cancers’, which has in turn prompted vastly more surgical procedures and chemotherapy. But death rates from metastatic breast cancer have hardly changed. When a pathologist looks at a biopsy slide, she is looking for known signs of cancer-tubules, cells with atypical looking nuclei, evidence of rapid cell division. These features, first identified in 1928, still underlie critical decisions today-which women must receive urgent treatment with surgery and chemotherapy? And which can be prescribed “watchful waiting”, sparing them invasive procedures for cancers that would not harm them? There is already evidence that algorithms can predict which cancers will metastasize and harm patients on the basis of the biopsy image. Fascinatingly, these algorithms also h...","","https://app.nightingalescience.org/contests/3jmp2y128nxd","completed","15","","","2022-06-01","2023-01-12","2023-08-22 17:07:00","2023-10-12 17:55:10" +"189","predicting-high-risk-breast-cancer-phase-2","Predicting High Risk Breast Cancer - Phase 2","Predicting High Risk Breast Cancer-a Nightingale OS & AHLI data challenge","Every year, 40 million women get a mammogram; some go on to have an invasive biopsy to better examine a concerning area. Underneath these routine tests lies a deep—and disturbing—mystery. Since the 1990s, we have found far more ‘cancers’, which has in turn prompted vastly more surgical procedures and chemotherapy. But death rates from metastatic breast cancer have hardly changed. When a pathologist looks at a biopsy slide, she is looking for known signs of cancer-tubules, cells with atypical looking nuclei, evidence of rapid cell division. These features, first identified in 1928, still underlie critical decisions today-which women must receive urgent treatment with surgery and chemotherapy? And which can be prescribed “watchful waiting”, sparing them invasive procedures for cancers that would not harm them? There is already evidence that algorithms can predict which cancers will metastasize and harm patients on the basis of the biopsy image. Fascinatingly, these algorithms also...","","https://app.nightingalescience.org/contests/vd8g98zv9w0p","completed","15","","","2023-02-03","2023-05-13","2023-08-22 17:07:01","2023-10-12 17:55:08" +"190","dream-2-in-silico-network-inference","DREAM 2 - In Silico Network Inference","Predict the connectivity and properties of in-silico networks","Three in-silico networks were created and endowed with a dynamics that simulate biological interactions. The challenge consists of predicting the connectivity and some of the properties of one or more of these three networks.","","https://www.synapse.org/#!Synapse:syn2825394/wiki/71150","completed","1","","","2007-03-25","\N","2023-08-24 18:54:05","2023-10-12 17:55:03" +"191","dream-3-in-silico-network-challenge","DREAM 3 - In Silico Network Challenge","Reverse engineering of gene networks from biological data","The goal of the in silico challenges is the reverse engineering of gene networks from steady state and time series data. Participants are challenged to predict the directed unsigned network topology from the given in silico generated gene topic_3170sets.","","https://www.synapse.org/#!Synapse:syn2853594/wiki/71567","completed","1","https://doi.org/10.1089/cmb.2008.09TT","","2008-06-09","\N","2023-08-25 16:43:41","2023-11-14 19:35:58" +"192","dream-4-in-silico-network-challenge","DREAM 4 - In Silico Network Challenge","Reverse engineer gene regulatory networks","The goal of the in silico network challenge is to reverse engineer gene regulation networks from simulated steady-state and time-series data. Participants are challenged to infer the network structure from the given in silico gene topic_3170sets. Optionally, participants may also predict the response of the networks to a set of novel perturbations that were not included in the provided datasets.","","https://www.synapse.org/#!Synapse:syn3049712/wiki/74628","completed","1","https://doi.org/10.1073/pnas.0913357107","","2009-06-09","\N","2023-08-25 16:43:42","2023-11-14 19:36:02" +"193","dream-5-network-inference-challenge","DREAM 5 - Network Inference Challenge","Reverse engineer gene regulatory networks","The goal of this Network Inference Challenge is to reverse engineer gene regulatory networks from gene topic_3170sets. Participants are given four microarray compendia and are challenged to infer the structure of the underlying transcriptional regulatory networks. Three of the four compendia were obtained from microorganisms, some of which are pathogens of clinical relevance. The fourth compendium is based on an in-silico (i.e., simulated) network. Each compendium consists of hundreds of microarray experiments, which include a wide range of genetic, drug, and environmental perturbations (or in the in-silico network case, simulations thereof). Network predictions will be evaluated on a subset of known interactions for each organism, or on the known network for the in-silico case.","","https://www.synapse.org/#!Synapse:syn2787209/wiki/70349","completed","1","https://doi.org/10.1038/nmeth.2016","","2010-06-09","2010-10-31","2023-08-25 16:43:43","2023-11-14 19:36:08" +"194","nlp-sandbox-date-annotation","NLP Sandbox Date Annotation","Identify dates in clinical notes","An NLP Sandbox Date Annotator takes as input a clinical note and outputs a list of predicted date annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","1","https://doi.org/10.7303/syn22277123","","2021-06-04","2023-09-01","2023-08-25 16:45:22","2023-11-15 22:41:56" +"195","nlp-sandbox-person-name-annotation","NLP Sandbox Person Name Annotation","Identify person names in clinical notes","An NLP Sandbox Person Name Annotator takes as input a clinical note and outputs a list of predicted person name annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","1","https://doi.org/10.7303/syn22277123","","2021-06-04","2023-09-01","2023-09-08 16:44:20","2023-09-28 23:59:20" +"196","nlp-sandbox-location-annotation","NLP Sandbox Location Annotation","Identify location information in clinical notes","An NLP Sandbox Location Annotator takes as input a clinical note and outputs a list of predicted location annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","1","https://doi.org/10.7303/syn22277123","","2021-06-04","2023-09-01","2023-09-08 16:44:21","2023-09-28 23:59:21" +"197","nlp-sandbox-contact-annotation","NLP Sandbox Contact Annotation","Identify contact information in clinical notes","An NLP Sandbox contact annotator takes as input a clinical note and outputs a list of predicted contact annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","1","https://doi.org/10.7303/syn22277123","","2021-06-04","2023-09-01","2023-09-08 16:44:22","2023-09-28 23:59:21" +"198","nlp-sandbox-id-annotation","NLP Sandbox ID Annotation","Identify identifiers in clinical notes","An NLP Sandbox ID annotator takes as input a clinical note and outputs a list of predicted ID annotations found in the clinical note.","","https://www.synapse.org/#!Synapse:syn22277123/wiki/609134","completed","1","https://doi.org/10.7303/syn22277123","","2021-06-04","2023-09-01","2023-09-08 16:44:22","2023-09-28 23:59:22" +"199","dream-2-bcl6-transcriptomic-target-prediction","DREAM 2 - BCL6 Transcriptomic Target Prediction","Predict BCL6 transcriptomic targets from biological data","A number of potential transcriptional targets of BCL6, a gene that encodes for a transcription factor active in B cells, have been identified with ChIP-on-chip data and functionally validated by perturbing the BCL6 pathway with CD40 and anti-IgM, and by over-expressing exogenous BCL6 in Ramos cell. We subselected a number of targets found in this way (the gold standard positive set), and added a number decoys (genes that have no evidence of being BCL6 targets, named the gold standard negative set), compiling a list of 200 genes in total. Given this list of 200 genes, the challenge consists of identifying which ones are the true targets and which ones are the decoys, using an independent panel of gene topic_3170.","","https://www.synapse.org/#!Synapse:syn3034857/wiki/","completed","1","https://doi.org/10.1073/pnas.0437996100","","2007-04-19","\N","2023-09-12 21:26:22","2023-10-12 17:53:55" +"200","dream-2-protein-protein-interaction-network-inference","DREAM 2 - Protein-Protein Interaction Network Inference","Predict a protein-protein interaction network of 47 proteins","For many pairs of bait and prey genes, yeast protein-protein interactions were tested in an unbiased fashion using a high saturation, high-stringency variant of the yeast two-hybrid (Y2H) method. A high confidence subset of gene pairs that were found to interact in at least three repetitions of the experiment but that hadn’t been reported in the literature was extracted. There were 47 yeast genes involved in these pairs. Including self interactions, there are a total of 47*48/2 possible pairs of genes that can be formed with these 47 genes. As mentioned above some of these gene pairs were seen to consistently interact in at least three repetitions of the Y2H experiments-these gene pairs form the gold standard positive set. A second set among these gene pairs were seen never to interact in repeated experiments and were not reported as interacting in the literature; we call this the gold standard negative set. Finally in a third set of gene pairs, which we shall call the undecided s...","","https://www.synapse.org/#!Synapse:syn2825374/wiki/","completed","1","https://doi.org/10.1126/science.1158684","","2007-05-24","\N","2023-09-12 21:26:28","2023-10-12 17:54:00" +"201","dream-2-genome-scale-network-inference","DREAM 2 - Genome-Scale Network Inference","Reconstruct genome-scale networks from microarray data","A panel of single-channel microarrays was collected for a particular microorganism, including some already published and some in-print data. The data was appropriately normalized (to the logarithmic scale). The challenge consists of reconstructing a genome-scale transcriptional network for this organism. The accuracy of network inference will be judged using chromatin precipitation and otherwise experimentally verified Transcription Factor (TF)-target interactions.","","https://www.synapse.org/#!Synapse:syn3034894/wiki/74418","completed","1","https://doi.org/10.1371/journal.pbio.0050008","","2007-06-05","2007-10-31","2023-09-12 21:26:34","2023-10-12 17:54:03" +"202","dream-2-synthetic-five-gene-network-inference","DREAM 2 - Synthetic Five-Gene Network Inference","Inferring five-gene networks from synthetic data","A synthetic-biology network consisting of 5 interacting genes was created and transfected to an in-vivo model organism. The challenge consists of predicting the connectivity of the five-gene network from in-vivo measurements.","","https://www.synapse.org/#!Synapse:syn3034869/wiki/74411","completed","1","https://doi.org/10.1016/j.cell.2009.01.055","","2007-06-20","2007-10-31","2023-09-12 21:26:56","2023-10-12 17:54:05" +"203","dream-3-signaling-cascade-identification","DREAM 3 - Signaling Cascade Identification","Inferring signaling cascade dynamics from flow cytometry data","The concentration of four intracellular proteins or phospho-proteins (X1, X2, X3 and X4) participating in a signaling cascade were measured in about 104 cells by antibody staining and flow cytometry. The idea of this challenge is to explore what key aspects of the dynamics and topology of interactions of a signaling cascade can be inferred from incomplete flow cytometry data.","","https://www.synapse.org/#!Synapse:syn3033068/wiki/74362","completed","1","","","2008-06-01","2008-10-31","2023-09-12 21:27:04","2023-10-12 17:54:08" +"204","dream-3-gene-expression-prediction","DREAM 3 - Gene Expression Prediction","Predicting gene expression from gene datasets","Gene expression time course data is provided for four different strains of yeast (S. Cerevisiae), after perturbation of the cells. The challenge is to predict the rank order of induction/repression of a small subset of genes (the prediction targets) in one of the four strains, given complete data for three of the strains, and data for all genes except the prediction targets in the other strain. You are also allowed to use any information that is in the public domain and are expected to be forthcoming about what information was used.","","https://www.synapse.org/#!Synapse:syn3033083/wiki/74369","completed","1","","","2008-06-01","2008-10-31","2023-09-12 21:27:12","2023-10-12 17:54:10" +"205","dream-4-predictive-signaling-network-modelling","DREAM 4 - Predictive Signaling Network Modelling","Cell-type specific high-throughput experimental data","This challenge explores the extent to which our current knowledge of signaling pathways, collected from a variety of cell types, agrees with cell-type specific high-throughput experimental data. Specifically, we ask the challenge participants to create a cell-type specific model of signal transduction using the measured activity levels of signaling proteins in HepG2 cell lines. The model, which can leverage prior information encoded in a generic signaling pathway provided in the challenge, should be biologically interpretable as a network, and capable of predicting the outcome of new experiments.","","https://www.synapse.org/#!Synapse:syn2825304/wiki/71129","completed","1","","","2009-03-09","\N","2023-09-12 21:27:14","2023-10-12 17:54:30" +"206","dream-3-signaling-response-prediction","DREAM 3 - Signaling Response Prediction","Predict missing protein concentrations from a large corpus of measurements","Approximately 10,000 intracellular measurements (fluorescence signals proportional to the concentrations of phosphorylated proteins) and extracellular measurements (concentrations of cytokines released in response to cell stimulation) were acquired in human normal hepatocytes and the hepatocellular carcinoma cell line HepG2 cells. The datasets consist of measurements of 17 phospho-proteins (at 0 min, 30 min, and 3 hrs) and 20 cytokines (at 0 min, 3 hrs, and 24 hrs) in two cell types (normal and cancer) after perturbations to the pathway induced by the combinatorial treatment of 7 stimuli and 7 selective inhibitors.","","https://www.synapse.org/#!Synapse:syn2825325/wiki/","completed","1","https://doi.org/10.1126%2Fscisignal.2002212","","2009-03-09","\N","2023-09-12 21:27:20","2023-10-12 17:54:33" +"207","dream-4-peptide-recognition-domain-prd-specificity-prediction","DREAM 4 - Peptide Recognition Domain (PRD) Specificity Prediction","Predict binding specificity of peptide-antibody interactions","Many important protein-protein interactions are mediated by peptide recognition domains (PRD), which bind short linear sequence motifs in other proteins. For example, SH3 domains typically recognize proline-rich motifs, PDZ domains recognize hydrophobic C-terminal tails, and kinases recognize short sequence regions around a phosphorylatable residue (Pawson, 2003). Given the sequence of the domains, the challenge consists of predicting a position weight matrix (PWM) that describes the specificity profile of each of the given domains to their target peptides. Any publicly accessible peptide specificity information available for the domain may be used.","","https://www.synapse.org/#!Synapse:syn2925957/wiki/72976","completed","1","","","2009-06-01","2009-10-31","2023-09-12 21:27:35","2023-10-12 17:54:35" +"208","dream-5-transcription-factor-dna-motif-recognition-challenge","DREAM 5 - Transcription-Factor, DNA-Motif Recognition Challenge","Predict binding intensities for transcription factors from motifs","Transcription factors (TFs) control the expression of genes through sequence-specific interactions with genomic DNA. Different TFs bind preferentially to different sequences, with the majority recognizing short (6-12 base), degenerate ‘motifs’. Modeling the sequence specificities of TFs is a central problem in understanding the function and evolution of the genome, because many types of genomic analyses involve scanning for potential TF binding sites. Models of TF binding specificity are also important for understanding the function and evolution of the TFs themselves. The challenge consists of predicting the signal intensities for the remaining TFs.","","https://www.synapse.org/#!Synapse:syn2887863/wiki/72185","completed","1","https://doi.org/10.1038/nbt.2486","","2011-06-01","2011-09-30","2023-09-12 21:27:41","2023-10-12 17:54:36" +"209","dream-5-epitope-antibody-recognition-ear-challenge","DREAM 5 - Epitope-Antibody Recognition (EAR) Challenge","Predict the binding specificity of peptide-antibody interactions","Humoral immune responses are mediated through antibodies. About 1010 to 1012 different antigen binding sites called paratopes are generated by genomic recombination. These antibodies are capable to bind to a variety of structures ranging from small molecules to protein complexes, including any posttranslational modification thereof. When studying protein-antibody interactions, two types of epitopes (the region paratopes interact with) are to be distinguished from each other-i) conformational and ii) linear epitopes. All potential linear epitopes of a protein can be represented by short peptides derived from the primary amino acid sequence. These peptides can be synthesized and arrayed on solid supports, e.g. glass slides (see Lorenz et al., 2009 [1]). By incubating these peptide arrays with antibody mixtures such as human serum or plasma, peptides can be determined that interact with antibodies in a specific fashion.","","https://www.synapse.org/#!Synapse:syn2820433/wiki/71017","completed","1","","","2010-06-09","\N","2023-09-12 21:27:44","2023-10-12 17:54:39" +"210","dream-gene-expression-prediction-challenge","DREAM Gene Expression Prediction Challenge","Predict gene expression levels from promoter sequences in eukaryotes","The level by which genes are transcribed is determined in large part by the DNA sequence upstream to the gene, known as the promoter region. Although widely studied, we are still far from a quantitative and predictive understanding of how transcriptional regulation is encoded in gene promoters. One obstacle in the field is obtaining accurate measurements of transcription derived by different promoters. To address this, an experimental system was designed to measure the transcription derived by different promoters, all of which are inserted into the same genomic location upstream to a reporter gene -a yellow florescence protein gene (YFP). The challenge consists of the prediction of the promoter activity given a promoter sequence and a specific experimental condition. To study a set of promoters that share many elements of the regulatory program, and thus are suitable for computational learning, the data pertains to promoters of most of the ribosomal protein genes (RP) of yeast (S....","","https://www.synapse.org/#!Synapse:syn2820426/wiki/71010","completed","1","","","2010-07-09","\N","2023-09-12 21:28:00","2023-10-19 23:32:10" +"211","dream-5-systems-genetics-challenge","DREAM 5 - Systems Genetics Challenge","Predict disease phenotypes and infer gene networks from systems genetics data","The central goal of systems biology is to gain a predictive, system-level understanding of biological networks. This can be done, for example, by inferring causal networks from observations on a perturbed biological system. An ideal experimental design for causal inference is randomized, multifactorial perturbation. The recognition that the genetic variation in a segregating population represents randomized, multifactorial perturbations (Jansen and Nap (2001), Jansen (2003)) gave rise to Systems Genetics (SG), where a segregating or genetically randomized population is genotyped for many DNA variants, and profiled for phenotypes of interest (e.g. disease phenotypes), gene expression, and potentially other ‘omics’ variables (protein expression, metabolomics, DNA methylation, etc.; Figure 1. Figure 1 was taken from Jansen and Nap (2001)). In this challenge we explore the use of Systems Genetics data for elucidating causal network models among genes, i.e. Gene Networks (DREAM5 SYSGEN...","","https://www.synapse.org/#!Synapse:syn2820440/wiki/","completed","1","","","2010-07-09","\N","2023-09-12 21:28:10","2023-10-12 17:54:42" +"212","dream-6-estimation-of-model-parameters-challenge","DREAM 6 - Estimation of Model Parameters Challenge","Challenge to estimate model parameters","Given the complete model structures (including expressions for the kinetic rate laws) for three gene regulatory networks, participants are asked to develop and/or apply optimization methods, including the selection of the most informative experiments, to accurately estimate parameters and predict outcomes of perturbations in Systems Biology models.","","https://www.synapse.org/#!Synapse:syn2841366/wiki/71372","completed","1","","","2011-06-01","2011-10-31","2023-09-12 21:28:12","2023-10-12 17:54:45" +"213","dream-6-flowcap2-molecular-classification-of-acute-myeloid-leukemia-challenge","DREAM 6 - FlowCAP2 Molecular Classification of Acute Myeloid Leukemia Challenge","Diagnose acute myeloid leukemia from patient data using flow cytometry","Flow cytometry (FCM) has been widely used by immunologists and cancer biologists for more than 30 years as a biomedical research tool to distinguish different cell types in mixed populations, based on the expression of cellular markers. It has also become a widely used diagnostic tool for clinicians to identify abnormal cell populations associated with disease. In the last decade, advances in instrumentation and reagent technologies have enabled simultaneous single-cell measurement of tens of surface and intracellular markers, as well as tens of signaling molecules, positioning FCM to play an even bigger role in medicine and systems biology [1,2]. However, the rapid expansion of FCM applications has outpaced the functionality of traditional analysis tools used to interpret FCM data such that scientists are faced with the daunting prospect of manually identifying interesting cell populations in 20 dimensional data from a collection of millions of cells. For these reasons a reliable...","","https://www.synapse.org/#!Synapse:syn2887788/wiki/72178","completed","1","https://doi.org/10.1038/nmeth.2365","","2011-06-01","2011-09-30","2023-09-12 21:28:19","2023-11-14 19:36:22" +"214","dream-6-alternative-splicing-challenge","DREAM 6 - Alternative Splicing Challenge","Compare mRNA-seq methods on primate and rhino transcripts","The goal of the mRNA-seq alternative splicing challenge is to assess the accuracy of the reconstruction of alternatively spliced mRNA transcripts from Illumina short-read mRNA-seq. Reconstructed transcripts will be scored against Pacific Biosciences long-read mRNA-seq. The ensuing analysis of the transcriptomes from mandrill and rhinoceros fibroblasts and their derived induced pluripotent stem cells (iPSC), as well as the transcriptome for human Embrionic Stem Cells (hESC) is an opportunity to discover novel biology as well as investigate species-bias of different methods.","","https://www.synapse.org/#!Synapse:syn2817724/wiki/","completed","1","","","2011-08-09","\N","2023-09-12 21:28:25","2023-10-12 17:54:50" +"215","causalbench-challenge","CausalBench Challenge","Gene network inference from single-cell perturbation data","Mapping gene-gene interactions in cellular systems is a fundamental step in early-stage drug discovery that helps generate hypotheses on what molecular mechanisms may effectively be targeted by potential future medicines. In the CausalBench Challenge, we invite the machine-learning community to advance the state-of-the-art in deriving gene-gene networks from large-scale real-world perturbational single-cell datasets to improve our ability to glean causal insights into disease-relevant biology.","","https://www.gsk.ai/causalbench-challenge/","completed","16","https://doi.org/10.48550/arXiv.2308.15395","","2023-03-01","2023-04-21","2023-09-12 21:28:25","2023-11-14 19:36:27" +"216","iclr-computational-geometry-and-topology-challenge-2022","ICLR Computational Geometry & Topology Challenge 2022","Advancing computational geometry and topology with python","The purpose of this challenge is to foster reproducible research in geometric (deep) learning, by crowdsourcing the open-source implementation of learning algorithms on manifolds. Participants are asked to contribute code for a published/unpublished algorithm, following Scikit-Learn/Geomstats' or pytorch's APIs and computational primitives, benchmark it, and demonstrate its use in real-world scenarios.","","https://github.com/geomstats/challenge-iclr-2022","completed","\N","","","\N","2022-04-04","2023-09-13 16:54:06","2023-10-19 23:28:44" +"217","iclr-computational-geometry-and-topology-challenge-2021","ICLR Computational Geometry & Topology Challenge 2021","Advancing computational geometry and topology with python","The purpose of this challenge is to push forward the fields of computational differential geometry and topology, by creating the best data analysis, computational method, or numerical experiment relying on state-of-the-art geometric and topological Python packages.","","https://github.com/geomstats/challenge-iclr-2021","completed","\N","https://doi.org/10.48550/arXiv.2108.09810","","\N","2021-05-02","2023-09-13 17:02:12","2023-10-19 23:28:44" +"218","genedisco-challenge","GeneDisco Challenge","Exploring experimental design with active learning for genetics","The GeneDisco challenge is a machine learning community challenge for evaluating batch active learning algorithms for exploring the vast experimental design space in genetic perturbation experiments. Genetic perturbation experiments, using for example CRISPR technologies to perturb the genome, are a vital component of early-stage drug discovery, including target discovery and target validation. The GeneDisco challenge is organized in conjunction with the Machine Learning for Drug Discovery workshop at ICLR-22.","","https://www.gsk.ai/genedisco-challenge/","completed","16","https://doi.org/10.48550/arXiv.2110.11875","","2022-01-31","2022-03-31","2023-09-13 17:20:30","2023-10-19 23:32:43" +"219","hidden-treasures-warm-up","Hidden Treasures: Warm Up","Assess genome sequencing software accuracy with unknown variants","In the context of human genome sequencing, software pipelines typically involve a wide range of processing elements, including aligning sequencing reads to a reference genome and subsequently identifying variants (differences). One way of assessing the performance of such pipelines is by using well-characterized datasets such as Genome in a Bottle’s NA12878. However, because the existing NGS reference datasets are very limited and have been widely used to train/develop software pipelines, benchmarking of pipeline performance would ideally be done on samples with unknown variants. This challenge will provide a unique opportunity for participants to investigate the accuracy of their pipelines by testing the ability to find in silico injected variants in FASTQ files from exome sequencing of reference cell lines. It will be a warm up for the community ahead of a more difficult in silico challenge to come in the fall. This challenge will provide users with a FASTQ file of a NA12878 se...","","https://precision.fda.gov/challenges/1","completed","6","","","2017-07-17","2017-09-13","2023-09-13 23:31:39","2023-10-12 17:55:23" +"220","data-management-and-graph-extraction-for-large-models-in-the-biomedical-space","Data management and graph extraction for large models in the biomedical space","Advancing biomedical knowledge graphs","This fall, CMU Libraries is hosting a hackathon in partnership with DNAnexus on the topic of data management and graph extraction for large models in the biomedical space. The hackathon will be held in person at CMU, October 19-21, 2023. The hackathon is a collaborative, rather than competitive, event, with each team working on a dedicated part of the problem. The teams will be focused on the following topics-1) Knowledge graph-based validation for variant (genomic) assertions; 2) Continuous monitoring for RLHF and flexible infrastructure for layering assertions with rollback; 3) Flexible tokenization of complex data types; 4) Assertion tracking in large models; 5) Column headers for data harmonization. The outputs are often published as preprints or on the F1000 hackathon channel. Contact Ben Busby (bbusby@dnanexus.com) with any questions about the hackathon or serving as a team lead.","","https://library.cmu.edu/about/news/2023-08/hackathon-2023","completed","\N","","","2023-10-19","2023-10-21","2023-09-13 23:32:59","2023-11-14 19:36:32" +"221","cagi2-asthma-twins","CAGI2: Asthma discordant monozygotic twins","Identify genetic differences between asthmatic and healthy twins","The dataset includes whole genomes of 8 pairs of discordant monozygotic twins (randomly numbered from 1 to 16) that is, in each pair identical twins one has asthma and one does not. In addition, RNA sequencing data for each individual is provided. One of the twins in each pair suffers from asthma while the other twin is healthy.","","https://genomeinterpretation.org/cagi2-asthma-twins.html","completed","\N","","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 18:11:42" +"222","cagi4-bipolar","CAGI4: Bipolar disorder","Predicting bipolar disorder from exome data","Bipolar disorder (BD) is a serious mental illness characterized by recurrent episodes of manias and depression, which are syndromes of abnormal mood, thinking and behavior. It affects 1.0-4.5% of the population [1], and it is among the major causes of disability worldwide. This challenge involved the prediction of which of a set of individuals have been diagnosed with bipolar disorder, given exome data. 500 of the 1000 exome samples were provided for training.","","https://genomeinterpretation.org/cagi4-bipolar.html","completed","\N","","","\N","2016-04-04","2023-09-28 18:19:48","2023-09-28 18:25:17" +"223","cagi3-brca","CAGI3: BRCA1 & BRCA2","Assess hereditary cancer risk via BRCA gene analysis","In normal cells, the BRCA1 and BRCA2 genes are involved in homologous recombination for double strand break repair and ensure the stability of a cell's genetic material. Mutations in these genes have been linked to development of breast and ovarian cancer. Myriad Genetics created the BRACAnalysis test in order to assess a woman’s risk of developing hereditary breast or ovarian cancer based on detection of mutations in the BRCA1 and BRCA2 genes. This test has become the standard of care in identification of individuals with hereditary breast and ovarian cancer (HBOC) syndrome. It is based on proprietary methods.","","https://genomeinterpretation.org/cagi3-brca.html","completed","\N","","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-19 23:32:48" +"224","cagi2-breast-cancer-pkg","CAGI2: Breast cancer pharmacogenomics","Exploring CHEK2 as a candidate gene for cancer susceptibility","Cell-cycle-checkpoint kinase 2 (CHEK2; OMIM #604373) is a protein that plays an important role in the maintenance of genome integrity and in the regulation of the G2/M cell cycle checkpoint. CHEK2 has been shown to interact with other proteins involved in DNA repair processes such as BRCA1 and TP53. These findings render CHEK2 an 23 attractive candidate susceptibility gene for a variety of cancers.","","https://genomeinterpretation.org/cagi2-breast-cancer-pkg.html","completed","\N","","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 17:46:22" +"225","cagi4-2eqtl","CAGI4: eQTL causal SNPs","Identify regulatory variants causing gene expression differences","Identifying the causal alleles responsible for variation in expression of human genes has been particularly difficult. This is an important problem, as genome-wide association studies (GWAS) suggest that much of the variation underlying common traits and diseases maps within regions of the genome that do not encode protein. A massively parallel reporter assay (MPRA) has been applied to thousands of single nucleotide polymorphisms (SNPs) and small insertion/deletion polymorphisms in linkage disequilibrium (LD) with cis-expression quantitative trait loci (eQTLs). The results identify variants showing differential expression between alleles. The challenge is to identify the regulatory sequences and the expression-modulating variants (emVars) underlying each eQTL and estimate their effects in the assay.","","https://genomeinterpretation.org/cagi4-2eqtl.html","completed","\N","","","\N","2016-04-04","2023-09-28 18:19:48","2023-09-29 3-58-33" +"226","cagi1-cbs","CAGI1: CBS","Seeking to understand CBS enzyme function in cysteine production","CBS is a vitamin-dependent enzyme involved in cysteine biosynthesis. The human CBS requires two cofactors for function, vitamin B6 and heme. Homocystinuria due to CBS deficiency (OMIM #236200) is a recessive inborn error of sulfur amino acid metabolism.","","https://genomeinterpretation.org/cagi1-cbs.html","completed","\N","","","\N","2010-12-10","2023-09-28 18:19:48","2023-10-12 17:46:07" +"227","cagi2-cbs","CAGI2: CBS","Developing treatment for homocystinuria caused by cbs deficiency","CBS is a vitamin-dependent enzyme involved in cysteine biosynthesis. The human CBS requires two cofactors for function, vitamin B6 and heme. Homocystinuria due to CBS deficiency (OMIM #236200) is a recessive inborn error of sulfur amino acid metabolism.","","https://genomeinterpretation.org/cagi2-cbs.html","completed","\N","","","\N","2011-10-06","2023-09-28 18:19:48","2023-11-15 22:46:32" +"228","cagi1-chek2","CAGI1: CHEK2","Variants in the ATM & CHEK2 genes are associated with breast cancer","Predictors will be provided with 41 rare missense, nonsense, splicing, and indel variants in CHEK2.","","https://genomeinterpretation.org/cagi1-chek2.html","completed","\N","","","\N","2010-12-10","2023-09-28 18:19:48","2023-10-19 23:32:57" +"229","cagi3-fch","CAGI3: FCH","Seeking to understand genetic basis of common hyperlipidemia disorder","Familial combined hyperlipidemia (FCH; OMIM 14380) the most prevalent hyperlipidemia, is a complex metabolic disorder characterized by variable occurrence of elevated low-density lipoprotein cholesterol (LDL-C) level and high triglycerides (TG)—a condition that is commonly associated with coronary artery disease (CAD).","","https://genomeinterpretation.org/cagi3-fch.html","completed","\N","","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-01 22:26:44" +"230","cagi3-ha","CAGI3: HA","Raising HDL levels to reduce heart disease risk","Hypoalphalipoproteinemia (HA; OMIM #604091) is characterized by severely decreased serum high-density lipoprotein cholesterol (HDL-C) levels and low apolipoprotein A1 (APOA1). Low HDL-C is a risk factor for coronary artery disease.","","https://genomeinterpretation.org/cagi3-ha.html","completed","\N","","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-15 22:46:55" +"231","cagi2-croshn-s","CAGI2: Crohn's disease","Seeking genes linked to Crohn's, an inflammatory bowel disease","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/cagi2-croshn-s.html","completed","\N","","","\N","2011-10-06","2023-09-28 18:19:48","2023-11-15 22:46:26" +"232","cagi3-crohn-s","CAGI3: Crohn's disease","Understanding the genetics behind Crohn's disease","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/cagi3-crohn-s.html","completed","\N","","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-15 22:46:27" +"233","cagi4-chron-s-exome","CAGI4: Crohn's exomes","Seeking to understand genetic basis of Crohn's bowel disease","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/cagi4-chron-s-exome.html","completed","\N","","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-15 22:46:23" +"234","cagi4-hopkins","CAGI4: Hopkins clinical panel","Exonic sequences of 83 genes linked to 14 diseases analyzed","The Johns Hopkins challenge, provided by the Johns Hopkins DNA Diagnostic Laboratory (http://www.hopkinsmedicine.org/dnadiagnostic), comprised of exonic sequence for 83 genes associated with one of 14 disease classes, including 5 decoys","","https://genomeinterpretation.org/cagi4-hopkins.html","completed","\N","","","\N","2016-04-04","2023-09-28 18:19:48","2023-10-12 17:45:27" +"235","cagi2-mouse-exomes","CAGI2: Mouse exomes","Predict causative variants from exome sequencing data","Predictors were given SNVs and indels found from exome sequencing. Causative variants had been identified for the L11Jus74 and Sofa phenotypes by the use of traditional breeding crosses,47 and the predictions were compared to these results, which were unpublished at the time of the CAGI submissions. The L11Jus74 phenotype is caused by two SNVs (chr11-102258914A> and chr11-77984176A>T), whereas a 15-nucleotide deletion in the Pfas gene is responsible for the Sofa phenotype. The predictions for Frg and Stn phenotypes could not be compared to experimental data, as the causative variants could not successfully be mapped by linkage","","https://genomeinterpretation.org/cagi2-mouse-exomes.html","completed","\N","","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 17:45:19" +"236","cagi3-mrn-mre11","CAGI3: MRE11","Genomes are subject to constant threat by damaging agents that generate dna","Predict probability of pathogenicity (a number between 0 and 1) for individual rare variants of MRE11 and NBS1.","","https://genomeinterpretation.org/cagi3-mrn.html","completed","\N","","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-15 22:47:19" +"237","cagi4-naglu","CAGI4: NAGLU","Predicting enzymatic activity of NAGLU mutants","NAGLU is a lysosomal glycohydrolyase. Deficiency of NAGLU causes the rare disorder Mucopolysaccharidosis IIIB or Sanfilippo B disease. Naturally occurring NAGLU mutants have been assayed for enzymatic activity in transfected cell lysates. The challenge is to predict the fractional activity of each mutant protein compared to the wild-type enzyme.","","https://genomeinterpretation.org/cagi4-naglu.html","completed","\N","","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-15 22:47:24" +"238","cagi4-npm-alk","CAGI4: NPM: ALK","Predicting kinase activity of NPM-ALK fusion mutants","NPM-ALK is a fusion protein in which the ALK tyrosine kinase is constitutively activated, contributing to cancer. NPM-ALK constructs with mutations in the kinase domain have been assayed in extracts of transfected cells. The challenge is to predict the kinase activity and the Hsp90 binding affinity of the mutant proteins relative to the reference NPM-ALK fusion protein.","","https://genomeinterpretation.org/cagi4-npm-alk.html","completed","\N","","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-15 22:47:32" +"239","cagi3-mrn-nbs1","CAGI3: NBS1","Predicting Pathogenicity of Rare MRE11 and NBS1 Variants","Predict probability of pathogenicity (a number between 0 and 1) for individual rare variants of MRE11 and NBS1.","","https://genomeinterpretation.org/cagi3-mrn.html","completed","\N","","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-16 18:38:55" +"240","cagi3-p16","CAGI3: p16","Assessing p16 protein variants' effects on cell growth","Evaluate how different variants of p16 protein impact its ability to block cell proliferation.","","https://genomeinterpretation.org/cagi3-p16.html","completed","\N","","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-20 23:28:57" +"241","cagi2-p53","CAGI2: p53 reactivation","Predictors are asked to submit predictions on the effect of the cancer rescue","The transcription factor p53 is a central tumor suppressor protein that controls DNA repair, cell cycle arrest, and apoptosis (programmed cell death). About half of human cancers have p53 mutations that inactivate p53. Over 250,000 US deaths yearly are due to tumors that express full-length p53 that has been inactivated by a single point mutation. For the past several years, the group of Rick Lathrop at University of California, Irvine, has been engaged in a complete functional census of p53 second-site suppressor (“cancer rescue”) mutations. These cancer rescue mutations are additional amino acids changes (to otherwise cancerous p53 mutations), which have been found to rescue p53 tumor suppressor function, reactivating otherwise inactive p53. These intragenic rescue mutations reactivate cancer mutant p53 in yeast and human cell assays by providing structural changes that compensate for the cancer mutation.","","https://genomeinterpretation.org/cagi2-p53.html","completed","\N","","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-20 23:28:58" +"242","cagi1-pgp","CAGI1: PGP","CAGI challenges utilizing public genomic and phenotypic data resources","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/cagi1-pgp.html","completed","\N","","","\N","2010-12-10","2023-09-28 18:19:48","2023-09-27 21:05:22" +"243","cagi2-pgp","CAGI2: PGP","CAGI challenges utilizing public genomic and phenotypic data resources","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/cagi2-pgp.html","completed","\N","","","\N","2011-10-06","2023-09-28 18:19:48","2023-09-27 21:05:23" +"244","cagi3-pgp","CAGI3: PGP","CAGI challenges utilizing public genomic and phenotypic data resources","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/cagi3-pgp.html","completed","\N","","","\N","2013-04-25","2023-09-28 18:19:48","2023-09-27 21:05:23" +"245","cagi4-pgp","CAGI4: PGP","CAGI challenges utilizing public genomic and phenotypic data resources","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/cagi4-pgp.html","completed","\N","","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-01 22:26:17" +"246","cagi4-pyruvate-kinase","CAGI4: Pyruvate kinase","Predicting mutation impacts on pyruvate kinase activity and regulation","Pyruvate kinase catalyzes the last step in glycolysis and is regulated by allosteric effectors. Variants in the gene encoding the isozymes expressed in red blood cells and liver, including missense variants mapping near the effector binding sites, cause PK deficiency. A large set of single amino acid mutations in the liver enzyme has been assayed in E. coli extracts for the effect on allosteric regulation of enzyme activity. The challenge is to predict the impacts of mutations on enzyme activity and allosteric regulation.","","https://genomeinterpretation.org/cagi4-pyruvate-kinase.html","completed","\N","","","\N","2015-01-11","2023-09-28 18:19:48","2023-11-01 22:26:25" +"247","cagi2-rad50","CAGI2: RAD50","Assessing RAD50 variants for breast cancer risk","RAD50 is a candidate intermediate-risk breast cancer susceptibility gene. The RAD50 data provided for CAGI challenge include a list of potentially interesting sequence variants observed from sequencing RAD50 gene in about 1,400 breast cancer cases and 1,200 ethnically matched controls. Variants in the list were observed between 1 and 20 times.","","https://genomeinterpretation.org/cagi2-rad50.html","completed","\N","","","\N","2011-10-06","2023-09-28 18:19:48","2023-11-15 22:47:40" +"248","cagi2-risksnps","CAGI2: riskSNPs","Exploring molecular mechanisms linking SNPs to disease risk","The goal of this experiment is to explore current understanding of the molecular level mechanisms underlying associations between SNPs and disease risk, incorporating expertise in each of the known mechanism areas, and as far as possible assigning possible mechanisms for each association locus. The correct mechanisms are unknown, so there can be no ranking of accuracy-that is not the point of the experiment. Rather, the goal is to ascertain which mechanisms appear most relevant, how confidently they can be assigned, and what fraction of loci can currently be assigned plausible mechanisms.","","https://genomeinterpretation.org/cagi2-risksnps.html","completed","\N","","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-19 23:33:11" +"249","cagi3-risksnps","CAGI3: riskSNPs","Exploring molecular mechanisms linking SNPs to disease risk","The goal of this experiment is to explore current understanding of the molecular level mechanisms underlying associations between SNPs and disease risk, incorporating expertise in each of the known mechanism areas, and as far as possible assigning possible mechanisms for each association locus. The correct mechanisms are unknown, so there can be no ranking of accuracy-that is not the point of the experiment. Rather, the goal is to ascertain which mechanisms appear most relevant, how confidently they can be assigned, and what fraction of loci can currently be assigned plausible mechanisms.","","https://genomeinterpretation.org/cagi3-risksnps.html","completed","\N","","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-19 23:33:13" +"250","cagi2-nav1-5","CAGI2: SCN5A","Predict the effect of SCN5A mutants in cardiac electrophysiology","The cardiac action potential (AP) is the sum of a number of distinct ionic currents. It can be divided into five phases (phase 0‐4). From pacemaker cells of the SA node the initial depolarizing wave front will spread throughout the cardiomyocytes via gap junctions. If the depolarization is sufficient voltage‐dependent sodium channels (Nav1.5) are activated and allow Na+ influx. This results in a further depolarization of the membrane which will lead to opening of even more Nav channels. This positive feedback mechanism is seen as the rapid upstroke in the initial phase (phase 0) of the action potential. Nav1.5 is encoded by SCN5A and mutations in this gene have been associated with various diseases such as Atrial fibrillation, Long QT syndrome, Cardiac Conduction Defect, Sick Sinus Disease, and Brugada Syndrome (BrS).","","https://genomeinterpretation.org/cagi2-nav1.5.html","completed","\N","","","\N","2011-10-06","2023-09-28 18:19:48","2023-11-14 19:36:54" +"251","cagi2-mr-1","CAGI2: Shewanella oneidensis strain MR-1","How MR-1 affect the fitness of that gene in a given condition","Predictors are asked to submit predictions on how insertions in the given gene of MR-1 affect the fitness of that gene in a given condition (stressor).","","https://genomeinterpretation.org/cagi2-mr-1.html","completed","\N","","","\N","2011-10-06","2023-09-28 18:19:55","2023-11-14 19:37:40" +"252","cagi3-mr-1","CAGI3: Shewanella oneidensis strain MR-1","How MR-1 affect the fitness of that gene in a given condition","Predictors are asked to submit predictions on how insertions in the given gene of MR-1 affect the fitness of that gene in a given condition (stressor).","","https://genomeinterpretation.org/cagi3-mr-1.html","completed","\N","","","\N","2013-04-25","2023-09-28 18:20:01","2023-11-14 19:37:47" +"253","cagi4-sickkids","CAGI4: SickKids","Match genome sequence to clinical descriptions","Realizing the promise of precision medicine will require developing methods for interpreting genome sequence data to infer individuals’ phenotypic traits and predispositions to disease. This challenge involves 25 children with suspected genetic disorders who were referred for clinical genome sequencing. Predictors are given their genome sequences and their clinical phenotypic descriptions, as provided to the diagnostic laboratory, and asked to predict which genome corresponds to which clinical description. Additionally, identify the diagnostic variants underlying the predictions. Optionally, identify predictive secondary variants conferring high risk of other diseases whose phenotypes are not reported in the clinical descriptions.","","https://genomeinterpretation.org/cagi4-sickkids.html","completed","\N","","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-14 19:38:30" +"254","cagi4-sumo-ligase","CAGI4: SUMO ligase","Predict effect of the variants on SUMO ligase","SUMO ligase identifies target proteins and covalently attaches SUMO to them, thereby modulating the functions of hundreds of proteins including proteins implicated in cancer, neurodegeneration, and other diseases. A large library of missense mutations in human SUMO ligase has been assessed for competitive growth in a high-throughput yeast-based complementation assay. The challenge is to predict the effect of mutations on function, as measured by the change in fractional representation of each mutant SUMO ligase clone, relative to wild-type clones, in a competitive yeast growth assay.","","https://genomeinterpretation.org/cagi4-sumo-ligase.html","completed","\N","","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-14 19:38:48" +"255","cagi3-splicing","CAGI3: TP53 splicing","Which TP53 mutations potentially contribute to cancer","The function of exonic splicing regulatory elements can be undermined by DNA sequence variation and in some cases can contribute to pathogenesis. Thousands of disease-causing mutations disrupt exonic splicing regulatory elements. These data suggest that >25 percent of missense mutations may impact pre-mRNA splicing rather than mRNA translation. Using minigene constructs derived from a fragment of the TP53 gene, we have experimentally determined if each mutation influences splicing fidelity in HEK293T cells. We hope that CAGI participants will be able to predict the outcome of our experiments. A long-term goal will be the computational prioritization of disease-causing mutations prior to experimental validation. This contribution is expected to have major impacts in understanding the pathogenic basis of disease-causing mutations.","","https://genomeinterpretation.org/cagi3-splicing.html","completed","\N","","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-14 19:39:00" +"256","cagi4-warfarin","CAGI4: Warfarin exomes","Predict the therapeutic doses of warfarin","With over 33 million prescriptions in 2011, warfarin is the most commonly used anticoagulant for preventing thromboembolic events. Warfarin has a twenty-fold inter-individual dose variability and a narrow therapeutic index, and it is responsible for a third of adverse drug event hospitalizations in older Americans [2]. Alternatives to warfarin, such as direct thrombin inhibitors and factor Xa inhibitors, are now available. However, these are more expensive, irreversible, and may cause a higher rate of acute coronary events compared to warfarin [3,4]. Thus, warfarin remains a mainstay of anticoagulant therapy, and better methods of dosing warfarin will lead to fewer adverse events due to overcoagulation.","","https://genomeinterpretation.org/cagi4-warfarin.html","completed","\N","","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-14 19:39:08" +"257","cagi6-calmodulin","CAGI6: Calmodulin","Predict competitive growth score of different Calmodulin variants","Calmodulin (CaM) is a ubiquitous calcium (Ca2+) sensor protein interacting with more than 200 molecular partners, thereby regulating a variety of biological processes. Missense point mutations in the genes encoding CaM have been associated with ventricular tachycardia and sudden cardiac death. A library encompassing up to 17 point mutations was assessed by far-UV circular dichroism (CD) by measuring melting temperature (Tm) and percentage of unfolding (%unfold) upon thermal denaturation at pH and salt concentration that mimic the physiological conditions. The challenge is to predict- the Tm and %unfold values for isolated CaM variants under Ca2+-saturating conditions (Ca2+-CaM) and in the Ca2+-free (apo) state; whether the point mutation stabilizes or destabilizes the protein (based on Tm and %unfold).","","https://genomeinterpretation.org/cagi6-cam.html","completed","1","","","\N","2021-12-31","2023-09-28 18:19:48","2023-11-15 22:47:52" +"258","cagi2-splicing","CAGI2: splicing","Compare exons to understand the mechanisms underlying pre-mRNA splicing errors","Accurate precursor mRNA (pre-mRNA) splicing is required for the expression of protein coding genes from the human genome. In this process, intervening sequences (introns) are removed from pre-mRNA and coding/regulatory sequences (exons) are ligated together generating a mature mRNA. A large ribonucleoprotein machine called the spliceosome assembles de novo upon every nascent intron and catalyzes the chemical steps of splicing.","","https://genomeinterpretation.org/cagi2-splicing.html","completed","\N","","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-18 15:32:55" +"259","cagi6-lc-arsa","CAGI6: ARSA","Predict the effect of naturally occurring missense mutations","Metachromatic Leukodystrophy (MLD) is an autosomal recessive, lysosomal-storage disease caused by mutations in Arylsulfatase A (ARSA) and toxic accumulation of sulfatide substrate. Genome sequencing has revealed hundreds of protein-altering, ARSA missense variants, but the functional effect of most variants remains unknown. ARSA enzyme activity using a high-throughput cellular assay was measured for a large set of variants of known significance and variants of unknown significance. The challenge is to predict the fractional enzyme activity of each mutant protein compared to the wildtype protein.","","https://genomeinterpretation.org/cagi6-lc-arsa.html","completed","1","","","\N","2022-11-16","2023-09-28 18:20:23","2023-11-14 19:39:42" +"260","predict-hits-for-the-wdr-domain-of-lrrk2","CACHE1: Predict Hits for The WDR Domain of LRRK2","Finding ligands targeting the central cavity of the WDR domain of LRRK2","The first CACHE Challenge target is LRRK2, the most commonly mutated gene in familial Parkinson's Disease. Participants are asked to find hits for the WD40 repeat (WDR) domain of LRRK2. Read more under Details below.","","https://cache-challenge.org/challenges/predict-hits-for-the-wdr-domain-of-lrrk2","completed","17","","","2021-12-01","2022-01-31","2023-09-27 19:01:55","2023-11-14 19:39:53" +"261","finding-ligands-targeting-the-conserved-rna-binding-site-of-sars-cov-2-nsp13","CACHE2: Finding Ligands Targeting The Conserved RNA Binding Site of SARS-CoV-2 NSP13","Target the NSP13 helicase of SARS-CoV-2","Predicted compounds will be procured and tested at CACHE using both enzymatic and binding assays","","https://cache-challenge.org/challenges/finding-ligands-targeting-the-conserved-rna-binding-site-of-sars-cov-2-nsp13","completed","17","","","2022-06-22","2022-09-04","2023-09-27 19:02:43","2023-11-14 19:41:01" +"262","finding-ligands-targeting-the-macrodomain-of-sars-cov-2-nsp3","CACHE3: Finding ligands targeting the macrodomain of SARS-CoV-2 Nsp3","Study the macrodomain of SARS-CoV-2 Nsp3 for potential therapeutic applications","To predict ligands that bind to the ADPr site of SARS-CoV-2 Nsp3 macrodomain (Mac1).","","https://cache-challenge.org/challenges/finding-ligands-targeting-the-macrodomain-of-sars-cov-2-nsp3","completed","17","","","2022-11-02","2023-01-01","2023-09-27 19:03:13","2023-10-16 19:01:19" +"263","finding-ligands-targeting-the-tkb-domain-of-cblb","CACHE4: Finding ligands targeting the TKB domain of CBLB","Investigate the TKB domain of CBLB to discover novel compounds for treatment","Predict compounds that bind to the closed conformation of the CBLB TKB domain with novel chemical templates and KD below 30 micromolar.","","https://cache-challenge.org/challenges/finding-ligands-targeting-the-tkb-domain-of-cblb","completed","17","","","2023-03-09","2023-05-09","2023-09-27 19:03:14","2023-10-16 19:01:22" +"264","rare-disease-ai-hackathon","Rare Disease AI Hackathon","Advance rare disease diagnosis using artificial intelligence (AI) models","Bring AI and medical experts together to build open source models for rare diseases. Create zero-barrier access to rare disease expertise for patients, researchers and physicians. Use AI to Uncover novel links between rare diseases. Establish validation methods for medical AI models. Jumpstart an open source community for rare disease AI models. Launch models for Beta testing on Hypophosphatasia.ai and EhlersDanlos.ai.","","https://www.rarediseaseaihackathon.org/","active","\N","","","2023-09-30","2024-06-15","2023-09-27 19:10:40","2024-02-05 16:55:39" +"265","cometh-benchmark","COMETH Benchmark","Quantify tumor heterogeneity","Successful treatment of cancer is still a challenge and this is partly due to a wide heterogeneity of cancer composition across patient population. Unfortunately, accounting for such heterogeneity is very difficult. Clinical evaluation of tumor heterogeneity often requires the expertise of anatomical pathologists and radiologists.This benchmark is dedicated to the quantification of intra-tumor heterogeneity using appropriate statistical methods on cancer omics data.In particular, it focuses on estimating cell types and proportion in biological samples based on methylation and methylome data sets. The goal is to explore various statistical methods for source separation/deconvolution analysis (Non-negative Matrix Factorization, Surrogate Variable Analysis, Principal component Analysis, Latent Factor Models, ...) using both RNA-seq and methylome data.","","https://www.codabench.org/competitions/218/","completed","10","","","2020-06-14","2020-12-29","2023-09-28 23:25:52","2023-11-14 19:41:05" +"266","the-miccai-2014-machine-learning-challenge","The MICCAI 2014 Machine Learning Challenge","Predict binary and continuous phenotypes from Structural Brain MRI","Machine learning tools are increasingly applied to brain MRI scans for predicting individual-level clinical phenotypes. Despite methodological advancements, benchmark studies with standardized datasets are scarce, hindering tool validation and comparison. The MICCAI 2014 Machine Learning Challenge (MLC) addresses this gap, utilizing four large-scale (N > 70) brain MRI datasets with clinically relevant phenotypes. The aim is to showcase the current state of neuroimage-based prediction, drawing machine-learning practitioners to MICCAI and medical image computing. MICCAI 2014 MLC complements the main conference, the Machine Learning in Medical Imaging Workshop, and the CADDementia challenge focused on Alzheimer's diagnosis from brain MR scans.","","https://competitions.codalab.org/competitions/1471","completed","9","","","2014-04-16","2014-06-14","2023-09-28 23:36:12","2023-11-14 19:41:17" +"267","cagi6-annotate-all-missense","CAGI6: Annotate All Missense","Predict the functional effect of every coding SNV in the human genome","dbNSFP currently describes 81,782,923 possible protein-altering variants in the human genome. The challenge is to predict the functional effect of every such variant. For the vast majority of these missense and nonsense variants, the functional impact is not currently known, but experimental and clinical evidence is accruing rapidly. Rather than drawing upon a single discrete dataset as typical with CAGI, predictions will be assessed by comparing with experimental or clinical annotations made available after the prediction submission date, on an ongoing basis. If predictors assent, predictions will also be incorporated into dbNSFP.","","https://genomeinterpretation.org/cagi6-annotate-all-missense.html","completed","1","","","2021-06-01","2021-10-11","2023-06-23 00:00:00","2023-11-15 22:48:16" +"268","cagi6-hmbs","CAGI6: HMBS","Submit the fitness score for each of the variants in the HMBS gene","Hydroxymethylbilane synthase (HMBS), also known as porphobilinogen deaminase (PBGD) or uroporphyrinogen I synthase, is an enzyme involved in heme production. In humans, variants that affect HMBS function result in acute intermittent porphyria (AIP), an autosomal dominant genetic disorder caused by a build-up of porphobilinogen in the cytoplasm. A large library of HMBS missense variants was assessed with respect to their effects on protein function using a high-throughput yeast complementation assay. The challenge is to predict the functional effects of these variants.","","https://genomeinterpretation.org/cagi6-hmbs.html","completed","1","","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-11-15 22:48:31" +"269","cagi6-id-panel","CAGI6: Intellectual Disability Panel","Analyze data of Intellectual Disability Panel to identify causative variants","The objective in this challenge is to predict a patient's clinical phenotype and the causal variant(s) based on their gene panel sequences. Sequence data for 74 genes from a cohort of 500 patients with a range of neurodevelopmental presentations (intellectual disability, autistic spectrum disorder, epilepsy, microcephaly, macrocephaly, hypotonia, ataxia) has been made available for this challenge. Additional data from 150 patients from the same clinical study is available for training and validation.","","https://genomeinterpretation.org/cagi6-id-panel.html","completed","1","","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-11-14 19:43:25" +"270","cagi6-mapk1","CAGI6: MAPK1","Predict the ΔΔG(H2O) value for the MAPK1","MAPK1 (ERK2) is active as serine/threonine kinase in the Ras-Raf-MEK-ERK signal transduction cascade that regulates cell proliferation, transcription, differentiation, and cell cycle progression. MAPK1 is activated by phosphorylation which occurs with strict specificity by MEK1/2 on Thr185 and Tyr187, and may also act as a transcriptional repressor independent of its kinase activity. A library of eleven missense variants selected from the COSMIC database was assessed by near and far-UV circular dichroism and intrinsic fluorescence spectra to determine thermodynamic stability at different concentrations of denaturant. These data were used to calculate a ΔΔGH20 value; i.e., the difference in unfolding free energy ΔGH20 between each variant and the wildtype protein, both in phosphorylated and unphosphorylated forms. The challenge is to predict these two ΔΔGH20 values and the catalytic efficiency (kcat/km)mut/(kcat/km)wt, as determined by a fluorescence assay, of the phosphorylated fo...","","https://genomeinterpretation.org/cagi6-mapk1.html","completed","1","","","2021-07-08","2021-10-11","2023-06-23 00:00:00","2023-11-15 22:48:43" +"271","cagi6-mapk3","CAGI6: MAPK3","Predict the ΔΔG(H2O) value for the MAPK3","MAPK3 (ERK1) is active as serine/threonine kinase in the Ras-Raf-MEK-ERK signal transduction cascade that regulates cell proliferation, transcription, differentiation, and cell cycle progression. MAPK3 is activated by phosphorylation which occurs with strict specificity by MEK1/2 on Thr202 and Tyr204, and may also act as a transcriptional repressor independent of its kinase activity. A library of twelve missense variants selected from the COSMIC database was assessed by near and far-UV circular dichroism and intrinsic fluorescence spectra to determine thermodynamic stability at different concentrations of denaturant. These data were used to calculate a ΔΔGH20 value; i.e., the difference in unfolding free energy ΔGH20 between each variant and the wildtype protein, both in phosphorylated and unphosphorylated forms. The challenge is to predict these two ΔΔGH20 values and the catalytic efficiency (kcat/km)mut/(kcat/km)wt, as determined by a fluorescence assay, of the phosphorylated fo...","","https://genomeinterpretation.org/cagi6-mapk3.html","completed","1","","","2021-08-04","2021-10-11","2023-06-23 00:00:00","2023-11-15 22:48:47" +"272","cagi6-mthfr","CAGI6: MTHFR","Submit predictions for each missense variant in the MTHFR","Methylenetetrahydrofolate reductase (MTHFR) catalyzes the production of 5-methyltetrahydrofolate, which is needed for conversion of homocysteine to methionine. Humans with variants affecting MTHFR function present with a wide range of phenotypes, including homocystinuria, homocysteinemia, developmental delay, severe mental retardation, psychiatric disturbances, and late-onset neurodegenerative disorders. A further complication to interpretation of variants in this gene is a common variant, Ala222Val, carried by a large fraction of the human population. A large library of MTHFR missense variants was assessed with respect to their effects on protein function using a high-throughput yeast complementation assay. The challenge is to predict the functional effects of these variants in two different settings- for the wildtype protein, and for the protein with the common Ala222Val variant.","","https://genomeinterpretation.org/cagi6-mthfr.html","completed","1","","","2021-05-03","2021-06-30","2023-06-23 00:00:00","2023-11-15 22:48:59" +"273","cagi6-prs","CAGI6: Polygenic Risk Scores","Estimate polygenic risk scores (PRS) for complex diseases","Polygenic risk scores (PRS) have potential clinical utility for risk surveillance, prevention and personalized medicine. Participants will be provided with datasets of four real phenotypes (Type 2 Diabetes, Breast Cancer, Inflammatory Bowel Disease and Coronary Artery Disease) and of thirty simulated phenotypes representing a range of genetic architectures of common polygenic diseases. The challenge is to predict the disease outcomes of individuals in held-out validation cohorts.","","https://genomeinterpretation.org/cagi6-prs.html","completed","1","","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-11-14 19:42:40" +"274","cagi6-rgp","CAGI6: Rare Genomes Project","Identify causative variants in rare disease genomes for diagnosis","The Rare Genomes Project (RGP) is a direct-to-participant research study on the utility of genome sequencing for rare disease diagnosis and gene discovery. The study is led by genomics experts and clinicians at the Broad Institute of MIT and Harvard. Research subjects are consented for genomic sequencing and the sharing of their sequence and phenotype information with researchers working to understand the molecular causes of rare disease. When a candidate disease variant believed to be related to the phenotype is identified, the variant is confirmed with Sanger sequencing in a clinical setting and returned to the participant via his or her local physician. In this challenge, whole genome sequence data and phenotype data from a subset of the solved and unsolved RGP families will be provided. Participants in the challenge will try to identify the causative variant(s) in each case. For the unsolved cases, prioritized variants from the participating teams will be examined to see if ad...","","https://genomeinterpretation.org/cagi6-rgp.html","completed","1","","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:27" +"275","cagi6-invitae","CAGI6: Sherloc clinical classification","122,000 coding variants predicted for ClinVar","Invitae is a genetic testing company that publishes their variant interpretations to ClinVar. In this challenge, over 122,000 previously uncharacterized variants are provided, spanning the range of effects seen in the clinic. Following the close of this challenge, Invitae will submit their interpretations for these variants to ClinVar. Predictors are asked to interpret the pathogenicity of these variants, and the clinical utility of predictions will be assessed across multiple categories by Invitae.","","https://genomeinterpretation.org/cagi6-invitae.html","completed","1","","","2021-07-08","2021-12-01","2023-06-23 00:00:00","2023-11-16 17:44:21" +"276","cagi6-splicing-vus","CAGI6: Splicing VUS","Predict whether vus disrupt splicing and contribute to genetic disorders","Variants causing aberrant splicing have been implicated in a range of common and rare disorders, including retinitis pigmentosa, autism spectrum disorder, amyotrophic lateral sclerosis, and a variety of cancers. However, such variants are frequently overlooked by diagnostic sequencing pipelines, leading to missed diagnoses for patients. Clinically ascertained variants of unknown significance underwent whole-blood based RT-PCR to test for impact on splicing. The challenge is to predict which of the tested variants disrupt splicing.","","https://genomeinterpretation.org/cagi6-splicing-vus.html","completed","1","","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-11-14 19:44:47" +"277","cagi6-stk11","CAGI6: STK11","Impact of variants in STK11 gene for Peutz-Jeghers syndrome","Serine/Threonine Kinase 11 (STK11) is considered a master kinase that functions as a tumor suppressor and nutrient sensor within a heterotrimeric complex with pseudo-kinase STRAD-alpha and structural protein MO25. Germline variants resulting in loss of STK11 define Peutz-Jaghers Syndrome, an autosomal dominant cancer predisposition syndrome marked by gastrointestinal hamartomas and freckling of the oral mucosa. Somatic loss of function variants, both nonsense and missense, occur in 15-30% of non-small cell lung adenocarcinomas, where they correlate clinically with insensitivity to anti-PD1 monoclonal antibody therapy. The challenge is to predict the impact on STK11 function for each missense variant in relation to wildtype STK11.","","https://genomeinterpretation.org/cagi6-stk11.html","completed","1","","","2021-06-08","2021-09-01","2023-06-23 00:00:00","2023-11-16 17:44:00" +"278","qbi-hackathon","QBI hackathon","The QBI hackathon","The QBI hackathon is a 48-hour event connecting the vibrant Bay Area developer community with the scientists from UCSF, UCB and UCSC, during which we work together on the cutting edge biomedical problems. Advances in computer vision, AI, and machine learning have enabled computers to pick out cat videos, recognize people’s faces from photos, play video games and drive cars. More recently, application of deep neural nets to protein structure prediction completely revolutionized the field. We look forward to seeing how far we can push science ahead when we apply these latest algorithms to biomedically relevant light microscopy, electron microscopy, and proteomics data. If you love FFTs, transformers, language models, topological data processing, or simply writing code, this is your chance to apply your skills to make an impact on global healthcare. Beyond the actual event, we hope to establish a better connection between talented developers and scientists in the Bay Area, so that we...","","https://www.eventbrite.com/e/qbi-hackathon-2023-tickets-633794304827?aff=oddtdtcreator","completed","\N","","","2023-11-04","2023-11-05","2023-10-06 21:22:51","2023-11-15 22:49:20" +"279","niddk-central-repository-data-centric-challenge","NIDDK Central Repository Data-Centric Challenge","Enhance NIDDK datasets for future Artificial Intelligence (AI) applications","The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repository (https://repository.niddk.nih.gov/home/) is conducting a Data Centric Challenge aimed at augmenting existing Repository data for future secondary research including data-driven discovery by artificial intelligence (AI) researchers. The NIDDK Central Repository (NIDDK-CR) program strives to increase the utilization and impact of the resources under its guardianship. However, lack of standardization and consistent metadata within and across studies limit the ability of secondary researchers to easily combine datasets from related studies to generate new insights using data science methods. In the fall of 2021, the NIDDK-CR began implementing approaches to augment data quality to improve AI-readiness by making research data FAIR (findable, accessible, interoperable, and reusable) via a small pilot project utilizing Natural Language Processing (NLP) to tag study variables. In 2022, the NIDD...","","https://www.challenge.gov/?challenge=niddk-central-repository-data-centric-challenge","completed","\N","","","2023-09-20","2023-11-03","2023-10-18 16:58:17","2023-11-15 22:49:26" +"280","stanford-ribonanza-rna-folding","Stanford Ribonanza RNA Folding","A path to programmable medicine and scientific breakthroughs","Ribonucleic acid (RNA) is essential for most biological functions. A better understanding of how to manipulate RNA could help usher in an age of programmable medicine, including first cures for pancreatic cancer and Alzheimer’s disease as well as much-needed antibiotics and new biotechnology approaches for climate change. But first, researchers must better understand each RNA molecule's structure, an ideal problem for data science.","","https://www.kaggle.com/competitions/stanford-ribonanza-rna-folding","completed","8","","","2023-08-23","2023-11-24","2023-10-23 20:58:06","2023-11-15 22:49:31" +"281","uls23","Universal Lesion Segmentation Challenge '23","Advancements, challenges, and a universal solution emerges","Significant advancements have been made in AI-based automatic segmentation models for tumours. Medical challenges focusing on e.g. Liver, kidney, or lung tumours have resulted in large performance improvements for segmenting these types of lesions. However, in clinical practice there is a need for versatile and robust models capable of quickly segmenting the many possible lesions types in the thorax-abdomen area. Developing a universal lesion segmentation (uls) model that can handle this diversity of lesions types requires a well-curated and varied dataset. Whilst there has been previous work on uls [6-8], most research in this field has made extensive use of a single partially annotated dataset [9], containing only the long- and short-axis diameters on a single axial slice. Furthermore, a test set containing 3d segmentation masks used during evaluation on this dataset by previous publications is not publicly available.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/747/ULS23_logo_aoB8tlx.png","https://uls23.grand-challenge.org/","active","5","","","2023-10-29","2024-03-17","2023-11-02 15:35:22","2023-11-17 21:29:35" +"282","vessel12","VESSEL12","Assess methods for blood vessels in lung CT images","The VESSEL12 challenge compares methods for automatic (and semi-automatic) segmentation of blood vessels in the lungs from CT images.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/1/logo.png","https://vessel12.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.media.2014.07.003","","2011-11-25","2012-04-01","2023-11-08 00:42:00","2023-11-17 21:30:05" +"283","crass","CRASS","Invites participants to submit clavicle segmentation results","Crass stands for chest radiograph anatomical structure segmentation. The challenge currently invites participants to send in results for clavicle segmentation algorithms.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/5/logo.png","https://crass.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:09:56" +"284","anode09","ANODE09","Automatic pulmonary nodule detection systems in chest CT scans","ANODE09 is an initiative to compare systems that perform automatic detection of pulmonary nodules in chest CT scans on a single common database, with a single evaluation protocol.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/7/logo.png","https://anode09.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.media.2010.05.005","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:17:55" +"285","cause07","CAUSE07","Compares algorithms for caudate nucleus segmentation in brain MRI scans","The goal of CAUSE07 is to compare different algorithms to segment the caudate nucleaus from brain MRI scans.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/8/logo.png","https://cause07.grand-challenge.org/","completed","5","","","2007-10-26","\N","2023-11-08 00:42:00","2023-11-17 21:34:10" +"286","subsolidnodules","Subsolid Nodules","We present results of our segmentation method for subsolid lung nodules","We are presenting results of our segmentation method for subsolid lung nodules.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/10/logo.png","https://subsolidnodules.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-08 21:24:01" +"287","caddementia","CADDementia","Classification in AD, MCI, and healthy controls using MRI data","We seek algorithms that perform multi-class classification of patients with Alzheimer's disease (AD), patients with mild cognitive impairment (MCI) and healthy controls (CN) using multi-center structural MRI data.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/17/logo3_100.png","https://caddementia.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.neuroimage.2015.01.048","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:18:33" +"288","mitos-atypia-14","MITOS-ATYPIA-14","Mitosis detection and nuclear atypia on breast cancer H&E stained images","MITOS & ATYPIA 14 contest, hosted by conference ICPR 2014 - detection of mitosis and evaluation of nuclear atypia on breast cancer H&E stained images","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/20/logo_mitos_atypia.png","https://mitos-atypia-14.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:19:06" +"289","lola11","LOLA11","Segmentation of lungs and lobes in chest CT scans","The goal of LOLA11 (LObe and Lung Analysis 2011) is to compare methods for (semi-)automatic segmentation of the lungs and lobes from chest computed tomography scans. Any team, whether from academia or industry, can join.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/39/lola11_web_GVIrfhf.png","https://lola11.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:19:28" +"290","promise12","PROMISE12","Segmentation algorithms for MRI of the prostate","The goal of this challenge is to compare interactive and (semi)-automatic segmentation algorithms for MRI of the prostate.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/40/promise12.png","https://promise12.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.media.2013.12.002","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:48:34" +"291","camelyon16","CAMELYON16","Evaluating algorithms for automated cancer metastasis detection","The goal of this challenge is to evaluate new and existing algorithms for automated detection of cancer metastasis in digitized lymph node tissue sections. Two large datasets from both the radboud university medical center and the university medical center utrecht are provided.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/65/logo.png","https://camelyon16.grand-challenge.org/","completed","5","https://doi.org/10.1001/jama.2017.14585","","2015-11-25","2016-04-01","2023-11-08 00:42:00","2023-11-11 01:44:54" +"292","isbi-aida","ISBI-AIDA","The isbi challenge focuses on evaluating endoscopic image analysis methods","The aim of this challenge is to bring together the community of researchers working on the various types of optical endoscopy at its multiple scales and different needs, to provide reference databases and reference results both for the imaging community and those interested in the translation to the clinical practice.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/67/logo.png","https://isbi-aida.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:50:12" +"293","luna16","LUNA16","Nodule detection algorithms for chest CT in a large-scale setting","The LUNA16 challenge will focus on a large-scale evaluation of automatic nodule detection algorithms for chest CT.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/71/luna16_logo.png","https://luna16.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.media.2017.06.015","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:19:48" +"294","camelyon17","CAMELYON17","Automated detection and classification of breast cancer metastases","Automated detection and classification of breast cancer metastases in whole-slide images of histological lymph node sections. This task has high clinical relevance and would normally require extensive microscopic assessment by pathologists.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/80/camelyon17_logo.png","https://camelyon17.grand-challenge.org/","active","5","https://doi.org/10.1109/tmi.2018.2867350","","2016-11-16","\N","2023-11-08 00:42:00","2023-11-11 01:45:01" +"295","retouch","RETOUCH","Detecting retinal fluid in optical coherence tomography images","Retinal OCT fluid challenge (RETOUCH) compares automated algorithms that are able to detect and segment different types of retinal fluid in optical coherence tomography (OCT).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/111/retouch-logo.png","https://retouch.grand-challenge.org/","completed","5","https://doi.org/10.1109/tmi.2019.2901398","","2017-04-03","\N","2023-11-08 00:42:00","2023-11-17 23:20:14" +"296","cataracts","CATARACTS","Image-based tool detection algorithms for cataract surgery","The challenge on automatic tool annotation for cataract surgery aims at evaluating image-based tool detection algorithms in the context of the most common surgical procedure in the world.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/130/logo.png","https://cataracts.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.media.2018.11.008","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:48:56" +"297","tadpole","TADPOLE","Assesses Alzheimer's disease prediction of longitudinal evolution","The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge is brought to you by the europond consortium in collaboration with the Alzheimer's Disease Neuroimaging Initiative (ADNI).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/141/logo_gC1i5c5.png","https://tadpole.grand-challenge.org/","completed","5","https://arxiv.org/abs/2002.03419","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:21:11" +"298","coronare","CoronARe","Methods in coronary artery reconstruction using C-arm angiography","Coronare ranks state-of-the-art methods in symbolic and tomographic coronary artery reconstruction from interventional c-arm rotational angiography. Specifically, we will benchmark the performance of the methods using accurately pre-processed data, and study the effects of imperfect pre-processing conditions (segmentation and background subtraction errors). The evaluation will be performed in a controlled environment using digital phantom images.","","https://coronare.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:49:15" +"299","iciar2018-challenge","ICIAR 2018","Automatic detection of cancerous regions in breast cancer histology images","Can you develop a method for automatic detection of cancerous regions in breast cancer histology images?","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/176/logo_small.png","https://iciar2018-challenge.grand-challenge.org/","active","5","https://doi.org/10.1016/j.media.2019.05.010","","2023-01-09","\N","2023-11-08 00:42:00","2023-11-14 19:49:22" +"300","sliver07","SLIVER07","Liver segmentation in clinical 3D CT scans in this competition","The goal of this competition is to compare different algorithms to segment the liver from clinical 3d computed tomography (CT) scans.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/178/splash2.jpg","https://sliver07.grand-challenge.org/","completed","5","https://doi.org/10.1109/tmi.2009.2013851","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:21:37" +"301","rocc","ROCC","DR disease in retina OCT volumes","Retinal OCT Classification Challenge (ROCC) is organized as a one day challenge in conjunction with MVIP2017. The goal of this challenge is to call different automated algorithms that are able to detect DR disease from normal retina on a common dataset of OCT volumes, acquired with topcon SD-OCT devices.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/180/logo.jpg","https://rocc.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:22:17" +"302","idrid","IDRiD","Retinal lesion segmentation, optic disc/fovea detection, and DR grading","This challenge evaluates automated techniques for analysis of fundus photographs. We target segmentation of retinal lesions like exudates, microaneurysms, and hemorrhages and detection of the optic disc and fovea. Also, we seek grading of fundus images according to the severity level of DR and DME.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/183/g2385.png","https://idrid.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.media.2019.101561","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:22:36" +"303","empire10","EMPIRE10","Chest CT images; assess the accuracy of algorithms","The EMPIRE10 challenge was launched in early 2010 with an initial set of 20 scan pairs to be registered by participants in their own facility. This was followed in September by a workshop at the MICCAI 2010 conference where participants registered a further 10 scan pairs live within a 3 hour timeframe. This process and the results obtained are described in detail in Murphy et al., ""Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge."", IEEE Trans Med Imaging. 2011 Nov;30(11):1901-20. Please cite this publication if you wish to reference the EMPIRE10 challenge. From this point forward all participants will be judged based on the full set of 30 scan pairs. New participants and new submissions are always welcome - in this way we hope that the EMPIRE10 website will continue to reflect the state of the art in registration of pulmonary CT images.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/186/logo.png","https://empire10.grand-challenge.org/","completed","5","https://doi.org/10.1109/tmi.2011.2158349","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:51:00" +"304","lumic","LUMIC","CT chest images using an anthropomorphic digital phantom","The LUMIC challenge tests the accuracy in registration between pre- and post-contrast CT chest images for algorithms, using an anthropomophic digital phantom.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/203/lumiclogo.png","https://lumic.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:22:52" +"305","continuousregistration","Continuous Registration","Submit your lung and brain registration method","Submit your method for lung and brain registration on https://github.com/superelastix/superelastix! Your method is easily accessible to end-users and automatically compiled, tested, and benchmarked weekly on several different data sets.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/207/logo.png","https://continuousregistration.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:50:44" +"306","drive","DRIVE","Develop a system to automatically segment vessels in human retina fundus images","The DRIVE database has been established to enable comparative studies on segmentation of blood vessels in retinal images. Retinal vessel segmentation and delineation of morphological attributes of retinal blood vessels, such as length, width, tortuosity, branching patterns and angles are utilized for the diagnosis, screening, treatment, and evaluation of various cardiovascular and ophthalmologic diseases such as diabetes, hypertension, arteriosclerosis and chorodial neovascularization. Automatic detection and analysis of the vasculature can assist in the implementation of screening programs for diabetic retinopathy, can aid research on the relationship between vessel tortuosity and hypertensive retinopathy, vessel diameter measurement in relation with diagnosis of hypertension, and computer-assisted laser surgery. Automatic generation of retinal maps and extraction of branch points have been used for temporal or multimodal image registration and retinal image mosaic synthesis. Mor...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/210/logo_drive.PNG","https://drive.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:51:30" +"307","curious2018","CuRIOUS 2018","MICCAI Challenge 2018 for brainshift correction with intra-operative ultrasound","Early brain tumor resection can effectively improve the patient’s survival rate. However, resection quality and safety can often be heavily affected by intra-operative brain tissue shift due to factors, such as gravity, drug administration, intracranial pressure change, and tissue removal. Such tissue shift can displace the surgical target and vital structures (e.g., blood vessels) shown in pre-operative images while these displacements may not be directly visible in the surgeon’s field of view. Intra-operative ultrasound (iUS) is a robust and relatively inexpensive technique to track intra-operative tissue shift and surgical tools, but to help update pre-surgical plans with this information, accurate and robust image registration algorithms are needed to relate pre-surgical MRI to iUS images. Despite the great progress so far, medical image registration techniques still have not made into the surgical room to directly benefit the patients with brain tumors. This challege/worksh...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/221/curious2018_logo.png","https://curious2018.grand-challenge.org/","completed","5","https://doi.org/10.1109/tmi.2019.2935060","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:51:50" +"308","refuge","REFUGE","Algorithms for glaucoma detection and optic disc/cup segmentation","The goal of the REtinal FUndus Glaucoma Challenge (REFUGE) is to evaluate and compare automated algorithms for glaucoma detection and optic disc/cup segmentation on a common dataset of retinal fundus images.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/229/logo_refuge_200x200.png","https://refuge.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:23:34" +"309","monuseg","MoNuSeg","Segmenting nuclei from H&E stained histopathological images","This challenge will showcase the best nuclei segmentation techniques that will work on a diverse set of H&E stained histology images obtained from different hospitals spanning multiple patients and organs. This will enable the training and testing of readily usable (or generalized) nuclear segmentation softwares.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/238/monuseg_logo.png","https://monuseg.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:52:09" +"310","paves","PAVES","Mra images for lower limb arterial occlusive disease planning","Peripheral artery:vein enhanced segmentation (PAVES) is the challenge focussed on providing easily interpretable and relevant images that can be readily understood by clinicians (vascular interventional radiologists & vascular surgeons) from mra datasets where the venous and arterial vasculature may be equally enhanced. The setting is lower limb arterial occlusive disease where imaging of the below knee arterial vasculature is critical in planning limb salvage interventions. However, the competing demands of the high spatial resolution needed to image small vessels versus imaging time constraints where there is often a very short arteriovenous transit time for contrast passage form arterial to venous compartments makes imaging challenging. While dynamic mra techniques can usually allow arterial imaging without venous ‚äòcontamination‚äô these necessarily sacrifice spatial resolution.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/243/paveslogo.png","https://paves.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:23:47" +"311","prostatex","PROSTATEx","Clinical significance of prostate lesions using MRI data","This challenge is the live continuation of the offline PROSTATEx Challenge (""SPIE-AAPM-NCI Prostate MR Classification Challenge”) that was held in conjunction with the 2017 SPIE Medical Imaging Symposium. In this challenge, the task is to predict the clinical significance of prostate lesions found in MRI data.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/258/prostatex-logo.jpg","https://prostatex.grand-challenge.org/","completed","5","","","\N","2022-04-30","2023-11-08 00:42:00","2023-11-15 22:52:25" +"312","hc18","HC18","Measuring fetal head circumference using 2D ultrasound images","Automated measurement of fetal head circumference using 2D ultrasound images","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/265/HC18_LogoV1.png","https://hc18.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:23:58" +"313","anhir","ANHIR","Aligning multi-stained histology tissue samples","The challenge focuses on comparing the accuracy (using manually annotated landmarks) and the approximate speed of automatic non-linear registration methods for aligning microscopy images of multi-stained histology tissue samples.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/285/logo_sq_OdYGo3e.png","https://anhir.grand-challenge.org/","active","5","https://doi.org/10.1109/tmi.2020.2986331","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:51:43" +"314","breastpathq","BreastPathQ: Cancer Cellularity Challenge 2019","Develop a method for analyzing histology patches","SPIE-AAPM-NCI BreastPathQ:Cancer Circularity Challenge 2019: Participants will be tasked to develop an automated method for analyzing histology patches extracted from whole slide images and assign a score reflecting cancer cellularity for tumor burden assessment in each.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/296/spie_with_overlays.png","https://breastpathq.grand-challenge.org/","active","5","https://doi.org/10.1117/1.jmi.8.3.034501","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:27:13" +"315","chaos","CHAOS","Segment liver in CT data and liver, spleen, and kidneys in MRI data","In this challenge, you segment the liver in CT data, and segment liver, spleen, and kidneys in MRI data.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/298/logo_8sv4fA4_SWcTFEs.png","https://chaos.grand-challenge.org/","active","5","https://doi.org/10.1016/j.media.2020.101950","","\N","\N","2023-11-08 00:42:00","2023-11-17 21:31:53" +"316","ead2019","EAD2019","Address multi-class artefact detection, region segmentation, and detection","Endoscopic artefact detection (EAD) is a core problem and needed for realising robust computer-assisted tools. The EAD challenge has 3 tasks: 1) multi-class artefact detection, 2) region segmentation, 3) detection generalisation.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/302/1772_A_M62_00022_1.jpg","https://ead2019.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:25:06" +"317","acdc-lunghp","ACDC-LungHP","Methods for whole-slide lung histopathology images","Automatic cancer detection and classification in whole-slide lung histopathology","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/305/logo.png","https://acdc-lunghp.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:52:50" +"318","palm","PALM","Pathological Myopia diagnosis and fundus lesion segmentation in patients","The pathologic myopia challenge (PALM) focuses on the investigation and development of algorithms associated with the diagnosis of pathological myopia (PM) and segmentation of lesions in fundus photos from PM patients.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/307/palm-logo.jpg","https://palm.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:25:23" +"319","ichallenges","iChallenges","Eye image modalities, including REFUGE, PALM, RETOUCH, among others","We organized a serial of challenges on different eye image modalities, such as REFUGE, PALM, RETOUCH, etc.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/345/ichallenge.png","https://ichallenges.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:25:37" +"320","decathlon-10","Decathlon","Test machine learning algorithm generalizability across 10 different tasks","The medical segmentation decathlon challenge tests the generalisability of machine learning algorithms when applied to 10 different semantic segmentation task.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/350/background_dark_logo.png","https://decathlon-10.grand-challenge.org/","completed","5","https://doi.org/10.1038/s41467-022-30695-9","","2022-07-21","2023-08-20","2023-11-08 00:42:00","2023-11-14 19:53:19" +"321","lyon19","LYON19","Develop methods for automatic lymphocyte detection in IHC stained specimens","Automatic Lymphocyte detection in IHC stained specimens.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/355/ban2_kpuoTJg.png","https://lyon19.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:27:28" +"322","kits19","KiTS19","Participate in the segmentation challenge for kidneys and kidney tumors in 2019","2019 Kidney and Kidney Tumor Segmentation Challenge","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/360/Screenshot_from_2019-01-02_17-23-36.png","https://kits19.grand-challenge.org/","active","5","https://arxiv.org/abs/1912.01054","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:27:36" +"323","paip2019","PAIP 2019","Address liver cancer segmentation and viable tumor burden estimation","PAIP2019: Liver Cancer Segmentation Task 1: Liver Cancer Segmentation Task 2: Viable Tumor Burden Estimation","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/370/Untitled_design.png","https://paip2019.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:27:53" +"324","orcascore","orCaScore","Coronary artery calcium scoring in cardiac CT scans","The purpose of the orCaScore challenge is to compare methods for automatic and semi-automatic coronary artery calcium scoring in cardiac CT scans. This evaluation framework was launched at the MICCAI 2014 workshops in Boston, USA, where we organized the Challenge on Automatic Coronary Calcium Scoring.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/378/00b670348bfdf4464e00c44310ec259f.jpg","https://orcascore.grand-challenge.org/","active","5","https://doi.org/10.1118/1.4945696","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:28:02" +"325","curious2019","curious2019","MRI to intra-operative ultrasound (iUS) before and after tumor resection","MICCAI Challenge 2019 for Correction of Brainshift with Intra-Operative Ultrasound. Taks 1: Register pre-operative MRI to iUS before tumor resection;Taks 2: Register iUS after tumor resection to iUS before tumor resection","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/380/CuRIOUS.png","https://curious2019.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:28:13" +"326","patchcamelyon","PatchCamelyon","Detect breast cancer metastasis in lymph nodes","PatchCamelyon is a new and challenging image classification dataset of 327.680 color images (96 x 96px) extracted from histopathology images of the CAMELYON16 challenge. The goal is to detect breast cancer metastasis in lymph nodes.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/381/Screen_Shot_2019-05-05_at_21.43.25.png","https://patchcamelyon.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:28:22" +"327","endovissub2019-scared","Stereo Correspondence and Reconstruction of Endoscopic Data","Address stereo correspondence and reconstruction challenges in endoscopic data","Stereo correspondence and reconstruction of endoscopic data","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/385/c7714704.jpg","https://endovissub2019-scared.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:58:30" +"328","verse2019","VerSe`19","Vertebrae labelling and segmentation on 150 CT scans","Vertebrae labelling and segmentation on a spine dataset on an unprecedented 150 CT scans with voxel-level vertebral annotations.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/388/logo_border.png","https://verse2019.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 21:30:59" +"329","gleason2019","Gleason2019","Methods for prostate cancer from H&E-stained histopathology images","MICCAI 2019 automatic prostate gleason grading challenge: this challenge aims at the automatic gleason grading of prostate cancer from h&e-stained histopathology images. This task is of critical importance because gleason score is a strong prognostic predictor. On the other hand, it is very challenging because of the large degree of heterogeneity in the cellular and glandular patterns associated with each gleason grade, leading to significant inter-observer variability, even among expert pathologists.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/391/GLEASON2019.png","https://gleason2019.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-14 20:00:08" +"330","age","AGE","Assess automatic methods for angle closure glaucoma evaluation","Angle closure glaucoma evaluation challenge","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/394/icon.png","https://age.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-11 01:48:53" +"331","amd","iChallenge-AMD","Tackle challenges in age-related macular degeneration diagnosis and analysis","Age-related macular degeneration challenge","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/395/%E6%BC%94%E7%A4%BA%E6%96%87%E7%A8%BF1-2.png","https://amd.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:58:34" +"332","structseg2019","StructSeg2019","Automated structure segmentation for radiotherapy planning","Welcome to automatic structure segmentation for radiotherapy planning challenge 2019. This competition is part of the MICCAI 2019 challenge.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/398/logo_aAzg3xS.jpg","https://structseg2019.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:54:50" +"333","digestpath2019","DigestPath2019","Algorithms for signet ring cell detection and colonoscopy tissue screening","The challenge aims to evaluate algorithms for signet ring cell detection and colonoscopy tissue screening in digestive system pathological images. It introduces the first public dataset for these tasks, providing expert-level annotations to advance research on automatic pathological object detection and lesion segmentation.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/399/digestpath-logo2.png","https://digestpath2019.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 18:40:29" +"334","odir2019","ODIR-2019","Compete in recognizing ocular diseases using morphological features","Peking university international competition on ocular disease intelligent recognition","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/402/logo.jpg","https://odir2019.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-14 20:27:41" +"335","lysto","Lymphocyte Assessment Hackathon","Workshop for lymphocyte assessment in computational pathology","Lymphocyte assessment hackathon in conjunction with the MICCAI compay 2019 workshop on computational pathology","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/421/lysto_square.png","https://lysto.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:55:01" +"336","aasce19","AASCE","Develop accurate automated methods for estimating spinal curvature","Accurate automated spinal curvature estimation","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/424/logo.png","https://aasce19.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:58:39" +"337","ecdp2020","HEROHE","Identify HER2-positive from HER2-negative breast cancer specimens","Unlike previous challenges, this proposes to find an image analysis algorithm to identify her2-positive from her2-negative breast cancer specimens evaluating only the morphological features present on the he slide, without the staining patterns of ihc.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/433/ECDP2020_square.jpg","https://ecdp2020.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:55:07" +"338","monusac-2020","MoNuSAC 2020","Address segmentation and classification of nuclei in multi-organ images","Multi-organ nuclei segmentation and classification challenge","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/445/logo.PNG","https://monusac-2020.grand-challenge.org/","completed","5","https://doi.org/10.13140/rg.2.2.12290.02244/1","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:58:42" +"339","endocv","EndoCV2020","Focus on artefact detection and disease detection in endoscopic images","Endoscopy computer vision challenge (endocv2020) introduces two core sub-themes in endoscopy: 1) artefact detection and segmentation (ead2020) and 2) disease detection and segmentation (edd2020).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/462/endoLogo.png","https://endocv.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:55:17" +"340","lndb","LNDb Challenge","Determine nodule detection and characterization for lung cancer screening","Lung cancer screening and fleischner follow-up determination in chest CT through nodule detection, segmentation and characterization","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/470/thumbnail_lndb.png","https://lndb.grand-challenge.org/","active","5","https://arxiv.org/abs/1911.08434","","\N","\N","2023-11-08 00:42:00","2023-11-17 21:31:00" +"341","verse2020","VerSe'20","Label and segment vertebrae on a diverse CT dataset","Vertebrae labelling and segmentation on a multi-centre, multi-scanner, and anatomically-diverse CT dataset.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/473/logo_border.png","https://verse2020.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 21:31:02" +"342","paip2020","PAIP2020","Classify molecular subtypes in colorectal cancers, predict MSI","Built on the success of its predecessor, paip2020 is the second challenge organized by the pathology AI platform (paip) and the seoul national university hospital (snuh). Paip2020 will proceed to not only detect whole tumor areas in colorectal cancers but also to classify their molecular subtypes, which will lead to characterization of their heterogeneity with respect to prognoses and therapeutic responses. All participants should predict one of the molecular carcinogenesis pathways, i.e., microsatellite instability(msi) in colorectal cancer, by performing digital image analysis without clinical tests. This task has a high clinical relevance as the currently used procedure requires an extensive microscopic assessment by pathologists. Therefore, those automated algorithms would reduce the workload of pathologists as a diagnostic assistance.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/480/paip2020_thumb_640x640.jpg","https://paip2020.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 21:33:31" +"343","ribfrac","RibFrac","Benchmark rib fracture detection and classification on 660 CT scans","Rib fracture detection and classification challenge: a large-scale benchmark of 660 CT scans with ~5,000 rib fractures (around 80gb)","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/482/challenge-logo-white_b8a8xbr.jpg","https://ribfrac.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 21:31:05" +"344","tn-scui2020","Thyroid Nodule Segmentation and Classification","Develop algorithms for thyroid nodules using a diverse ultrasound dataset","The main topic of this tn-scui2020 challenge is finding automatic algorithms to accurately classify the thyroid nodules in ultrasound images. It will provide the biggest public dataset of thyroid nodule with over 4500 patient cases from different ages, genders, and were collected using different ultrasound machines. Each ultrasound image is provided with its ground truth class (benign or maglinant) and a detailed delineation of the nodule. This challenge will provide a unique opportunity for participants from different backgrounds (e.g. academia, industry, and government, etc.) To compare their algorithms in an impartial way.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/484/Capture8888.PNG","https://tn-scui2020.grand-challenge.org/","active","5","","","2022-01-20","\N","2023-11-08 00:42:00","2023-11-14 19:56:07" +"345","learn2reg","Learn2Reg","Address challenges in learning from small datasets","Challenge on medical image registration addressing: learning from small datasets; estimating large deformations; dealing with multi-modal scans; and learning from noisy annotations","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/486/logo_warped.png","https://learn2reg.grand-challenge.org/","active","5","https://doi.org/10.1109/tmi.2022.3213983","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:56:18" +"346","asoca","Automated Segmentation Of Coronary Arteries","Develop automated methods for segmentation of coronary arteries","Automated segmentation of coronary arteries","","https://asoca.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-08 17:59:56" +"347","knoap2020","KNOAP2020","Compare methods MRI, X-ray, and clinical risk factors for knee osteoarthritis","Knee osteoarthritis causes a large economic burden on the society and reduces life quality of an individual. Therefore, methods that are able to identify subjects who will develop the disease in the future are important. Usually the methods are optimized for specific datasets and it is unclear how the different methods would perform on previously unseen data. Therefore, we are organizing a challenge to objectively compare methods that use mri, x-ray image data, and clinical risk factors for prediction of incident symptomatic radiographic knee osteoarthritis.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/492/KNOAP_logo_640x640.png","https://knoap2020.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-14 19:58:04" +"348","humanactivitiyclassificationwithradar","Human Activity Classification with Radar","Benchmark classification algorithms on a publicly available radar dataset","The radar challenge is a new event hosted at 2020 iet international radar conference that enables participants to test their classification algorithms on a common, publicly available database of radar data in order to benchmark performances. Dataset download link: http://researchdata.gla.ac.uk/848/","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/493/logo.png","https://humanactivitiyclassificationwithradar.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2024-01-31 22:45:45" +"349","saras-esad","SARAS-ESAD","AI-based surgical scene for robotic assistants in minimally invasive surgery","This challenge is part of medical imaging with deep learning conference, 2020. The conference is held between 6 ‚äë 8 july 2020 in montr√©al. The saras (smart autonomous robotic assistant surgeon) eu consortium, www.saras-project.eu, is working towards replacing the assistant surgeon in mis with two assistive robotic arms. To accomplish that, an artificial intelligence based system is required which not only can understand the complete surgical scene but also detect the actions being performed by the main surgeon. This information can later be used infer the response required from the autonomous assistant surgeon.","","https://saras-esad.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-28 00:29:54" +"350","surgvisdom","SurgVisDom","VR simulations to overcome data privacy concerns in context-aware models","Exploring visual domain adaptation using vr simulations to overcome data privacy concerns in context-aware models.","","https://surgvisdom.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-22 19:40:08" +"351","cada","CADA","Cerebral aneurysm image analysis challenge","Cerebral aneurysms are local dilations of arterial blood vessels caused by a weakness of the vessel wall. Subarachnoid hemorrhage (sah) caused by the rupture of a cerebral aneurysm is a life-threatening condition associated with high mortality and morbidity. The mortality rate is above 40%, and even in case of survival cognitive impairment can affect patients for a long time. Major goals in image analysis are the detection and risk assessment of aneurysms. We, therefore, subdivided the challenge into three categories. The first task is finding the aneurysm; the second task is the accurate segmentation to allow for a longitudinal assessment of the development of suspicious aneurysms. The third task is the estimation of the rupture risk of the aneurysm.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/531/data-first-row-2.png","https://cada.grand-challenge.org/","completed","5","https://doi.org/10.1007/978-3-030-72862-5","","\N","\N","2023-11-08 00:42:00","2023-11-11 01:54:15" +"352","dfu2020","Diabetic Foot Ulcer Challenge 2020","Diabetic foot ulcer challenge 2020","Diabetic foot ulcer challenge 2020","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/532/bb.png","https://dfu2020.grand-challenge.org/","active","5","https://doi.org/10.1016/j.compbiomed.2021.104596","","\N","\N","2023-11-08 00:42:00","2023-11-08 22:47:03" +"353","covid-ct","CT diagnosis of COVID-19","COVID-19 CT Image Diagnosis Competition","Coronavirus disease 2019 (COVID-19) has infected more than 1.3 million individuals all over the world and caused more than 106,000 deaths. One major hurdle in controlling the spreading of this disease is the inefficiency and shortage of medical tests. To mitigate the inefficiency and shortage of existing tests for COVID-19, we propose this competition to encourage the development of effective deep learning techniques to diagnose COVID-19 based on CT images. The problem we want to solve is to classify each CT image into positive COVID-19 (the image has clinical findings of COVID-19) or negative COVID-19 ( the image does not have clinical findings of COVID-19). It‚äôs a binary classification problem based on CT images.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/537/covid-CT2.png","https://covid-ct.grand-challenge.org/","completed","5","","","\N","\N","2023-11-14 20:25:11","2023-11-17 21:32:02" +"354","autoimplant","AutoImplant","MICCAI 2020 Cranial Implant Design","The MICCAI 2020 cranial implant design challenge","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/540/logos.PNG","https://autoimplant.grand-challenge.org/","completed","5","https://arxiv.org/abs/2006.12449","","\N","\N","2023-11-14 20:25:44","2023-11-08 00:59:03" +"355","cada-rre","CADA - Rupture Risk Estimation","Cerebral aneurysm challenge: detect, segment, and assess rupture risk","Cerebral aneurysms are local dilations of arterial blood vessels caused by a weakness of the vessel wall. Subarachnoid hemorrhage (sah) caused by the rupture of a cerebral aneurysm is a life-threatening condition associated with high mortality and morbidity. The mortality rate is above 40%, and even in case of survival cognitive impairment can affect patients for a long time. Major goals in image analysis are the detection and risk assessment of aneurysms. We, therefore, subdivided the challenge into three categories. The first task is finding the aneurysm; the second task is the accurate segmentation to allow for a longitudinal assessment of the development of suspicious aneurysms. The third task is the estimation of the rupture risk of the aneurysm.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/541/data-first-row-2.png","https://cada-rre.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-11 01:59:43" +"356","cada-as","CADA - Aneurysm Segmentation","Cerebral aneurysm image analysis: detect, segment, assess risk","Cerebral aneurysms are local dilations of arterial blood vessels caused by a weakness of the vessel wall. Subarachnoid hemorrhage (sah) caused by the rupture of a cerebral aneurysm is a life-threatening condition associated with high mortality and morbidity. The mortality rate is above 40%, and even in case of survival cognitive impairment can affect patients for a long time. Major goals in image analysis are the detection and risk assessment of aneurysms. We, therefore, subdivided the challenge into three categories. The first task is finding the aneurysm; the second task is the accurate segmentation to allow for a longitudinal assessment of the development of suspicious aneurysms. The third task is the estimation of the rupture risk of the aneurysm.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/543/data-first-row-2.png","https://cada-as.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:59:10" +"357","panda","The PANDA challenge","PANDA Challenge: prostate cancer grading","The panda challenge: prostate cancer grade assessment using the gleason grading system","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/544/panda_logo_notext.png","https://panda.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:59:10" +"358","pathvqachallenge","Pathology Visual Question Answering","Pathology visual question answering","Pathology visual question answering","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/548/grand_challenge3.jpg","https://pathvqachallenge.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-11 01:50:06" +"359","qubiq","QUBIQ","Biomedical image segmentation uncertainties","Quantification of uncertainties in biomedical image segmentation challenge","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/552/brain.png","https://qubiq.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:59:15" +"360","lodopab","LoDoPaB-CT","Low-dose CT reconstruction challenge","Low-dose CT reconstruction in the setting of the lodopab-ct dataset.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/555/logo_white_bg.png","https://lodopab.grand-challenge.org/","active","5","https://doi.org/10.1038/s41597-021-00893-z","","\N","\N","2023-11-08 00:42:00","2023-11-17 21:31:13" +"361","apples-ct","Apples-CT","Ct reconstruction for apple defect detection","High-throughput CT image reconstruction and defect detection for apples","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/557/logo.png","https://apples-ct.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:59:18" +"362","riadd","RIADD (ISBI-2021)","Retinal image analysis for disease detection","Retinal image analysis for multi-disease detection","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/562/Logo_ISBI_640_OO4Fuj9.png","https://riadd.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:59:20" +"363","mitoem","MitoEM","3D mitochondria segmentation benchmark","Large-scale 3d mitochondria instance segmentation benchmark","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/566/logo2.png","https://mitoem.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:59:22" +"364","a-afma","A-AFMA","Automated prenatal ultrasound measurement","Prenatal ultrasound (us) measurement of amniotic fluid is an important part of fetal surveillance as it provides a non-invasive way of assessing if there is oligohydramnios (insufficient amniotic fluid) and polyhydramnios (excess amniotic fluid), which are associated with numerous problems both during pregnancy and after birth. In this image analysis challenge, we aim to attract attention from the image analysis community to work on the problem of automated measurement of the mvp using the predefined ultrasound video clip based on a linear-sweep protocol [1]. We define two tasks. The first task is to automatically detect amniotic fluid and the maternal bladder. The second task is to identify the appropriate points for mvp measurement given the selected frame of the video clip, and calculate the length of the connected line between these points. The data was collected from women in the second trimester of pregnancy, as part of the pure study at the john radcliffe hospital in oxford, uk.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/567/Figure_3_MVP_example.png","https://a-afma.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-11 07:00:24" +"365","covid-segmentation","COVID-19 LUNG CT LESION SEGMENTATION CHALLENGE - 2020","SARS-CoV-2 lung lesion segmentation","This challenge will create the platform to evaluate emerging methods for the segmentation and quantification of lung lesions caused by SARS-CoV-2 infection from CT images.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/569/Challenge_Image.png","https://covid-segmentation.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:09:24" +"366","valdo","Where is VALDO?","Vascular lesion detection challenge 2021","This challenge aims at promoting the development of new solutions for the automated segmentation of such very sparse and small objects while leveraging weak and noisy labels. The central objective of this challenge is to facilitate quantification of CSVD in brain MRI scans.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/570/LogoVALDO.png","https://valdo.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:45:10" +"367","segpc-2021","SegPC-2021","Plasma cell cancer segmentation challenge","This challenge is positioned towards robust segmentation of cells which is the first stage to build such a tool for plasma cell cancer, namely, multiple myeloma (mm), which is a type of blood cancer.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/574/logo_fRPkhwS.png","https://segpc-2021.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-08 00:59:29" +"368","endocv2021","EndoCV2021","Endoscopy Computer Vision Challenge 2021","Computer-aided detection, localization, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking and development of computer vision methods remains an open problem. This is mostly due to the lack of datasets or challenges that incorporate highly heterogeneous dataset appealing participants to test for generalisation abilities of the methods. We aim to build a comprehensive, well-curated, and defined dataset from 6 different centres worldwide and provide 5 datasets types that include: i) multi-centre train-test split from 5 centres ii) polyp size-based split (participants should do this by themselves if of interest), iii) data centre wise split, iv) modality split (only test) and v) one hidden centre test. Participants will be evaluated on all types to address strength and weaknesses of each participants’ method. Both detection bounding boxes and pixel-wise segme...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/575/endoLogo-2021_AdZmuvg.jpg","https://endocv2021.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:38:42" +"369","fusc","Foot Ulcer Segmentation Challenge","Foot ulcer segmentation challenge","The goal of this challenge is to segment the wound area from natural images photographed during clinical visits. In the dataset provided, over 1000 images are collected over 2 years from hundreds of patients. All images are completely de-identified by removing personal identifiers defined by hipaa.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/579/Logo.png","https://fusc.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:38:28" +"370","nucmm","NucMM","Millimeter-scale nucleus 3D segmentation","Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and clinical analysis, enabling the study of cellular expression patterns and cell lineages. We pushed the task forward to the sub-cubic millimeter scale and curated the NucMM dataset with two fully annotated volumes: one 0.1 mm^3 electron microscopy (EM) volume containing nearly the entire zebrafish brain with around 170,000 nuclei; and one 0.25 mm^3 micro-CT (uCT) volume containing part of a mouse visual cortex with about 7,000 nuclei.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/580/NucMM_logo.png","https://nucmm.grand-challenge.org/","completed","5","https://doi.org/10.1007/978-3-030-87193-2_16","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:45:22" +"371","vessel-wall-segmentation","Carotid Artery Vessel Wall Segmentation Challenge","Carotid artery vessel wall segmentation","In this challenge, the task is to segment the vessel wall from 3D-MERGE image with high accuracy and robustness. While the challenges of segmentation in different body regions are different, all vessel wall segmentation requires the basic steps of identifying the artery (localization) and lumen and outer wall segmentation. Then the wall thickness (difference between the lumen and outer wall contours) can be measured. Other clinically usable measurements such as lumen area or percent stenosis can also be derived from the vessel wall segmentation. Therefore, this challenge focuses on the important first step of vessel wall segmentation.","","https://vessel-wall-segmentation.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:38:02" +"372","crossmoda","Cross-Modality Domain Adaptation Image Segmentation - 2021","Cross-modality domain adaptation 2021","This challenge proposes the first medical imaging benchmark of unsupervised cross-modality Domain Adaptation approaches (from contrast-enhanced T1 to high-resolution T2).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/592/crossmoda_logo_black.png","https://crossmoda.grand-challenge.org/","active","5","https://doi.org/10.1016/j.media.2022.102628","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:37:49" +"373","brainptm-2021","BrainPTM 2021","Brain pre-surgical tractography mapping","In this challenge we ask the participants to perform direct white matter tracts mapping in clinical brain MRI scans we provide. The data that is provided consists of 75 cases (patients referred for brain tumor removal) that were acquired at Sheba Medical Center at Tel HaShomer, Israel [2]. Patient pathologies include oligodendrogliomas , astrocytomas, glioblastomas and cavernomas, on first occurrence or in a post-surgical recurrence. According to the neuro-radiologist's estimation, the tumor volumes ranged from 4 (cavernoma) to 60 [cm^3] (glioblastoma multiforme). Also, different levels of edema are present around the dataset tumors, from inexistent to very significant. Along with each case both T1 Structural and Diffusion Weighted modalities are provided. For 60 cases (training) semi-manual white matters tracts mapping is provided in the form of binary segmentation maps. For the rest 15 cases (test) no tracts annotations are provided as these will be used for participants algori...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/595/logo_square.jpg","https://brainptm-2021.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-16 17:40:36" +"374","cholectriplet2021","CholecTriplet 2021","EndoVis sub-challenge for surgical action","This sub-challenge focuses on exploiting machine learning methods for the online automatic recognition of surgical actions as a series of triplets. Participants will develop and compete with algorithms to recognize action triplets directly from the provided surgical videos. This novel challenge investigates the state-of-the-art on surgical fine-grained activity recognition and will establish a new promising research direction in computer-assisted surgery.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/596/logo-challenge.png","https://cholectriplet2021.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:37:21" +"375","paip2021","PAIP2021","PAIP 2021 challenge: perineural invasion","PAIP 2021 challenge aims to promote the development of a common algorithm for automatic detection of perineural invasion in resected specimens of multi-organ cancers. PAIP 2021 challenge will have a technical impact in the following fields: detection of composite targets (nerve and tumor) and common modeling for target images in multiple backgrounds. This challenge will provide a good opportunity to overcome the limitations of current disease-organ-specific modeling and develop a technological approach to the universality of histology in multiple organs.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/598/640-640.png","https://paip2021.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:37:00" +"376","flare","FLARE21","Abdominal organ segmentation challenge","Abdominal organ segmentation plays an important role in clinical practice, and to some extent, it seems to be a solved problem because the state-of-the-art methods have achieved inter-observer performance in several benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can be generalized on more diverse datasets. Moreover, many SOTA methods use model ensembles to boost performance, but these solutions usually have a large model size and cost extensive computational resources, which are impractical to be deployed in clinical practice. To address these limitations, we organize the Fast and Low GPU Memory Abdominal Organ Segmentation challenge that has two main features: (1) the dataset is large and diverse, includes 511 cases from 11 medical centers. (2) we not only focus on segmentation accuracy but also segmentation efficiency, whi...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/599/logo_hDqJ8uG.gif","https://flare.grand-challenge.org/","active","5","https://doi.org/10.1016/j.media.2022.102616","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:36:39" +"377","nucls","NuCLS","Triple-negative breast cancer nuclei challenge","Classification, Localization and Segmentation of nuclei in scanned FFPE H&E stained slides of triple-negative breast cancer from The Cancer Genome Atlas. See: Amgad et al. 2021. arXiv:2102.09099 [cs.CV].","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/601/TCGA-AR-A0U4-DX1_id-5ea40a88ddda5f8398990ccf_left-42405_top-70784_bo_PgpXdUu.png","https://nucls.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:29:28" +"378","bcsegmentation","Breast Cancer Segmentation","Triple-negative breast cancer segmentation","Semantic segmentation of histologic regions in scanned FFPE H&E stained slides of triple-negative breast cancer from The Cancer Genome Atlas. See: Amgad M, Elfandy H, ..., Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/602/BCSegmentationLogo.png","https://bcsegmentation.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:29:37" +"379","feta","FeTA - Fetal Tissue Annotation Challenge","Fetal tissue annotation challenge","The Fetal Tissue Annotation and Segmentation Challenge (FeTA) is a multi-class, multi-institution image segmentation challenge part of MICCAI 2022. The goal of FeTA is to develop generalizable automatic multi-class segmentation methods for the segmentation of developing human brain tissues that will work with data acquired at different hospitals. The challenge provides manually annotated, super-resolution reconstructed MRI data of human fetal brains which will be used for training and testing automated multi-class image segmentation algorithms. In FeTA 2021, we used the first publicly available dataset of fetal brain MRI to encourage teams to develop automatic brain tissue segmentation algorithms. This year, FeTA 2022 takes it to the next level by launching a multi-center challenge for the development of image segmentation algorithms that will be generalizable to different hospitals with unseen data. We will include data from two institutions in the training dataset, and there wi...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/604/FeTA_logo_640.png","https://feta.grand-challenge.org/","upcoming","5","","","2024-03-21","2024-04-26","2023-11-08 00:42:00","2023-12-12 19:00:18" +"380","fastpet-ld","fastPET-LD","PET scan ""hot spots"" detection challenge","In this challenge, we provide 2 training datasets of 68 cases each: the first one was acquired at Sheba medical center (Israel) nuclear medicine department with a very-short exposure of 30s pbp, while the second is the same data followed by a denoising step implemented by a fully convolutional Dnn architecture trained under perceptual loss [1,2]. The purpose of this challenge is the detection of “hot spots”, that is locations that have an elevated standard uptake value (SUV) and potential clinical significance. Corresponding CT scans are also provided. The ground truth, common to both datasets, was generated by Dr. Liran Domachevsky, chair of nuclear medicine at Sheba medical center. It consists of a 3-D segmentation map of the hot spots as well as an Excel file containing the position and size of a 3D cuboid bounding box for each hot spot.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/605/IMG_19052021_144815_600_x_600_pixel.jpg","https://fastpet-ld.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:35:52" +"381","autoimplant2021","AutoImplant 2021","Automatic cranial implant design challenge","Please see our AutoImplant 2020 website for an overview of the cranial implant design topic. Our 2nd AutoImplant Challenge (referred to as AutoImplant 2021) sees the (not limited to) following three major improvements compared to the prior edition, besides a stronger team: Real craniotomy defective skulls will be provided in the evaluation phase. Task specific metrics (e.g., boundary Dice Score) that are optimally in agreement with the clinical criteria of cranial implant design will be implemented and used. Besides a metric-based scoring and ranking system, neurosurgeons will be invited to verify, score and rank the participants-submitted cranial implants based their clinical usability (for the real cases in Task 2).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/607/AutoImplant_2021_Logo.png","https://autoimplant2021.grand-challenge.org/","completed","5","https://doi.org/10.1109/tmi.2021.3077047","","\N","\N","2023-11-08 00:42:00","2023-11-16 17:41:01" +"382","dfu-2021","DFUC2021","Diabetic foot ulcer challenge 2021","We have received approval from the UK National Health Service (NHS) Re-search Ethics Committee (REC) to use these images for the purpose of research. The NHS REC reference number is 15/NW/0539. Foot images with DFU were collected from the Lancashire Teaching Hospital over the past few years. Three cameras were used for capturing the foot images, Kodak DX4530, Nikon D3300and Nikon COOLPIX P100. The images were acquired with close-ups of the full foot at a distance of around 30–40 cm with the parallel orientation to the plane of an ulcer. The use of flash as the primary light source was avoided, and instead, adequate room lights were used to get the consistent colours in images. Images were acquired by a podiatrist and a consultant physician with specialization in the diabetic foot, both with more than 5 years professional experience. As a pre-processing stage, we have discarded photographs with out of focus and blurry artefacts. The DFUC2021 consists of 15,683 DFU patche...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/608/footsnap_logo.png","https://dfu-2021.grand-challenge.org/","active","5","https://doi.org/10.1007/978-3-030-94907-5_7","","\N","\N","2023-11-08 00:42:00","2023-11-16 17:41:08" +"383","saras-mesad","SARAS-MESAD","MICCAI 2021 multi-domain surgeon action detection","This challenge is organized under MICCAI 2021, the 24th International Conference on Medical Image Computing and Computer Assisted Intervention. The event will be held from September 27th to October 1st 2021 in Strasbourg, France. The challenge focuses on multi-domain surgeon action detection.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/609/Screenshot_3.png","https://saras-mesad.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:30:14" +"384","node21","NODE21","NODE21: nodule generation and detection","Among both men and women, lung cancer causes the greatest number of cancer deaths worldwide. Symptoms of lung cancer typically occur at an advanced stage of the disease, when treatment has a reduced chance of success. Early detection is therefore a key factor in reducing mortality rates from lung cancer. Pulmonary nodules, detected through imaging, are the initial manifestation of lung cancer, visible well before clinical symptoms or signs emerge. They can be visible on a chest radiograph (CXR), and chest radiography is by far the most common radiological exam in the world. Thus, CXR plays a critical role in the accurate identification of nodules in the drive towards early detection of lung cancer. Pulmonary nodules are frequently encountered as incidental findings in patients undergoing routine examination or CXR imaging for issues unrelated to lung cancer.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/612/node21logo.jpg","https://node21.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:34:20" +"385","wsss4luad","WSSS4LUAD","WSSS4LUAD semantic segmentation challenge","The WSSS4LUAD dataset contains over 10,000 patches of lung adenocarcinoma from whole slide images from Guangdong Provincial People's Hospital and TCGA with image-level annotations. The goal of this challenge is to perform semantic segmentation for differentiating three important types of tissues in the WSIs of lung adenocarcinoma, including cancerous epithelial region, cancerous stroma region and normal region. Paticipants have to use image-level annotations to give pixel-level prediction.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/621/%E6%88%AA%E5%B1%8F2021-07-05_%E4%B8%8A%E5%8D%8810.17.09.png","https://wsss4luad.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:30:23" +"386","ski10","SKI10","SKI10 cartilage and bone segmentation challenge","Welcome to the SKI10 website. The goal of SKI10 was to compare different algorithms for cartilage and bone segmentation from knee MRI data. Knee cartilage segmentation is a clinically relevant segmentation problem that has gained considerable importance in recent years. Among others, it is used to quantify cartilage deterioration for the diagnosis of Osteoarthritis and to optimize surgical planning of knee implants. See the SKI10 paper in the SKI10 Zenodo repository for further details. SKI10 started out as one of the three competitions of the Grand Challenge Workshop 2010, organized in conjunction with the MICCAI 2010 conference.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/624/ski10sq-big.png","https://ski10.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:31:16" +"387","emsig","EMSIG","EMSIG hackathon: radar-based activity recognition","Welcome to the EMSIG Hackathon 2021, organised by EMSIG (www.emsig.org.uk/), the University of Glasgow, Edinburgh Napier University, UCL, DSTL and BAE Systems plc. The goal of the challenge is to evaluate and compare algorithms for human activity recognition based on radar data. We invite the UK radar community to participate by developing and testing existing and novel automated classification methods.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/628/EMSIGgroup_nYL722C.png","https://emsig.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:30:58" +"388","qubiq21","QUBIQ2021","Quantification of uncertainties challenge 2021","The QUBIQ challenge deals with benchmarking algorithms that quantify uncertainties in biomedical image segmentation. Participants will work on binary segmentation tasks, all of which with multiple annotations from domain experts. To be segmented are various pathologies and anatomical structures, such as brain, kidney, or prostate, in MR or CT image data. A successful algorithm will segment these structures and reproduce the distribution of the experts’ annotations.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/629/brain.png","https://qubiq21.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:30:44" +"389","midog2021","MIDOG Challenge 2021","Mitosis domain generalization challenge 2021","Motivation: Mitosis detection is a key component of tumor prognostication for various tumors, including breast cancer. Scanning microscopy slides with different scanners leads to a significant visual difference, resulting in a domain shift. This domain shift prevents most deep learning models from generalizing to other scanners, leading to strongly reduced performance.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/633/midog_logo.png","https://midog2021.grand-challenge.org/","completed","5","https://doi.org/10.5281/zenodo.4573978","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:26:47" +"390","tiger","TIGER","Tumor infiltrating lymphocytes assessment","TIGER is the first challenge on fully automated assessment of tumor-infiltrating lymphocytes (TILs) in H&E breast cancer slides. It is organized by the Diagnostic Image Analysis Group (DIAG) of the Radboud University Medical Center (Radboudumc) in Nijmegen (The Netherlands), in close collaboration with the International Immuno-Oncology Biomarker working Group (www.tilsinbreastcancer.org). The goal of this challenge is to evaluate new computer algorithms for the automated assessment of tumor-infiltrating lymphocytes (TILs) in Her2 positive and Triple Negative breast cancer (BC) histopathology slides. In recent years, several studies have shown the predictive and prognostic value of visually scored TILs in BC as well as in other cancer types, making TILs a powerful biomarker that can potentially be used in the clinic. With TIGER, we aim at developing computer algorithms that can automatically generate a ""TIL score"" with a high prognostic value.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/636/tiger-logo_qr7B8JU.png","https://tiger.grand-challenge.org/","active","5","","","2022-02-03","\N","2023-11-08 00:42:00","2023-11-15 22:26:17" +"391","stoic2021","STOIC2021 - COVID-19 AI Challenge","COVID-19 AI challenge: CT diagnosis and prognosis","We are launching an artificial intelligence challenge aimed at predicting the severe outcome of COVID-19, based on the largest dataset of Computed Tomography (CT) images of COVID-19 suspects and patients collected to date. Participants will have access to data from the STOIC project, recently published in Radiology. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. The focus of the challenge is the prediction of severe COVID-19, defined as intubation or death within one month from the acquisition of the CT scan (AUC, primary metric). COVID19 positivity will be assessed as a secondary metric in the leaderboard.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/637/stoic2021logo_B3b4JM9.png","https://stoic2021.grand-challenge.org/","active","5","","","2021-12-23","2028-01-01","2023-11-08 00:42:00","2023-11-15 22:24:32" +"392","cxr-covid19","Chest XR COVID-19 detection","AI models for COVID-19 detection in chest x-rays","The Coronavirus Disease 2019 (COVID-19) has spread globally and caused unprecedented damages worldwide. Technology, in particular AI, can play an important role in helping fight against this pandemic. In addition, lessons learned can be helpful in fighting and preventing future pandemics. Multiple hospitals and health professionals have shared COVID-19 images coming from multiple modalities to help advance the research in the field. This challenge aims to develop a multiclass classification algorithm capable of detecting COVID-19 in Chest X-ray images. The dataset contains 3 image classes: COVID-19, Pneumonia and Normal (healthy) (See example images below). With 20,000+ images, the participants can train their algorithms to solve this challenge. A test set will be released and will be used to benchmark the obtained results. This Challenge is part of the ‘Ethics and Explainability for Responsible Data Science (EE-RDS) conference’ which will be held virtually and in Johannesburg (So...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/638/CovidChallenge.png","https://cxr-covid19.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:13:57" +"393","pi-cai","The PI-CAI Challenge","AI and radiologists: prostate cancer detection","Prostate cancer (PCa) is one of the most prevalent cancers in men. One million men receive a diagnosis and 300,000 die from clinically significant PCa (csPCa) (defined as ISUP ≥ 2 cancer) each year, worldwide. Multiparametric magnetic resonance imaging (mpMRI) is playing an increasingly important role in the early diagnosis of prostate cancer, and has been recommended by the 2019 European Association of Urology (EAU) guidelines and the 2019 UK National Institute for Health and Care Excellence (NICE) guidelines, prior to biopsies (Mottet et al., 2021). However, current guidelines for reading prostate mpMRI (i.e. PI-RADS v2.1) follow a semi-quantitative assessment, mandating substantial expertise for proper usage. Moreover, prostate cancer can exhibit a broad range of clinical behavior and highly heterogeneous morphology in MRI. As such, assessments are susceptible to low inter-reader agreement (<50%), sub-optimal interpretation and overdiagnosis (Rosenkrantz et al., 2016, Westphale...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/642/square_logo_we441sa.jpg","https://pi-cai.grand-challenge.org/","active","5","https://doi.org/10.5281/zenodo.6667655","","2022-06-12","\N","2023-11-08 00:42:00","2023-11-15 22:10:32" +"394","ultra-low-dose-pet","Ultra-low Dose PET Imaging Challenge","Low-dose PET scanner imaging recovery challenge","This challenge aims to develop computational algorithms capable of recovering high-quality imaging from low statistics corresponding to low dose scans, with the hope of reducing the radiation exposure to be equivalent to transatlantic flight.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/643/Ultra-low_Dose_PET-01.jpg","https://ultra-low-dose-pet.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:09:48" +"395","airogs","AIROGS","AI for robust glaucoma screening challenge","Early detection of glaucoma can avoid visual impairment, which could be facilitated through screening. Artificial intelligence (AI) could increase the cost-effectiveness of glaucoma screening, by reducing the need for manual labor. AI approaches for glaucoma detection from color fundus photography (CFP) have been proposed and promising at-the-lab performances have been reported. However, large performance drops often occur when AI solutions are applied in real-world settings. Unexpected out-of-distribution data and bad quality images are major causes for this performance drop. Aim: The development of solutions for glaucoma screening from CFP that are robust to real-world scenarios.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/644/logo2-01-logo.png","https://airogs.grand-challenge.org/","active","5","","","2021-12-01","\N","2023-11-08 00:42:00","2023-11-15 22:03:44" +"396","conic-challenge","CoNIC 2022","Colon nuclei identification and counting 2022","The CoNIC challenge starts with the discovery phase where we share the training data, challenge goals, and the evaluation code. Participants should start experimenting with the dataset and train/validate their model on it. All challenge submissions are in the form of a docker container, which means that participants should submit their method to be evaluated on the test sets. In other words, only the training set will be released during the challenge and participants will not have access to any part of the test set. The test will be done in two phases. The first phase, the preliminary test, will give participants a chance to work on their submissions for two weeks, get familiar with the submission workflow, improve their code if needed, and make sure their method works fine in the challenge evaluation pipeline. We will release a template docker structure and tutorials on how participants should package their code using the template and submit it to the challenge website. During ...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/648/conic_logo_YbqWBDc.png","https://conic-challenge.grand-challenge.org/","completed","5","https://arxiv.org/abs/2111.14485","","2022-02-13","\N","2023-11-08 00:42:00","2024-01-31 22:31:47" +"397","cholectriplet2022","CholecTriplet2022: Surgical Action Triplet Detection","Dounding box localization of the regions of action triplets","Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies acted upon provides a better, comprehensive and fine-grained modeling of surgical activities. Automatic recognition of these triplet activities directly from surgical videos would facilitate the development of intra-operative decision support systems that are more helpful, especially for safety, in the operating room (OR). Our previous EndoVIS challenge, CholecTriplet2021 (MICCAI 2021), and existing works on surgical action triplet recognition tackles this as a multi-label classification of all possible combinations. For better clinical utility, real-time modeling of tool-tissue interaction will go beyond determining the presence of these action triplets, to also include estimating their locations in each video frame. Hence, this challenge extends our previous challenge on action triplet recognition to also include bounding box localization of the...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/649/t50-logo-2022_kz11kSp.png","https://cholectriplet2022.grand-challenge.org/","completed","5","https://doi.org/10.1007/978-3-030-59716-0_35","","\N","\N","2023-11-08 00:42:00","2023-11-27 20:45:26" +"398","endocv2022","EndoCV2022","Endoscopic video sequence detection and segmentation","Accurate detection of artefacts is a core challenge in a wide-range of endoscopic applications addressing multiple different disease areas. The importance of precise detection of these artefacts is essential for high-quality endoscopic video acquisition crucial for realising reliable computer assisted endoscopy tools for improved patient care. In particular, colonoscopy requires colon preparation and cleaning to obtain improved adenoma detection rate. Computer aided systems can help to guide both expert and trainee endoscopists to obtain consistent high quality surveillance and detect, localize and segment widely known cancer precursor lesion, “polyps”. While deep learning has been successfully applied in the medical imaging, generalization is still an open problem. Generalizability issue of deep learning models need to be clearly defined and tackled to build more reliable technology for clinical translation. Inspired by the enthusiasm of participants on our previous challenges, t...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/650/EndoCV2022-logo-v2.jpg","https://endocv2022.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.media.2021.102002","","2022-02-20","\N","2023-11-08 00:42:00","2023-11-15 21:56:32" +"399","mela","MELA2022","MICCAI 2022 MELA challenge: ct scan benchmark","The mediastinum is the common site of various lesions, including hyperplasia, cysts, tumors, and lymph nodes transferred from the lungs, which might cause serious problems due to their location. Therefore, the detection of mediastinal lesions has important indications for the early screening and diagnosis of related diseases. Computer-aided diagnosis methods have been developed to assist doctors in interpreting massive computed tomography (CT) scans. However, few prior studies investigate deep learning methods on this labor-intensive task. This challenge establishes a large-scale benchmark dataset to automatically detect mediastinal lesions from 1100 CT scans, consisting of 770 CTs for training, 110 CTs for validation, and 220 CTs for testing. Each annotation file includes coordinates of the bounding box of each mediastinal lesion region per CT scan for serving the task of detection. We hope this challenge could facilitate the research and application of automatic mediastinal lesi...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/651/LOGO-%E8%93%9D-900.png","https://mela.grand-challenge.org/","completed","5","","","2022-07-02","2022-07-17","2023-11-08 00:42:00","2023-11-15 21:56:16" +"400","kipa22","KiPA22 (Regular Challenge)","Kidney and artery segmentation challenge","The challenge is aimed to segment kidney, renal tumors, arteries, and veins from computed tomography angiography (CTA) images in one inference.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/654/logo3_%E5%89%AF%E6%9C%AC.png","https://kipa22.grand-challenge.org/","active","5","https://doi.org/10.1016/j.media.2021.102055","","2022-07-01","\N","2023-11-08 00:42:00","2023-11-17 23:31:41" +"401","parse2022","Parse2022","Pulmonary artery segmentation challenge 2022","It is of significant clinical interest to study pulmonary artery structures in the field of medical image analysis. One prerequisite step is to segment pulmonary artery structures from CT with high accuracy and low time-consuming. The segmentation of pulmonary artery structures benefits the quantification of its morphological changes for diagnosis of pulmonary hypertension and thoracic surgery. However, due to the complexity of pulmonary artery topology, automated segmentation of pulmonary artery topology is a challenging task. Besides, the open accessible large-scale CT data with well labeled pulmonary artery are scarce (The large variations of the topological structures from different patients make the annotation an extremely challenging process). The lack of well labeled pulmonary artery hinders the development of automatic pulmonary artery segmentation algorithm. Hence, we try to host the first Pulmonary ARtery SEgmentation challenge in MICCAI 2022 (Named Parse2022) to start a...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/658/logo.jpg","https://parse2022.grand-challenge.org/","active","5","","","2023-06-30","\N","2023-11-08 00:42:00","2023-11-17 23:31:47" +"402","tdsc-abus2023","TDSC-ABUS2023","Automated 3D breast ultrasound tumor challenge","Tumor Detection, Segmentation And Classification Challenge On Automated 3D Breast Ultrasound","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/662/logo.png","https://tdsc-abus2023.grand-challenge.org/","completed","5","","","2023-07-15","2023-08-20","2023-11-08 00:42:00","2023-11-08 01:00:36" +"403","instance","INSTANCE2022","Intracranial hemorrhage segmentation challenge 2022","Participants are required to segment Intracranial Hemorrhage region in Non-Contrast head CT (NCCT).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/667/Logo_%E9%A1%B5%E9%9D%A2_2.png","https://instance.grand-challenge.org/","active","5","https://doi.org/10.1109/jbhi.2021.3103850","","2022-07-14","\N","2023-11-08 00:42:00","2023-11-15 21:55:33" +"404","bcnb","BCNB","Early breast cancer core-needle biopsy dataset","Breast cancer (BC) has become the greatest threat to women’s health worldwide. Clinically, identification of axillary lymph node (ALN) metastasis and other tumor clinical characteristics such as ER, PR, and so on, are important for evaluating the prognosis and guiding the treatment for BC patients. Several studies intended to predict the ALN status and other tumor clinical characteristics by clinicopathological data and genetic testing score. However, due to the relatively poor predictive values and high genetic testing costs, these methods are often limited. Recently, deep learning (DL) has enabled rapid advances in computational pathology, DL can perform high-throughput feature extraction on medical images and analyze the correlation between primary tumor features and above status. So far, there is no relevant research on preoperatively predicting ALN metastasis and other tumor clinical characteristics based on WSIs of primary BC samples.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/668/BCNB-logo_CUGMa0V.png","https://bcnb.grand-challenge.org/","completed","5","https://doi.org/10.3389/fonc.2021.759007","","\N","\N","2023-11-08 00:42:00","2023-11-16 17:41:33" +"405","ravir","RAVIR","Retinal arteries and veins segmentation dataset","The retinal vasculature provides important clues in the diagnosis and monitoring of systemic diseases including hypertension and diabetes. The microvascular system is of primary involvement in such conditions, and the retina is the only anatomical site where the microvasculature can be directly observed. The objective assessment of retinal vessels has long been considered a surrogate biomarker for systemic vascular diseases, and with recent advancements in retinal imaging and computer vision technologies, this topic has become the subject of renewed attention. In this paper, we present a novel dataset, dubbed RAVIR, for the semantic segmentation of Retinal Arteries and Veins in Infrared Reflectance (IR) imaging. It enables the creation of deep learning-based models that distinguish extracted vessel type without extensive post-processing.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/673/IR_Case_022.png","https://ravir.grand-challenge.org/","active","5","https://doi.org/10.1109/jbhi.2022.3163352","","2022-07-18","\N","2023-11-08 00:42:00","2023-11-16 17:41:34" +"406","dfuc2022","DFUC 2022","Diabetic foot ulcer (DFU) segmentation challenge","Diabetes is a global epidemic affecting around 425 million people and expected to rise to 629 million by 2045. Diabetic Foot Ulcer (DFU) is a severe condition that can result from the disease. The rise of the condition over the last decades is a challenge for healthcare systems. Cases of DFU usually lead to severe conditions that greatly prolongs treatment and result in limb amputation or death. Recent research focuses on creating detection algorithms to monitor their condition to improve patient care and reduce strain on healthcare systems. Work between Manchester Metropolitan University, Lancashire Teaching Hospitals and Manchester University NHS Foundation Trust has created an international repository of up to 11,000 DFU images. Analysis of ulcer regions is a key for DFU management. Delineation of ulcers is time-consuming. With effort from the lead scientists of the UK, US, India and New Zealand, this challenge promotes novel work in DFU segmentation and promote interdisciplina...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/674/footsnap_logo.png","https://dfuc2022.grand-challenge.org/","active","5","https://arxiv.org/abs/2204.11618","","2022-06-20","\N","2023-11-08 00:42:00","2023-11-17 23:32:01" +"407","atlas","ATLAS R2.0 - Stroke Lesion Segmentation","Anatomical tracings of lesions after stroke","Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires significant neuroanatomical expertise. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N=1271), a larger dataset of T1w stroke MRIs and manually segmented lesion masks that includes training (public. n=655), test (masks hidden, n=300), and generalizability (completely hidden, n=316) data. Algorithm development using this larger sample should lead to more robust solutions, and the hidden test and generalizability datasets allow for unbiased performance eval...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/676/ATLAS_Logo_square.png","https://atlas.grand-challenge.org/","active","5","https://doi.org/10.1101/2021.12.09.21267554","","2022-09-18","\N","2023-11-08 00:42:00","2023-11-15 21:54:33" +"408","3dteethseg","3D Teeth Scan Segmentation and Labeling Challenge MICCAI2022","Teeth segmentation in orthodontic CAD systems","Computer-aided design (CAD) tools have become increasingly popular in modern dentistry for highly accurate treatment planning. In particular, in orthodontic CAD systems, advanced intraoral scanners (IOSs) are now widely used as they provide precise digital surface models of the dentition. Such models can dramatically help dentists simulate teeth extraction, move, deletion, and rearrangement and therefore ease the prediction of treatment outcomes. Although IOSs are becoming widespread in clinical dental practice, there are only few contributions on teeth segmentation/labeling available in the literature and no publicly available database. A fundamental issue that appears with IOS data is the ability to reliably segment and identify teeth in scanned observations. Teeth segmentation and labelling is difficult as a result of the inherent similarities between teeth shapes as well as their ambiguous positions on jaws.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/680/Grand-Challenge-Logo_2.jpg","https://3dteethseg.grand-challenge.org/","completed","5","","","2022-07-01","2022-08-15","2023-11-08 00:42:00","2023-11-17 23:32:08" +"409","flare22","MICCAI FLARE 2022","Fast and low-resource abdominal organ segmentation","We extend the FLARE 2021 Challenge from fully supervised settings to a semi-supervised setting that focuses on how to use unlabeled data. Specifically, we provide a small number of labeled cases (50) and a large number of unlabeled cases (2000) in the training set, 50 visible cases for validation, and 200 hidden cases for testing. The segmentation targets include 13 organs: liver, spleen, pancreas, right kidney, left kidney, stomach, gallbladder, esophagus, aorta, inferior vena cava, right adrenal gland, left adrenal gland, and duodenum. In addition to the typical Dice Similarity Coefficient (DSC) and Normalized Surface Dice (NSD), our evaluation metrics also focus on the inference speed and resources (GPU, CPU) consumption. Compare to the FLARE 2021 challenge, the dataset is 4x larger and the segmentations targets are increased to 13 organs. Moreover, the resource-related metrics are changed to the area under GPU memory-time curve and the area under CPU utilization-time curve rat...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/683/challenge-logo_xJtvQKE.png","https://flare22.grand-challenge.org/","active","5","https://doi.org/10.1109/tpami.2021.3100536","","2023-01-01","\N","2023-11-08 00:42:00","2023-11-15 21:54:18" +"410","aggc22","AGGC22","Segment the Circle of Willis vessel components for both CTA and MRA","Driving innovation in computational pathology for prostate cancer diagnosis. Develop algorithms to identify Gleason Patterns in H&E-stained whole slide images.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/684/logo_yEwrCqI.PNG","https://aggc22.grand-challenge.org/","active","5","","","2022-06-29","\N","2023-11-08 00:42:00","2023-11-17 23:32:15" +"411","autopet","autoPET","Whole-body FDG-PET/CT lesion segmentation","Automatic tumor lesion segmentation in whole-body FDG-PET/CT on large-scale database of 1014 studies of 900 patients (training database) acquired on a single site: accurate and fast lesion segmentation avoidance of false positives (brain, bladder, etc.) Testing will be performed on 200 150 studies (held-out test database) with 100 studies originating from the same hospital as the training database and 100 50 are drawn from a different hospital with similar acquisition protocol to assess algorithm robustness and generalizabilit","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/686/autopet-5.png","https://autopet.grand-challenge.org/","completed","5","","","2022-05-02","2022-09-04","2023-11-08 00:42:00","2023-11-15 21:53:42" +"412","acrobat","ACROBAT 2023","Acrobat challenge: WSI registration in breast cancer","The ACROBAT challenge aims to advance the development of WSI registration algorithms that can align WSIs of IHC-stained breast cancer tissue sections to corresponding tissue regions that were stained with H&E. All WSIs originate from routine diagnostic workflows.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/687/final_logo_1280x1280_qgi9ILO.png","https://acrobat.grand-challenge.org/","active","5","","","2022-06-15","\N","2023-11-08 00:42:00","2023-11-17 23:32:30" +"413","surgt","SurgT: Surgical Tracking","Surgical video tracking for trajectory estimation","This challenge consists of surgical videos with a target bounding box and the participants are expected to develop visual tracking algorithms to estimate the trajectory of the bounding box throughout the video-sequence.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/688/Screenshot_from_2022-06-27_09-41-30.png","https://surgt.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:32:36" +"414","slcn","Surface Learning for Clinical Neuroimaging","Developmental phenotypes prediction from cortical imaging","The goal of this challenge will therefore be to elicit submissions of novel methods for registration-free or registration-robust cortical phenotype regression, with emphasis on interpretable or explainable machine learning methods which deliver biomarkers predictive of risk for neurodevelopmental impairment. These will be benchmarked on the tasks of regression of gestational age at birth (seen as a correlate of prematurity) on both registered and native space cortical surface data.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/689/SLCN_Logo.png","https://slcn.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.neuroimage.2018.01.054","","2022-04-24","2022-07-15","2023-11-08 00:42:00","2023-11-15 21:53:02" +"415","p2ilf","Preoperative to Intraoperative Laparoscopy Fusion","Preoperative to intraoperative laparoscopy fusion","Augmented reality (AR) in laparoscopic liver surgery needs key landmark detection in intraoperative 2D laparoscopic images and its registration with the preoperative 3D model from CT/MRI data. Such AR techniques are vital to surgeons as they enable precise tumor localisation for surgical removal. A full resection of targeted tumor minimises the risk of recurrence. However, the task of automatic anatomical curve segmentation (considered as landmarks), and its registration to 3D models is a non-trivial and complex task. Most developed methods in this domain are built around traditional methodologies in computer vision. This challenge is designed to challenge participants to deploy machine learning methods for two tasks - Task I: segmentation of five key anatomical curves from laparoscopic video images and 3D model, including ridge (L, R), ligament, silhouettes, liver boundary; Task 2: matching these segmented curves to the 3D liver model from volumetric data (CT/MR).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/690/logo_V3.png","https://p2ilf.grand-challenge.org/","completed","5","","","2022-09-02","2022-09-14","2023-11-08 00:42:00","2023-11-15 21:52:39" +"416","hecktor","MICCAI HECKTOR 2022","Head and neck tumor segmentation in PET/CT","Following the success of the first two editions of the HECKTOR challenge in 2020 and 2021, this challenge will be presented at the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022. Two tasks are proposed this year (participants can choose to participate in either or both tasks): Task 1: The automatic segmentation of Head and Neck (H&N) primary tumors and lymph nodes (new!) in FDG-PET/CT images; Task 2: The prediction of patient outcomes, namely Recurrence-Free Survival (RFS) from the FDG-PET/CT images and available clinical data.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/691/grandchallenge_logo_d8JNqKz.png","https://hecktor.grand-challenge.org/","active","5","https://doi.org/10.1007/978-3-030-98253-9","","2022-08-26","2024-05-01","2023-11-08 00:42:00","2023-11-15 21:52:24" +"417","k2s","K2S: from undersampled K-space To automatic Segmentation","K2s: undersampled k-space to segmentation","Magnetic resonance imaging (MRI) is the modality of choice for evaluating knee joint degeneration, but it can be susceptible to long acquisition times, tedious post processing, and lack of standardization. One of the most compelling applications of deep learning, therefore, is accelerated analysis of knee MRI. In addition to faster MRI acquisition, deep learning has enhanced image post-processing applications such as tissue segmentation. While fast, undersampled MRI acquisition may not have qualitative, visual acuity that comes from fully-sampled data, the underlying embedding space may be adequate for some applications. The implications for down-stream tasks such as tissue segmentation using convolutional neural networks are not well-characterized. Efficient segmentation of key anatomical structures from undersampled data is an open question that has clinical relevance, e.g., patient triage. The goal of this challenge, therefore, is to train segmentation models from 8x undersamp...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/692/K2S_gradient_5AE5wa5.png","https://k2s.grand-challenge.org/","completed","5","https://doi.org/10.1148/ryai.2021200165","","2022-05-16","2022-06-16","2023-11-08 00:42:00","2023-11-15 21:51:47" +"418","drac22","Diabetic Retinopathy Analysis Challenge MICCAI2022","DR lesions segmentation in UW-OCTA-M images","Diabetic retinopathy is one of the leading causes of blindness and affects approximately 78% people, with a history of diabetes of 15 years or longer [1]. DR often causes gradual changes in vasculature structure and resulting abnormalities. DR is diagnosed by visually inspecting retinal fundus images for the presence of retinal lesions, such as microaneurysms (MAs), intraretinal microvascular abnormalities (IRMAs), nonperfusion areas and neovascularization. The detection of these lesions is critical to the diagnosis of DR. There have been some works using fundus images for DR diagnosis [2]. With rising popularity, OCT angiography (OCTA) has the capability of visualizing the retinal and choroidal vasculature at a microvascular level in great detail [3]. Specially, swept-source (SS)-OCTA allows additionally the individual assessment of the choroidal vasculature. There are already some works using SS-OCTA to grade for qualitative features of diabetic retinopathy [4-6]. Further, ultra...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/694/logo_kT1YYna.png","https://drac22.grand-challenge.org/","active","5","","","2022-08-07","\N","2023-11-08 00:42:00","2023-11-15 21:50:58" +"419","amos22","Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022","Multi-modality abdominal multi-organ segmentation","Abdominal multi-organ segmentation is one of the most attractive topics in the field of medical image analysis, which plays an important role in supporting clinical workflows such as disease diagnosis and treatment planning. The recent success of deep learning methods applied for abdominal multi-organ segmentation exposes the lack of large-scale comprehensive benchmarks for developing and comparing such methods. While several benchmark datasets for abdominal organ segmentation are available, the limited number of organs of interest and training samples still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of different methods. And, most research in medical image analysis today focuses on building bespoke systems to handle stereotypical inputs and outputs associated with a single task, the complexity of systems like this can grow dramatically as the inputs or outputs grow more diverse. If a single algorithm could h...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/695/AMOS_2022.png","https://amos22.grand-challenge.org/","active","5","","","2022-10-09","2060-12-31","2023-11-08 00:42:00","2023-11-15 21:50:34" +"420","surgtoolloc","Surgical Tool Localization in endoscopic videos","Surgical tool localization in endoscopic videos","The ability to automatically detect and track surgical instruments in endoscopic video will enable many transformational interventions. Assessing surgical performance and efficiency, identifying skilled tool use and choreography, and planning operational and logistical aspects of OR resources are just some of the applications that would benefit. Unfortunately obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task. Annotating bounding boxes frame-by-frame in video is tedious and time consuming, yet a wide variety of surgical tools and surgeries must be captured for robust training. Moreover, ongoing annotator training is needed to stay up to date with surgical instrument innovation. In robot-assisted surgery however, potentially informative data like timestamps of instrument installation and removal can be programmatically harvested. The ability to use only tool presence labels to localize tools would significan...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/698/grand-challenge_logo.png","https://surgtoolloc.grand-challenge.org/","completed","5","","","2022-08-29","2022-09-08","2023-11-08 00:42:00","2023-11-15 21:50:20" +"421","curious2022","Brain shift with Intraoperative Ultrasound - Segmentation tasks","Brain shift with intraoperative ultrasound segmentation","Early brain tumor resection can effectively improve the patient’s survival rate. However, resection quality and safety can often be heavily affected by intra-operative brain tissue shift due to factors, such as gravity, drug administration, intracranial pressure change, and tissue removal. Such tissue shift can displace the surgical target and vital structures (e.g., blood vessels) shown in pre-operative images while these displacements may not be directly visible in the surgeon’s field of view. Intra-operative ultrasound (iUS) is a robust and relatively inexpensive technique to track intra-operative tissue shift and surgical tools. Automatic algorithms for brain tissue segmentation in iUS, especially brain tumors and resection cavity can greatly facilitate the robustness and accuracy of brain shift correction through image registration, and allow easy interpretation of the iUS. This has the potential to improve surgical outcomes and patient survival rate. The challenge is an ex...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/699/CuRIOUS_logo.png","https://curious2022.grand-challenge.org/","completed","5","","","2022-08-15","2022-09-13","2023-11-08 00:42:00","2023-11-15 21:50:06" +"422","vessel-wall-segmentation-2022","Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis","Carotid vessel wall segmentation and diagnosis","In this challenge, the task is to segment the vessel wall from 3D-VISTA images and diagnose the atherosclerotic lesions with high accuracy and robustness. And then the clinical usable measurements such as wall thickness (difference between the lumen and outer wall contours), lumen area or stenosis percent can be derived from the vessel wall segmentation. In addition, the identification of the lumen and outer wall boundary of the vessel wall is also critical for the diagnosis of lesions. In summary, this challenge focuses on carotid vessel wall segmentation and atherosclerotic lesion diagnosis.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/700/%E5%9B%BE%E7%89%871.png","https://vessel-wall-segmentation-2022.grand-challenge.org/","completed","5","","","2022-07-08","2022-08-01","2023-11-08 00:42:00","2023-11-16 17:41:55" +"423","crossmoda2022","Cross-Modality Domain Adaptation: Segmentation & Classification","CrossMoDA 2022: unsupervised domain adaptation","The CrossMoDA 2022 challenge is the second edition of the first large and multi-class medical dataset for unsupervised cross-modality Domain Adaptation.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/701/squarelogo_2022.png","https://crossmoda2022.grand-challenge.org/","active","5","","","2022-05-11","\N","2023-11-08 00:42:00","2023-11-17 23:32:53" +"424","atm22","Multi-site, Multi-Domain Airway Tree Modeling (ATM'22)","Airway segmentation in x-ray CT for pulmonary diseases","Airway segmentation is a crucial step for the analysis of pulmonary diseases including asthma, bronchiectasis, and emphysema. The accurate segmentation based on X-Ray computed tomography (CT) enables the quantitative measurements of airway dimensions and wall thickness, which can reveal the abnormality of patients with chronic obstructive pulmonary disease (COPD). Besides, the extraction of patient-specific airway models from CT images is required for navigatiisted surgery.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/702/logo_xqf7twK.png","https://atm22.grand-challenge.org/","active","5","https://doi.org/10.1007/978-3-031-16431-6_48","","2022-08-17","\N","2023-11-08 00:42:00","2023-11-21 17:16:40" +"425","ps-fh-aop-2023","FH-PS-AOP challenge","Fetal head and pubic symphysis segmentation","The task of the FH-PS-AOP grand challenge is to automatically segment 700 FH-PSs from transperineal ultrasound images in the devised Set 2 (test set), given the availability of Set 1, consisting of 401 images. Set 2 is held private and therefore not released to the potential participants to prevent algorithm tuning, but instead the algorithms have to be submitted in the form of Docker containers that will be run by organizers on Set 2. The challenge is organized by taking into account the current guidelines for biomedical image analysis competitions, in particular the recommendations of the Biomedical Image Analysis Challenges (BIAS) initiative for transparent challenge reporting.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/703/F2_WDBTbsq.tif","https://ps-fh-aop-2023.grand-challenge.org/","completed","5","https://doi.org/10.1007/s11517-022-02747-1","","2023-03-27","2023-09-20","2023-11-08 00:42:00","2023-11-16 17:41:56" +"426","shifts","Shifts Challenge 2022","Shifts challenge 2022: distributional shift and uncertainty","The goal of the Shifts Challenge 2022 is to raise awareness among the research community about the problems of distributional shift, robustness, and uncertainty estimation, and to identify new solutions to address them. The competition will consist of two new tracks: White Matter Multiple Sclerosis (MS) lesion segmentation in 3D Magnetic Resonance Imaging (MRI) of the brain and Marine cargo vessel power estimation.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/704/logo_1200.png","https://shifts.grand-challenge.org/","active","5","https://arxiv.org/abs/2206.15407","","2022-09-15","2024-04-08","2023-11-08 00:42:00","2023-11-17 23:33:07" +"427","megc2022","ACMMM MEGC2022: Facial Micro-Expression Grand Challenge","Facial macro- and micro-expressions spotting","The unseen testing set (MEGC2022-testSet) contains 10 long video, including 5 long videos from SAMM (SAMM Challenge dataset) and 5 clips cropped from different videos in CAS(ME)3. The frame rate for SAMM Challenge dataset is 200fps and the frame rate for CAS(ME)3 is 30 fps. The participants should test on this unseen dataset.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/705/acmmm2022_logo.png","https://megc2022.grand-challenge.org/","active","5","https://doi.org/10.1109/fg47880.2020.00029","","2022-05-23","\N","2023-11-08 00:42:00","2023-11-16 17:39:17" +"428","midog2022","MItosis DOmain Generalization Challenge 2022","Mitosis domain generalization challenge 2022","Motivation: Mitosis detection is a key component of tumor prognostication for various tumors. Modern deep learning architectures provide detection accuracies for mitosis that are on the level of human experts. Mitosis is known to be relevant for many tumor types, yet, when trained on one tumor / tissue type, the performance will typically drop significantly on another. Scope: Detect mitotic figures (cells undergoing cell division) from histopathology images (object detection). You will be provided with images from 6 different tumor types, 5 out of which are labeled. In total the set consists of 405 cases and includes 9501 mitotic figure annotations in the training set. Evaluation will be done on ten different tumor types with the F1 score as main metric.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/706/midog_compact.png","https://midog2022.grand-challenge.org/","completed","5","https://doi.org/10.5281/zenodo.6362337","","2022-08-04","2022-08-30","2023-11-08 00:42:00","2023-11-16 17:39:11" +"429","isles22","Ischemic Stroke Lesion Segmentation Challenge","Ischemic stroke lesion segmentation challenge","The goal of this challenge is to evaluate automated methods of stroke lesion segmentation in MR images. Participants are tasked with automatically generating lesion segmentation masks from DWI, ADC and FLAIR MR modalities. The task consist on a single phase of algorithms evaluation. Participants will submit their segmentation model (""algorithm"") via a Docker container which will then be used to generate predictions on a hidden dataset.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/707/Slide1_N1qHO1K.png","https://isles22.grand-challenge.org/","active","5","","","2022-07-15","2030-12-06","2023-11-08 00:42:00","2023-11-15 21:48:03" +"430","neurips22-cellseg","Cell Segmentation in Multi-modality Microscopy Images","Weakly supervised cell segmentation in high-res microscopy","Cell segmentation is usually the first step for downstream single-cell analysis in microscopy image-based biology and biomedical research. Deep learning has been widely used for image segmentation, but it is hard to collect a large number of labeled cell images to train models because manually annotating cells is extremely time-consuming and costly. Furthermore, datasets used are often limited to one modality and lacking in diversity, leading to poor generalization of trained models. This competition aims to benchmark cell segmentation methods that could be applied to various microscopy images across multiple imaging platforms and tissue types. We frame the cell segmentation problem as a weakly supervised learning task to encourage models that use limited labeled and many unlabeled images for cell segmentation as unlabeled images are relatively easy to obtain in practice.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/708/logo_EOVfhip.PNG","https://neurips22-cellseg.grand-challenge.org/","active","5","","","2023-08-01","\N","2023-11-08 00:42:00","2023-11-16 17:39:21" +"431","bci","Breast Cancer Immunohistochemical Image Generation Challenge","Breast cancer immunohistochemical image generation","This is an image-to-image translation task that builds a mapping between two domains (HE and IHC). Given an HE image, the algorithm should predict the corresponding IHC image.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/711/logo.png","https://bci.grand-challenge.org/","active","5","","","2022-08-24","\N","2023-11-08 00:42:00","2023-11-16 17:39:23" +"432","auto-rtp","Fully Automated Radiotherapy Treatment Planning Challenge","Automated radiotherapy treatment planning in prostate cancer","Participants will be provided with simulation CTs for ten prostate cancer patients, together with a treatment intent/prescription (in a machine readable format). The cases will be a mix of prostate only and prostate + nodes. Participants are asked to generate a treatment plan in an as-automated-as-possible way, including contouring and plan generation. No manual intervention on contouring or planning should be performed, but manual steps to transfer data between systems are permitted if required. Freedom is given to participants with respect to the ""treatment machine"" the plan is designed for. However, it is expected that all participants produce a plan that is deliverable in clinically reasonable time frame.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/713/AUTO-RTP_Logo.png","https://auto-rtp.grand-challenge.org/","active","5","","","2023-06-05","\N","2023-11-08 00:42:00","2023-11-16 17:39:25" +"433","2023paip","PAIP 2023: TC prediction in pancreatic and colon cancer","Tumor cellularity prediction in pancreatic and colon cancer","Tumor cellularity (TC) is used to compute the residual tumor burden in several organs, such as the breast and colon. The TC is measured based on semantic cell segmentation, which accurately classifies and delineates individual cells. However, manual analysis of TC is impractical in clinics because of the large volumes of pathological images and is unreliable owing to inconsistent TC values among pathologists. Essentially, tumor cellularity should be calculated by individual cell counting; however, manual counting is impossible, and human pathologists cannot avoid individual differences in diagnostic performance. Automatic image analysis is the ideal method for solving this problem, and it can efficiently reduce the workload of pathologists.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/716/PAIP2023-640.png","https://2023paip.grand-challenge.org/","active","5","","","2023-02-15","\N","2023-11-08 00:42:00","2023-11-16 17:39:26" +"434","snemi3d","SNEMI3D: 3D Segmentation of neurites in EM images","IEEE ISBI 2013 challenge: multimodal segmentation","In this challenge, a full stack of electron microscopy (EM) slices will be used to train machine-learning algorithms for the purpose of automatic segmentation of neurites in 3D. This imaging technique visualizes the resulting volumes in a highly anisotropic way, i.e., the x- and y-directions have a high resolution, whereas the z-direction has a low resolution, primarily dependent on the precision of serial cutting. EM produces the images as a projection of the whole section, so some of the neural membranes that are not orthogonal to a cutting plane can appear very blurred. None of these problems led to major difficulties in the manual labeling of each neurite in the image stack by an expert human neuro-anatomist.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/717/logo.png","https://snemi3d.grand-challenge.org/","active","5","","","2013-01-15","\N","2023-11-08 00:42:00","2023-11-16 17:39:27" +"435","han-seg2023","The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge","Endometrial carcinoma prediction on whole-slide images","Cancer in the region of the head and neck (HaN) is one of the most prominent cancers, for which radiotherapy represents an important treatment modality that aims to deliver a high radiation dose to the targeted cancerous cells while sparing the nearby healthy organs-at-risk (OARs). A precise three-dimensional spatial description, i.e. segmentation, of the target volumes as well as OARs is required for optimal radiation dose distribution calculation, which is primarily performed using computed tomography (CT) images. However, the HaN region contains many OARs that are poorly visible in CT, but better visible in magnetic resonance (MR) images. Although attempts have been made towards the segmentation of OARs from MR images, so far there has been no evaluation of the impact the combined analysis of CT and MR images has on the segmentation of OARs in the HaN region. The Head and Neck Organ-at-Risk Multi-Modal Segmentation Challenge aims to promote the development of new and applicatio...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/718/logo.jpg","https://han-seg2023.grand-challenge.org/","active","5","","","2023-03-26","2024-02-17","2023-11-08 00:42:00","2024-01-31 22:38:41" +"436","endo-aid","Endometrial Carcinoma Detection in Pipelle biopsies","Non-rigid registration challenge for expansion microscopy","Evaluation platform as reference benchmark for algorithms that can predict endometrial carcinoma on whole-slide images of Pipelle sampled endometrial slides stained in H&E, based on the test data set used in our project.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/719/logo-challenge.png","https://endo-aid.grand-challenge.org/","active","5","","","\N","\N","2023-11-08 00:42:00","2024-01-31 22:33:53" +"437","rnr-exm","Robust Non-rigid Registration Challenge for Expansion Microscopy","Xray projectomic reconstruction with skeleton segmentation","Despite the wide adoption of ExM, there are few public benchmarks to evaluate the registration pipeline, which limits the development of robust methods for real-world deployment. To address this issue, we have launched RnR-ExM, a challenge that releases 24 pairs of 3D image volumes from three different species. Participants are asked to align these pairs and submit dense deformation fields for assessment. Half of the volume pairs (the validation and test set) have annotated cell structures (nuclei, blood vessels) as registration landmarks.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/720/RnR-ExM_Logo.png","https://rnr-exm.grand-challenge.org/","active","5","","","2023-02-17","2028-03-16","2023-11-08 00:42:00","2023-11-16 17:39:32" +"438","xpress","Xray Projectomic Reconstruction Extracting Segment with Skeleton","Automated lesion segmentation in PET/CT - domain generalization","In this task, we provide volumetric XNH images of cortical white matter axons from the mouse brain at 100 nm per voxel isotropic resolution. Additionally, we provide ground truth annotations for axon trajectories. Manual voxel-wise annotation of ground truth is a time-consuming bottleneck for training segmentation networks. On the other hand, skeleton-based ground truth is much faster to annotate, and sufficient to determine connectivity. Therefore, we encourage participants to develop methods to leverage skeleton-based training. To this end, we provide two types of training (validation) sets: a small volume of voxel-wise annotations and a larger volume with skeleton-based annotations. The participants will have the flexibility to use either or both of the provided annotations to train their models, and are challenged to submit an accurate voxel-wise prediction on the test volume. Entries will be evaluated on how accurately the submitted segmentations agree with the ground-truth s...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/721/XPRESS_logo_sq2-01.png","https://xpress.grand-challenge.org/","active","5","","","2023-02-06","\N","2023-11-08 00:42:00","2023-11-16 17:39:34" +"439","autopet-ii","autoPET-II","Automated lesion segmentation in PET/CT - domain generalization challenge","Positron Emission Tomography / Computed Tomography (PET/CT) is an integral part of the diagnostic workup for various malignant solid tumor entities. Due to its wide applicability, Fluorodeoxyglucose (FDG) is the most widely used PET tracer in an oncological setting reflecting glucose consumption of tissues, e.g. typically increased glucose consumption of tumor lesions. As part of the clinical routine analysis, PET/CT is mostly analyzed in a qualitative way by experienced medical imaging experts. Additional quantitative evaluation of PET information would potentially allow for more precise and individualized diagnostic decisions. A crucial initial processing step for quantitative PET/CT analysis is segmentation of tumor lesions enabling accurate feature extraction, tumor characterization, oncologic staging and image-based therapy response assessment. Manual lesion segmentation is however associated with enormous effort and cost and is thus infeasible in clinical routine. Automatio...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/722/autopet-5.png","https://autopet-ii.grand-challenge.org/","completed","5","","","2023-02-28","2023-09-24","2023-11-08 00:42:00","2023-11-15 21:45:49" +"440","toothfairy","ToothFairy: Cone-Beam Computed Tomography Segmentation Challenge","Toothfairy challenge: inferior alveolar canal segmentation","This is the first edition of the ToothFairy challenge organized by the University of Modena and Reggio Emilia with the collaboration of Raudboud University. This challenge aims at pushing the development of deep learning frameworks to segment the Inferior Alveolar Canal (IAC) by incrementally extending the amount of publicly available 3D-annotated Cone Beam Computed Tomography (CBCT) scans. CBCT modality is becoming increasingly important for treatment planning and diagnosis in implant dentistry and maxillofacial surgery. The three-dimensional information acquired with CBCT can be crucial to plan a vast number of surgical interventions with the aim of preserving noble anatomical structures such as the Inferior Alveolar Canal (IAC), which contains the homonymous nerve (Inferior Alveolar Nerve, IAN). Deep learning models can support medical personnel in surgical planning procedures by providing a voxel-level segmentation of the IAN automatically extracted from CBCT scans.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/723/logo.jpg","https://toothfairy.grand-challenge.org/","active","5","","","2023-06-30","\N","2023-11-08 00:42:00","2023-11-17 23:33:47" +"441","spider","SPIDER","Lumbar SPIDER challenge: MRI segmentation of spinal structures","The Lumbar SPIDER Challenge focuses on the segmentation of three anatomical structures in lumbar spine MRI: vertebrae, intervertebral discs (IVDs), and spinal canal. The segmentation task requires participants to produce separate masks for each vertebra, IVD, and the spinal canal in the lumbar spine MRI volume. The numbering of the vertebrae and IVDs is not specific and may vary across different cases.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/724/SPIDER_logo_square_jsl2NDu.png","https://spider.grand-challenge.org/","active","5","","","2023-07-26","2024-04-30","2023-11-08 00:42:00","2023-11-17 23:34:08" +"442","lnq2023","LNQ2023","3D lymph node segmentation for comprehensive disease evaluation","Accurate lymph node size estimation is critical for staging cancer patients, initial therapeutic management, and in longitudinal scans, assessing response to therapy. Current standard practice for quantifying lymph node size is based on a variety of criteria that use unidirectional or bidirectional measurements on just one or a few nodes, typically on just one axial slice. But humans have hundreds of lymph nodes, any number of which may be enlarged to various degrees due to disease or immune response. While a normal lymph node may be approximately 5mm in diameter, a diseased lymph node may be several cm in diameter. The mediastinum, the anatomical area between the lungs and around the heart, may contain ten or more lymph nodes, often with three or more enlarged greater than 1cm. Accurate segmentation in 3D would provide more information to evaluate lymph node disease.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/725/LNQ-square.png","https://lnq2023.grand-challenge.org/","completed","5","","","2023-05-01","2023-09-30","2023-11-08 00:42:00","2023-11-17 23:34:13" +"443","arcade","ARCADE-MICCAI2023","ARCADE 2023: automatic region-based coronary artery disease diagnostics","Coronary artery disease (CAD) is a condition that affects blood supply of heart, due to buildup of atherosclerotic plaque in the coronary arteries. CAD is one of the leading death causes around the world. The most common diagnosis procedure for CAD is coronary angiography, which uses contrast material and X-rays for observation of lesions in arteries, this type of procedure showing blood flow in coronary arteries in real time what allows precise detection of stenosis and control of intraventricular interventions and stent insertions. Coronary angiography is useful diagnostic method for planning necessary revascularization procedures based on calculated occlusion and affected segment of coronary arteries. The development of automated analytical tool for lesion detection and localization is a promising strategy for increasing effectiveness of detection and treatment strategies for CAD.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/726/aRCADE__1.jpg","https://arcade.grand-challenge.org/","completed","5","","","2023-06-07","2023-09-20","2023-11-08 00:42:00","2023-11-15 21:45:02" +"444","ultrasoundenhance2023","Ultrasound Image Enhancement challenge 2023","Ultrasound image enhancement challenge 2023","Ultrasound imaging is commonly used for aiding disease diagnosis and treatment, with advantages in noninvasive. Lately, medical ultrasound shows prospects revolving from expensive big-size machines in hospitals to economical hand-held devices in wider use. The barrier is that ultrasound examination with a handheld device has the drawback of low imaging quality due to hardware limitations. Toward this, ultrasound image enhancement provides a potential low-cost solution. Restoring high-quality images from low-quality ones using computer algorithms would exempt requirements for hardware improvements and promote ultrasound device revolutions and wider applications. We propose to hold the challenge of enhancement for ultrasound images in conjunction with MICCAI 2023. We will provide various ultrasound data of five organs, including the thyroid, carotid artery, liver, breast, and kidney. The challenging task is reconstructing high-quality ultrasound images from low-quality ones. A tota...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/727/logo.png","https://ultrasoundenhance2023.grand-challenge.org/","completed","5","","","2023-07-10","2023-08-31","2023-11-08 00:42:00","2023-11-15 21:44:45" +"445","multicenteraorta","SEG.A. - Segmentation of the Aorta","Aortic vessel tree segmentation challenge in CT images","Segmentation, modeling and visualization of the arterial tree are still a challenge in medical image analysis. The main track of this challenge deals with the fully automatic segmentation of the aortic vessel tree in computed tomography images. Optionally, teams can submit tailored solutions for meshing and visualization of the vessel tree.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/729/logo_final_miccai.jpg","https://multicenteraorta.grand-challenge.org/","completed","5","https://arxiv.org/abs/2108.02998","","2023-06-15","2023-08-15","2023-11-08 00:42:00","2023-11-17 23:34:24" +"446","sppin","Surgical Planning in Pediatric Neuroblastoma","Pediatric neuroblastoma surgical planning challenge","Neuroblastoma: Neuroblastoma is one of the most common cancers in children, accounting for 15% of pediatric cancer related deaths. This tumor originates from the symphatic nervous system, and is often located in the abdomen. Treatment of neuroblastoma includes surgical resection of the tumor, but complete resection of the tumor is often challenging. Surgical planning in Neuroblastoma: Surgical procedures can be complicated due to the neuroblastoma often being in proximity or even encasing organs and vessels in the affected area. These structures can include abdominal organs such as kidneys, liver, pancreas and spleen or big abdominal vessels such as the aorta and renal veins. During surgical planning it is essential to have a clear understanding of the neuroblastoma in relation to the relevant anatomy. Currently, magnetic resonance imaging (MRI) is used as pre-operative imaging. Studying 3D models of the tumor and relevant structures guides surgeons in the pre-operative understan...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/730/SPINN_Logo_SB_2023_03_11-09_TnwZJgK.png","https://sppin.grand-challenge.org/","completed","5","","","2023-08-10","2023-09-01","2023-11-08 00:42:00","2023-11-11 01:52:06" +"447","medfm2023","Foundation Model Prompting for Medical Image Classification","Model adaptation for medical image classification challenge","In the past few years, deep learning foundation models have been trendy, especially in computer vision and natural language processing. As a result, many milestone works have been proposed, such as Vision Transformers (ViT), Generative Pretrained Transformer (GPT), and Contrastive Language-Image Pretraining (CLIP). They aim to solve many downstream tasks by utilizing the robust representation learning and generalization abilities of foundation models.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/731/logo640.png","https://medfm2023.grand-challenge.org/","active","5","","","2023-07-14","2033-10-15","2023-11-08 00:42:00","2023-11-16 17:39:48" +"448","dentex","DENTEX - MICCAI23","Dental enumeration and diagnosis on panoramic x-rays","Panoramic X-rays are widely used in dental practice to provide a comprehensive view of the oral cavity and aid in treatment planning for various dental conditions. However, interpreting these images can be a time-consuming process that can distract clinicians from essential clinical activities. Moreover, misdiagnosis is a significant concern, as general practitioners may lack specialized training in radiology, and communication errors can occur due to work exhaustion.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/732/logo_diseased.png","https://dentex.grand-challenge.org/","completed","5","https://arxiv.org/abs/2303.06500","","2023-04-30","2023-09-01","2023-11-08 00:42:00","2023-11-16 17:39:45" +"449","segrap2023","SegRap 2023","Multi-modal CT image segmentation challenge with 45 OARs","Radiotherapy is one of the most important cancer treatments for killing cancer cells with external beam radiation. Treatment planning is vital for radiotherapy, which sets up the radiation dose distribution for tumors and ordinary organs. The goal of planning is to ensure the cancer cells receive enough radiation and to prevent normal cells in organs-at-risk (OARs) from being damaged too much. For instance, optical nerves and chiasma in the head cannot receive too much radiation. Otherwise, the patient risks losing his/her vision. Gross Target Volume (GTV) is the position and extent of gross tumor imaged by CT scans, i.e., what can be seen. A critical step in radiation treatment planning is to delineate the boundaries of GTV and tens of OARs. However, manual delineation slice-by-slice in CT scans is tedious and time-consuming for radiation oncologists. Automatic delineation of GTV and OARs would substantially reduce the treatment planning time and therefore improve the efficiency ...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/734/segrap-logo_cLCSrpZ.png","https://segrap2023.grand-challenge.org/","completed","5","","","2023-07-09","2023-09-13","2023-11-08 00:42:00","2023-11-16 17:39:47" +"450","ldctiqac2023","Low-dose Computed Tomography Perceptual Image Quality Assessment","Low-dose CT perceptual image quality assessment challenge","Image quality assessment (IQA) is extremely important in computed tomography (CT) imaging, since it facilitates the optimization of radiation dose and the development of novel algorithms in medical imaging, such as restoration. In addition, since an excessive dose of radiation can cause harmful effects in patients, generating high-quality images from low-dose images is a popular topic in the medical domain. However, even though peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are the most widely used evaluation metrics for these algorithms, their correlation with radiologists’ opinion of the image quality has been proven to be insufficient in previous studies, since they calculate the image score based on numeric pixel values (1-3). In addition, the need for pristine reference images to calculate these metrics makes them ineffective in real clinical environments, considering that pristine, high-quality images are often impossible to obtain due to th...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/735/IQA_logo_3io1BcW.png","https://ldctiqac2023.grand-challenge.org/","completed","5","","","2023-04-19","2023-07-28","2023-11-08 00:42:00","2023-11-16 17:39:58" +"451","cl-detection2023","CL-Detection 2023","Cephalometric landmark detection in lateral x-ray images","We invite you to participate in the CL-Detection 2023 cephalometric landmark detection challenge, which is held with 2023 MICCAI conference. Prof Wang is also hosting another challenge in MICCAI 2023. If you are seeking more publication opportunities, feel free to check the challenge website (Automated prediction of treatment effectiveness in ovarian cancer using histopathological images)","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/737/logo.png","https://cl-detection2023.grand-challenge.org/","completed","5","https://doi.org/10.1109/tmi.2015.2412951","","2023-05-31","2023-08-15","2023-11-08 00:42:00","2023-11-16 17:39:52" +"452","surgtoolloc23","Endoscopic surgical tool localization using tool presence labels","Endoscopic surgical tool localization challenge","The ability to automatically detect and track surgical instruments in endoscopic video will enable many transformational interventions. Assessing surgical performance and efficiency, identifying skilled tool use and choreography, and planning operational and logistical aspects of OR resources are just some of the applications that would benefit. The annotations needed to train machine learning models to robustly identify and localize surgical tools, however, are difficult to obtain. Annotating bounding boxes frame-by-frame in video is tedious and time consuming, yet a wide variety of surgical tools and surgeries must be captured for robust training. Moreover, ongoing annotator training is needed to stay up to date with surgical instrument innovation. In robot-assisted surgery, potentially informative data like timestamps of instrument installation and removal, can be programmatically harvested. The ability to use only tool presence labels to localize tools would significantly redu...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/738/grand-challenge_logo_2023.png","https://surgtoolloc23.grand-challenge.org/","completed","5","","","2023-06-29","2023-09-20","2023-11-08 00:42:00","2023-11-16 17:39:53" +"453","ocelot2023","OCELOT 2023: Cell Detection from Cell-Tissue Interaction","Cell detection from cell-tissue interaction","Cell detection in histology images is one of the most important tasks in computational pathology. Recently, the OCELOT dataset was released in [1] which provides overlapping cell and tissue annotations on images acquired from multiple organs stained with H&E. [1] showed that understanding the relationship between the surrounding tissue structures and individual cells can boost cell detection performance.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/739/gc_loco_XeBK92j.png","https://ocelot2023.grand-challenge.org/","completed","5","https://arxiv.org/abs/2303.13110","","2023-06-04","2023-08-04","2023-11-08 00:42:00","2023-11-16 17:39:56" +"454","thompson-challenge","The Trauma THOMPSON Challenge","Trauma thompson challenge: trauma image analysis","The primary goal of The Trauma THOMPSON Challenge is to find the best algorithms for automatic action recognition and prediction using computer vision from first-person view in the medical domain (refer to egocentric datasets of medical procedures). We offer the first egocentric view dataset of life-saving intervention (LSI) procedures with detailed annotations by medical professionals. We have collected over 200 procedure videos with environment, simulator, and type variability. Based on this dataset, the challenge we propose involves multiple tasks to encourage participants across the globe to design impactful algorithms with applications to medicine. The envisioned algorithms include action recognition, action anticipation, procedure recognition, and visual question answering (VQA).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/742/ttlog_black_-_Copy.png","https://thompson-challenge.grand-challenge.org/","completed","5","","","2023-09-01","\N","2023-11-08 00:42:00","2024-01-31 22:34:26" +"455","bonbid-hie2023","Hypoxic Ischemic Encephalopathy Lesion Segmentation Challenge","Hypoxic ischemic encephalopathy lesion segmentation","Hypoxic ischemic encephalopathy (HIE) is a brain injury that occurs in 1 ~ 5/1000 term-born neonates. HIE affects around 200,000 term-born neonates every year worldwide, costing about $2 billion/year in the US alone, let alone family burdens. Although therapeutic hypothermia can reduce mortality and morbidity, yet around 60% of patients still die or develop neurocognitive deficits by 2 years of age. HIE lesion segmentation is a crucial step in clinical care of HIE. It will lead to a more accurate estimation of prognosis, a better understanding of neurological symptoms, and a timely prediction of response to therapy.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/743/hie.png","https://bonbid-hie2023.grand-challenge.org/","completed","5","","","2023-07-01","2023-09-21","2023-11-08 00:42:00","2023-11-16 17:40:00" +"456","panorama","PANORAMA","AI and radiologists at pancreatic cancer diagnosis","The PANORAMA (Pancreatic cancer diagnosis: Radiologists meet AI) study is a new prospectively designed multi-center study with over 1500 cases, established in conjunction with an international, multi-disciplinary scientific advisory board (11 experts in pancreas radiology, AI and pancreatic cancer survivor representative) ⁠—to unify and standardize present-day guidelines and to ensure meaningful validation of pancreas-AI towards clinical translation (Reinke et al., 2022).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/746/panorama_logo_square.png","https://panorama.grand-challenge.org/","completed","5","","","\N","\N","2023-11-08 00:42:00","2023-11-16 17:40:02" +"457","cameo-3d","CAMEO-3D","Protein structure prediction quality assessment","Cameo Continuously Applies Quality Assessment Criteria Established By The Protein Structure Prediction Community. Since The Accuracy Requirements For Different Scientific Applications Vary, There Is No ""One Fits All"" Score. Cameo Therefore Offers A Variety Of Scores - Assessing Different Aspects Of A Prediction (Coverage, Local Accuracy, Completeness, Etc.) To Reflect These Requirements.","","https://www.cameo3d.org/modeling/3-months/","active","18","","","2023-11-04","\N","2023-11-11 01:29:20","2023-11-16 22:41:57" +"458","cameo-qe-model-quality-estimation","CAMEO-QE: Model Quality Estimation","Protein structure prediction quality assessment","Cameo Continuously Applies Quality Assessment Criteria Established By The Protein Structure Prediction Community. Since The Accuracy Requirements For Different Scientific Applications Vary, There Is No ""One Fits All"" Score. Cameo Therefore Offers A Variety Of Scores - Assessing Different Aspects Of A Prediction (Coverage, Local Accuracy, Completeness, Etc.) To Reflect These Requirements.","","https://www.cameo3d.org/quality-estimation/","active","18","","","2023-11-04","\N","2023-11-11 01:29:20","2023-11-16 22:41:57" +"459","cameo-3d-modeling-of-structures-and-complexes-beta","CAMEO-3D: Modeling of Structures & Complexes - BETA","Protein structure prediction quality assessment","Cameo Continuously Applies Quality Assessment Criteria Established By The Protein Structure Prediction Community. Since The Accuracy Requirements For Different Scientific Applications Vary, There Is No ""One Fits All"" Score. Cameo Therefore Offers A Variety Of Scores - Assessing Different Aspects Of A Prediction (Coverage, Local Accuracy, Completeness, Etc.) To Reflect These Requirements.","","https://beta.cameo3d.org/complete-modeling/","active","18","","","2023-11-04","\N","2023-11-11 01:29:20","2023-11-16 22:41:58" +"460","pegs-dream-challenge","PEGS DREAM Challenge","","","","https://www.synapse.org/pegs","upcoming","1","","","\N","\N","2023-11-13 22:48:02","2023-11-16 16:20:18" +"461","fda-data-centric-challenge","FDA Data-Centric Challenge","","The Food and Drug Administration (FDA) - Center for Devices and Radiological Health (CDRH), Sage Bionetworks, and precisionFDA call on the scientific, industry, and data science communities to develop methods to augment the training data and improve the robustness of a baseline artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD).","","https://www.synapse.org/fda_data_centric","upcoming","1","","","\N","\N","2023-11-13 22:49:41","2023-12-12 19:02:40" +"462","ai-institute-for-dynamic-systems","AI Institute for Dynamic Systems","","","","https://www.synapse.org/#!Synapse:syn52052735","upcoming","1","","","\N","\N","2023-11-13 22:51:53","2023-11-17 0:13:33" +"463","competition-nih-alzheimers-adrd-1","PREPARE Phase 1 - Find IT!","Help NIH break new ground in early Alzheimer's prediction and related dementias","The goal of the PREPARE Challenge (Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA Challenge) is to inform novel approaches to early detection that might ultimately lead to more accurate tests, tools, and methodologies for clinical and research purposes. Advances in artificial intelligence (AI), machine learning (ML), and computing ecosystems increase possibilities of intelligent data collection and analysis, including better algorithms and methods that could be leveraged for the prediction of biological, psychological (cognitive), socio-behavioral, functional, and clinical changes related to AD/ADRD. This first phase, Find IT!: Data for Early Prediction, is focused on finding, curating, or contributing data to create representative and open datasets that can be used for the early prediction of AD/ADRD.","","https://www.drivendata.org/competitions/253/competition-nih-alzheimers-adrd-1/","completed","19","","","2023-09-01","2024-01-31","2023-11-16 21:57:03","2023-12-06 7:15:18" +"464","prepare-phase-2-build-it","PREPARE Phase 2 - Build IT!","Help NIH break new ground in early Alzheimer's prediction and related dementias","The goal of the PREPARE Challenge (Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA Challenge) is to inform novel approaches to early detection that might ultimately lead to more accurate tests, tools, and methodologies for clinical and research purposes. Advances in artificial intelligence (AI), machine learning (ML), and computing ecosystems increase possibilities of intelligent data collection and analysis, including better algorithms and methods that could be leveraged for the prediction of biological, psychological (cognitive), socio-behavioral, functional, and clinical changes related to AD/ADRD. This second phase, Build IT!: Algorithms and Approaches, is focused on advancing algorithms and analytic approaches for early prediction of AD/ADRD, with an emphasis on explainability of predictions.","","","upcoming","19","","","2024-09-01","\N","2023-11-17 00:09:25","2023-12-06 7:18:18" +"465","prepare-phase-3-put-it-all-together","PREPARE Phase 3 - Put IT All Together!","Help NIH break new ground in early Alzheimer's prediction and related dementias","The goal of the PREPARE Challenge (Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA Challenge) is to inform novel approaches to early detection that might ultimately lead to more accurate tests, tools, and methodologies for clinical and research purposes. Advances in artificial intelligence (AI), machine learning (ML), and computing ecosystems increase possibilities of intelligent data collection and analysis, including better algorithms and methods that could be leveraged for the prediction of biological, psychological (cognitive), socio-behavioral, functional, and clinical changes related to AD/ADRD. This third phase, Put IT All Together!: Proof of Principle Demonstration, is for the top solvers from Phase 2 demonstrate algorithmic approaches on diverse datasets and share their results at an innovation event.","","","upcoming","19","","","2025-03-01","\N","2023-11-17 00:09:26","2023-12-06 7:18:08" +"466","cdc-fall-narratives","Unsupervised Wisdom: Explore Medical Narratives on Older Adult Falls","Extract insights about older adult falls from emergency department narratives","Falls among adults 65 and older is the leading cause of injury-related deaths. Falls can also result in serious injuries to the head and/or broken bones. Some risk factors associated with falls can be reduced through appropriate interventions like treating vision problems, exercising for strength and balance, and removing tripping hazards in your home. Medical record narratives are a rich yet under-explored source of potential insights about how, when, and why people fall. However, narrative data sources can be difficult to work with, often requiring carefully designed, time-intensive manual coding procedures. Modern machine learning approaches to working with narrative data have the potential to effectively extract insights about older adult falls from narrative medical record data at scale. The goal in this challenge is to identify effective methods of using unsupervised machine learning to extract insights about older adult falls from emergency department narratives. Insights...","https://drivendata-public-assets.s3.amazonaws.com/cdc-banner-hands.png","https://www.drivendata.org/competitions/217/cdc-fall-narratives/","completed","19","","","\N","2023-10-06","2023-12-06 06:56:06","2023-12-06 7:21:14" +"467","visiomel-melanoma","VisioMel Challenge: Predicting Melanoma Relapse","Use digitized microscopic slides to predict the likelihood of melanoma relapse","Melanoma is a cancer of the skin which develops from cells responsible for skin pigmentation. In 2020, over 325,000 people were diagnosed with skin melanoma, with 57,000 deaths in the same year.1 Melanomas represent 10% of all skin cancers and are the most dangerous due to high likelihood of metastasizing (spreading). Patients are initially diagnosed with melanoma after a pathologist examines a portion of the cancerous tissue under a microscope. At this stage, the pathologist assesses the risk of relapse—a return of cancerous cells after the melanoma has been treated—based on information such as the thickness of the tumor and the presence of an ulceration. Combined with factors such as age, sex, and medical history of the patient, these microscopic observations can help a dermatologist assess the severity of the disease and determine appropriate surgical and medical treatment. Preventative treatments can be administered to patients with high likelihood for relapse. However, these...","https://drivendata-public-assets.s3.amazonaws.com/visiomel_banner_img.jpeg","https://www.drivendata.org/competitions/1481/visiomel-melanoma/","completed","19","","","\N","2023-05-11","2023-12-06 07:35:00","2023-12-06 7:52:55" +"468","competition-cervical-biopsy","TissueNet: Detect Lesions in Cervical Biopsies","Detect the most severe epithelial lesions of the uterine cervix","A biopsy is a sample of tissue examined at a microscopic level to diagnose cancer or signs of pre-cancer. While most diagnoses are still made with photonic microscopes, digital pathology has developed considerably over the past decade as it has become possible to digitize slides into ""virtual slides"" or ""whole slide images"" (WSIs). These heavy image files contain all the information required to diagnose lesions as malignant or benign. Making this diagnosis is no easy task. It requires specialized training and careful examination of microscopic tissue. Approaches in machine learning are already able to help analyze WSIs by measuring or counting areas of the image under a pathologist's supervision. In addition, computer vision has shown some potential to classify tumor subtypes, and in time may offer a powerful tool to aid pathologists in making the most accurate diagnoses. This challenge focuses on epithelial lesions of the uterine cervix, and features a unique collection of thou...","https://s3.amazonaws.com/drivendata-public-assets/sfp_comp_image.jpg","https://www.drivendata.org/competitions/67/competition-cervical-biopsy/","completed","19","","","\N","2020-10-29","2023-12-06 07:52:34","2023-12-06 7:58:21" +"469","clog-loss-alzheimers-research","Clog Loss: Advance Alzheimer’s Research with Stall Catchers","Automatically classify which blood vessels are flowing and which are stalled","5.8 million Americans live with Alzheimer’s dementia, including 10% of all seniors 65 and older. Scientists at Cornell have discovered links between “stalls,” or clogged blood vessels in the brain, and Alzheimer’s. Stalls can reduce overall blood flow in the brain by 30%. The ability to prevent or remove stalls may transform how Alzheimer’s disease is treated. Stall Catchers is a citizen science project that crowdsources the analysis of Alzheimer’s disease research data provided by Cornell University’s Department of Biomedical Engineering. It resolves a pressing analytic bottleneck: for each hour of data collection it would take an entire week to analyze the results in the lab, which means an entire experimental dataset would take 6-12 months to analyze. Today, the Stall Catchers players are collectively analyzing data 5x faster than the lab while exceeding data quality requirements. The research team has realized there are aspects of this task that are best suited to uniquely h...","","https://www.drivendata.org/competitions/65/clog-loss-alzheimers-research/","completed","19","","","\N","2020-08-03","2023-12-06 08:04:52","2023-12-06 8:07:15" +"470","flu-shot-learning","Flu Shot Learning: Predict H1N1 and Seasonal Flu Vaccines","Predict whether people got H1N1 and flu vaccines using information they shared","In this challenge, we will take a look at vaccination, a key public health measure used to fight infectious diseases. Vaccines provide immunization for individuals, and enough immunization in a community can further reduce the spread of diseases through ""herd immunity."" As of the launch of this competition, vaccines for the COVID-19 virus are still under development and not yet available. The competition will instead revisit the public health response to a different recent major respiratory disease pandemic. Beginning in spring 2009, a pandemic caused by the H1N1 influenza virus, colloquially named ""swine flu,"" swept across the world. Researchers estimate that in the first year, it was responsible for between 151,000 to 575,000 deaths globally. A vaccine for the H1N1 flu virus became publicly available in October 2009. In late 2009 and early 2010, the United States conducted the National 2009 H1N1 Flu Survey. This phone survey asked respondents whether they had received the H1N1...","https://drivendata-public-assets.s3.amazonaws.com/flu-vaccine.jpg","https://www.drivendata.org/competitions/66/flu-shot-learning/","active","19","","","\N","2024-07-30","2023-12-06 08:10:49","2023-12-06 8:14:49" +"471","machine-learning-with-a-heart","Warm Up: Machine Learning with a Heart","Predict the presence or absence of heart disease in patients","We've all got to start somewhere. This is one of the smallest datasets on DrivenData. That makes it a great place to dive into the world of data science competitions. Get your heart thumping and try your hand at predicting heart disease.","","https://www.drivendata.org/competitions/54/machine-learning-with-a-heart/","completed","19","","","\N","2019-10-30","2023-12-06 08:19:47","2023-12-06 8:21:53" +"472","dengai-predicting-disease-spread","DengAI: Predicting Disease Spread","Predict the number of dengue fever cases reported each week in 2 regions","Using environmental data collected by various U.S. Federal Government agencies—from the Centers for Disease Control and Prevention to the National Oceanic and Atmospheric Administration in the U.S. Department of Commerce—can you predict the number of dengue fever cases reported each week in San Juan, Puerto Rico and Iquitos, Peru? This is an intermediate-level practice competition. Your task is to predict the number of dengue cases each week (in each location) based on environmental variables describing changes in temperature, precipitation, vegetation, and more. An understanding of the relationship between climate and dengue dynamics can improve research initiatives and resource allocation to help fight life-threatening pandemics.","","https://www.drivendata.org/competitions/44/dengai-predicting-disease-spread/","active","19","","","\N","2024-10-05","2023-12-06 08:28:42","2023-12-06 8:30:39" +"473","senior-data-science-safe-aging-with-sphere","Senior Data Science: Safe Aging with SPHERE","Predict actual activity from sensor data in seniors","This challenge is part of a large research project which centers around using sensors and algorithms to help older people live safely at home while maintaining their privacy and independence. Using passive, automated monitoring, the ultimate goal is to look out for a person's well-being without being burdensome or intrusive. To gather data, researchers in the SPHERE Inter-disciplinary Research Collaboration (IRC) equipped volunteers with accelerometers similar to those found in cell phones or fitness wearables, and then had the subjects go about normal activities of daily living in a home-like environment that was also equipped with motion detectors. After gathering a robust set of sensor data, they had multiple annotators use camera footage to establish the ground truth, labeling chunks of sensor data as one of twenty specifically chosen activities (e.g. walk, sit, stand-to-bend, ascend stairs, descend stairs, etc). Your challenge: help push forward the state of the art by pred...","","https://www.drivendata.org/competitions/42/senior-data-science-safe-aging-with-sphere/","completed","19","","","\N","2016-07-31","2023-12-06 08:35:31","2023-12-06 8:44:36" +"474","countable-care-modeling-womens-health-care-decisions","Countable Care: Modeling Women's Health Care Decisions","Predict what drives women’s health care decisions in America","Recent literature suggests that the demand for women’s health care will grow over 6% by 2020. Given how rapidly the health landscape has been changing over the last 15 years, it’s increasingly important that we understand how these changes affect what care people receive, where they go for it, and how they pay. Through the National Survey of Family Growth, the CDC provides one of the few nationally representative datasets that dives deep into the questions that women face when thinking about their health. Can you predict what drives women’s health care decisions in America?","","https://www.drivendata.org/competitions/6/countable-care-modeling-womens-health-care-decisions/","completed","19","","","\N","2015-04-14","2023-12-06 08:45:12","2023-12-06 8:46:00" +"475","warm-up-predict-blood-donations","Warm Up: Predict Blood Donations","Predict whether a donor will return to donate blood given their donation history","We've all got to start somewhere. This is the smallest, least complex dataset on DrivenData. That makes it a great place to dive into the world of data science competitions. Get your blood pumping and try your hand at predicting donations.","","https://www.drivendata.org/competitions/2/warm-up-predict-blood-donations/","completed","19","","","\N","2019-03-21","2023-12-06 08:52:21","2023-12-06 8:53:13" +"476","genetic-engineering-attribution","Genetic Engineering Attribution Challenge","Identify the lab-of-origin for genetically engineered DNA","our goal is to create an algorithm that identifies the most likely lab-of-origin for genetically engineered DNA. Applications for genetic engineering are rapidly diversifying. Researchers across the world are using powerful new techniques in synthetic biology to solve some of the world’s most pressing challenges in medicine, agriculture, manufacturing and more. At the same time, increasingly powerful genetically engineered systems could yield unintended consequences for people, food crops, livestock, and industry. These incredible advances in capability demand tools that support accountable innovation. Genetic engineering attribution is the process of identifying the source of a genetically engineered piece of DNA. This ability ensures that scientists who have spent countless hours developing breakthrough technology get their due credit, intellectual property is protected, and responsible innovation is promoted. By connecting a genetically engineered system with its designers, s...","https://s3.amazonaws.com/drivendata-public-assets/al-green-homepage.jpg","https://www.drivendata.org/competitions/63/genetic-engineering-attribution/","completed","19","","","\N","2020-10-19","2023-12-06 08:54:24","2023-12-06 8:56:29" +"477","neural-latents-benchmark-21","Neural Latents Benchmark '21","A benchmark on co-smoothing or inference of firing rates of unseen neurons","Advances in neural recording present increasing opportunities to study neural activity in unprecedented detail. Latent variable models (LVMs) are promising tools for analyzing this rich activity across diverse neural systems and behaviors, as LVMs do not depend on known relationships between the activity and external experimental variables. To coordinate LVM modeling efforts, we introduce the Neural Latents Benchmark (NLB). The first benchmark suite, NLB 2021, evaluates models on 7 datasets of neural spiking activity spanning 4 tasks and brain areas.","https://neurallatents.github.io/logo.svg","https://eval.ai/web/challenges/challenge-page/1256/overview","completed","16","","","\N","2022-04-03","2023-12-12 18:31:00","2023-12-12 22:39:42" +"478","brain-to-text-benchmark-24","Brain-to-Text Benchmark '24","Develop new and improved algorithms for decoding speech from the brain","People with ALS or brainstem stroke can lose the ability to move, rendering them “locked-in” their own bodies and unable to communicate. Speech brain-computer interfaces (BCIs) can restore communication by decoding what someone is trying to say directly from their brain activity. Once deciphered, the person’s intended message can be spoken for them or typed as text on a computer. We recently showed that a speech BCI can decode speech at 62 words per minute with a 23% word error rate, demonstrating the potential of a high-performance speech BCI. Nevertheless, word error rates are not yet low enough for fluent communication. The goal of this competition is to foster the development of new and improved algorithms for decoding speech from the brain. Improved accuracies will make it more likely that a speech BCI can be clinically translated, improving the lives of those with paralysis. We hope that this baseline can also serve as an indicator of progress in the field and provide a st...","https://evalai.s3.amazonaws.com/media/logos/35b2c474-c1be-41ae-97a4-49446766f9b1.png","https://eval.ai/web/challenges/challenge-page/2099/overview","active","16","","","2023-06-01","2024-06-01","2023-12-12 21:54:25","2023-12-12 22:38:33" +"479","vqa-answertherapy-2024","VQA-AnswerTherapy 2024","Grounding all answers for each visual question","Visual Question Answering (VQA) is a task of predicting the answer to a question about an image. Given that different people can provide different answers to a visual question, we aim to better understand why with answer groundings. To achieve this goal, we introduce the VQA-AnswerTherapy dataset, the first dataset that visually grounds each unique answer to each visual question. We offer this work as a valuable foundation for improving our understanding and handling of annotator differences. This work can inform how to account for annotator differences for other related tasks such as image captioning, visual dialog, and open-domain VQA (e.g., VQAs found on Yahoo!Answers and Stack Exchange). This work also contributes to ethical AI by enabling revisiting how VQA models are developed and evaluated to consider the diversity of plausible answer groundings rather than a single (typically majority) one.","https://evalai.s3.amazonaws.com/media/logos/e63bc0a0-cd35-4418-b32b-4ef2b9c61ce2.png","https://eval.ai/web/challenges/challenge-page/1910/overview","active","16","","","2024-01-30","2199-12-26","2023-12-12 22:41:48","2024-01-31 23:05:00" +"480","vqa-challenge-2021","VQA Challenge 2021","Answer open-ended, free-form natural language questions about images","Recent progress in computer vision and natural language processing has demonstrated that lower-level tasks are much closer to being solved. We believe that the time is ripe to pursue higher-level tasks, one of which is Visual Question Answering (VQA), where the goal is to be able to understand the semantics of scenes well enough to be able to answer open-ended, free-form natural language questions (asked by humans) about images. VQA Challenge 2021 is the 6th edition of the VQA Challenge on the VQA v2.0 dataset introduced in Goyal et al., CVPR 2017. The 2nd, 3rd, 4th and 5th editions of the VQA Challenge were organized in CVPR 2017, CVPR 2018, CVPR 2019 and CVPR 2020 on the VQA v2.0 dataset. The 1st edition of the VQA Challenge was organized in CVPR 2016 on the 1st edition (v1.0) of the VQA dataset introduced in Antol et al., ICCV 2015.","https://evalai.s3.amazonaws.com/media/logos/85d3b99e-b3a7-498a-a142-3325eab17138.png","https://eval.ai/web/challenges/challenge-page/830/overview","completed","16","","","2021-02-24","2021-05-07","2023-12-12 22:42:59","2023-12-12 23:00:07" +"481","ntx-hackathon-2023-sleep-states","NTX Hackathon 2023 - Sleep States","Speculate on possible use-cases of Neurotechnology and BCI","This competition is dedicated to advancing the use of machine learning and deep learning techniques in the realm of Brain-Computer Interface (BCI). It focuses on analyzing EEG data obtained from IDUN Guardian Earbuds. Electroencephalography (EEG) is a non-invasive method of recording electrical activity in the brain. Its high-resolution, real-time data is crucial in various clinical and consumer applications. In clinical environments, EEG is instrumental in diagnosing and monitoring neurological disorders like epilepsy, sleep disorders, and brain injuries. It's also used for assessing brain function in patients under anesthesia or in comas. The real-time aspect of EEG data is vital for clinicians to make informed decisions about diagnosis and treatment, such as pinpointing the onset and location of a seizure. Beyond clinical use, EEG has significant applications in understanding human cognition. Researchers utilize EEG to explore cognitive processes including attention, percepti...","https://miniodis-rproxy.lisn.upsaclay.fr/coda-v2-prod-public/logos/2023-12-02-1701542051/06a6dc054e4b/NTXHackathon23-Logo-Black-Blue-2048.png","https://www.codabench.org/competitions/1777/","completed","10","","","2023-12-01","2023-12-15","2023-12-12 23:22:24","2023-12-12 23:30:24" +"482","ai2healhackathon","UF AI2Heal 2024","Rebooting medicine with next-gen artificial intelligence","AI2Heal 2024 aims to showcase innovative applications of AI and other emerging technologies, including but not limited to augmented, virtual, and other forms of extended reality (AR/VR/XR), digital twins, and internet of things (IOT), that highlight the potential of digital health solutions and next-generation AI-enabled healthcare to improve patient outcomes and address inefficiencies in the healthcare system. The AI2Heal 2024 Challenges fall under three primary Domains: 1) Building the Health Metaverse; 2) Smart Hospitals and Remote Healthcare Delivery; and 3) Intelligent Cognitive Support","https://d112y698adiu2z.cloudfront.net/photos/production/challenge_photos/002/704/686/datas/full_width.png","https://ai2healhackathon.devpost.com/","active","20","","","2024-01-06","2024-02-24","2024-01-09 16:48:56","2024-01-31 22:52:19" +"483","data-hacks","Data Hacks: Equity in Healthcare","DataHacks with women in data science for middle and high schoolers","Hosted by CP Mentorship, 7EDU & Leadways School, Sponsored by WiDS - Women in Data Science & Microsoft, this one full day event is to support the worldwide Datathon competition, led by experts in data science fields. Winners will receive awards from Kaggle. Eligibility: middle to high schoolers (6th - 12th grade)","https://d112y698adiu2z.cloudfront.net/photos/production/challenge_photos/002/707/384/datas/full_width.png","https://data-hacks.devpost.com/","completed","20","","","2024-01-26","2024-01-27","2024-01-09 16:58:53","2024-01-11 23:46:37" +"484","mchacksnsbe","McHacks'24: Mario's Odyssey of Innovation","Spark change: innovate for mental health, education, feeding, and transport","Join our dynamic hackathon open to all students, regardless of major. This platform is designed to showcase your critical thinking and innovative prowess, enabling you to craft remarkable designs and comprehensive solutions for real-world challenges. Experience the power of collaboration as you team up with diverse talents, fostering an environment that promotes creative problem-solving. Beyond honing your skills, this hackathon serves as an ideal opportunity to engage with recruiters and companies, boosting your visibility within the workforce. Seize this avenue to shine, connect, and create a lasting impact. Your journey from idea to implementation starts here.","https://d112y698adiu2z.cloudfront.net/photos/production/challenge_photos/002/602/756/datas/full_width.jpg","https://mchacksnsbe.devpost.com/","completed","20","","","2024-02-10","2024-02-10","2024-01-09 18:36:27","2024-01-11 23:46:46" +"485","ml-hacks-19785","ML Hacks","Create AI/ML applications to revolutionize the fields of science, art, and more","Machine learning/AI are tools that can be applied to almost every domain, from communications to health. In this hackathon, we want you, the participant, to find unqiue and inspiring ways to leverage machine learning & AI to make projects in these domains, and in doing so, change the world for the better.","","https://ml-hacks-19785.devpost.com/","active","20","","","2024-02-16","2024-02-25","2024-01-09 18:45:21","2024-01-09 18:47:51" +"486","frosthacks","Frost Hacks","Embark on a winter innovation odyssey","Welcome to Frost Hacks, Hyderabad's first 30-hour hackathon in collaboration with Major League Hacking (MLH) - an International Student League. Our mission is to cultivate a dynamic and collaborative environment students can immerse themselves in project-based learning, all while having an unforgettable time.","https://d112y698adiu2z.cloudfront.net/photos/production/challenge_photos/002/675/245/datas/full_width.png","https://frosthacks.devpost.com/","completed","20","","","2024-01-20","2024-01-21","2024-01-09 18:48:59","2024-01-11 23:46:58" +"487","ncit-hackfest-2024","NCIT Hackfest 2024","Lethargy breaking initiative driven to energize and engage NCIT students","HackFest 2024 is the intitiative from NOSK, Nepal Open Source Klub. HackFest NCIT 2024 is 72 hour long running hackathon will be held on January 11-13 , Hack Fest 2024 is the lethargy breaking initiative driven to energize and engage NCIT to take active role and participation in the Tech domain through innovative and competitive events, competitions, workshops and techno centric activities. We call for hackers, artists, coders, designers, tech evangelists, creatives, and developers from all disciplines to take a part to win awesome prizes and collaborate with other developers .","https://d112y698adiu2z.cloudfront.net/photos/production/challenge_photos/002/707/493/datas/full_width.png","https://ncit-hackfest-2024.devpost.com/","completed","20","","","2024-01-11","2024-01-13","2024-01-09 18:51:10","2024-01-11 23:47:12" +"488","spot-the-mask","Spot the Mask Challenge","Can you predict whether a person in an image is wearing a face mask?","Face masks have become a common public sight in the last few months. The Centers for Disease Control (CDC) recently advised the use of simple cloth face coverings to slow the spread of the virus and help people who may have the virus and do not know it from transmitting it to others. Wearing masks is broadly recognised as critical to reducing community transmission and limiting touching of the face. In a time of concern about slowing the transmission of COVID-19, increased surveillance combined with AI solutions can improve monitoring and reduce the human effort needed to limit the spread of this disease. The objective of this challenge is to create an image classification machine learning model to accurately predict the likelihood that an image contains a person wearing a face mask, or not. The total dataset contains 1,800+ images of people either wearing masks or not. Your machine learning solution will help policymakers, law enforcement, hospitals, and even commercial busines...","","https://zindi.africa/competitions/spot-the-mask","active","21","","","\N","\N","2024-01-09 18:59:10","2024-01-09 19:08:11" +"489","to-vaccinate-or-not-to-vaccinate","To Vaccinate or Not to Vaccinate: It’s not a Question","Analysing social media sentiment towards vaccines","Work has already begun towards developing a COVID-19 vaccine. From measles to the common flu, vaccines have lowered the risk of illness and death, and have saved countless lives around the world. Unfortunately in some countries, the 'anti-vaxxer' movement has led to lower rates of vaccination and new outbreaks of old diseases. Although it may be many months before we see COVID-19 vaccines available on a global scale, it is important to monitor public sentiment towards vaccinations now and especially in the future when COVID-19 vaccines are offered to the public. The anti-vaccination sentiment could pose a serious threat to the global efforts to get COVID-19 under control in the long term. The objective of this challenge is to develop a machine learning model to assess if a Twitter post related to vaccinations is positive, neutral, or negative. This solution could help governments and other public health actors monitor public sentiment towards COVID-19 vaccinations and help impro...","","https://zindi.africa/competitions/to-vaccinate-or-not-to-vaccinate","active","21","","","\N","\N","2024-01-09 19:02:43","2024-01-09 19:08:08" +"490","computer-vision-for-image-classification","Computer Vision for Image Classification","Learning competition for CMU Data Science Club and AI Saturdays Kigali in Rwanda","This challenge was designed by Carnegie Mellon University Africa Data Science Club in Rwanda in partnership with AI Saturdays Kigali, specifically for the students of the Carnegie Mellon University and general AI saturdays Kigali community with a modified dataset from UCI machine Learning repository, which takes place between 16 February - 18 May. Welcome to the CMU students and AI Saturdays Kigali members! CMU Africa Data science club has made this competition open to the Zindi community to learn and test their skills. Anyone is welcome to enter this 'knowledge' challenge.","","https://zindi.africa/competitions/computer-vision-for-image-classification","active","21","","","\N","\N","2024-01-09 19:08:58","2024-01-09 19:10:01" +"491","space-medicine","Astropreneurship and Space Medicine","Hacking space issues with a focus on health","This is an event devoted to investigating space problems in healthcare! Due to COVID-19, this hackathon is mostly online, with the exception of the final presentatioN and prize ceremony which is being held at the Harvard Innovation Lab at Harvard Business School.","","https://space-medicine.devpost.com/","completed","20","","","2024-01-11","2024-01-17","2024-01-11 23:04:48","2024-01-11 23:07:06" +"492","biomarkers-of-aging-challenge","Biomarkers of Aging Challenge","Systematic evaluation and validation of biomarkers of aging","#bioage Systematic evaluation and validation of biomarkers of aging remains a significant challenge, yet these are essential prerequisites for their ultimate use in clinical trials for longevity interventions. Access to high quality omics datasets and disparate biomarker formulations are key roadblocks to reaching this goal. Moreover, there are disconnects between programming languages and methodologies favored by computational biologists and data scientists, which hinders the formation of transformative collaborations between such researchers. To enable innovation of the next generation of biomarkers of aging, we have built an open-source toolset called Biolearn. Biolearn is an unprecedented, one-stop, open-source platform for evaluation and validation of biomarkers of aging by curating and harmonizing large, high-quality omics and health datasets. We have built-in the ability to simultaneously calculate all currently available biomarkers of aging by harmonizing their formulatio...","banner/boac.png","https://www.synapse.org/#!Synapse:syn52966292/wiki/","active","1","","","2023-12-04","\N","2024-01-29 17:48:58","2024-02-05 16:58:24" +"493","pairboneage22","Project AIR - commercial AI for bone age prediction on hand XR","Bone age prediction on hand radiographs on a multicenter dataset","Head-to-head performance evaluation of commercially available AI products. This challenge shows the results for bone age prediction on hand radiographs on a multicenter dataset (seven centers) from the Netherlands.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/715/bone-age-hand-xr-logo.png","https://pairboneage22.grand-challenge.org/","completed","5","","","\N","2024-01-08","2024-01-31 22:49:24","2024-02-05 16:56:59" +"494","pairlungnodulexr22","Project AIR - commercial AI for lung nodule detection on CXR","Lung nodule detection on chest radiographs on a multicenter dataset","Head-to-head performance evaluation of commercially available AI products. This challenge shows the results for lung nodule detection on chest radiographs on a multicenter dataset (seven centers) from the Netherlands.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/741/lung-nodules-cxr-logo.png","https://pairlungnodulexr22.grand-challenge.org/","completed","5","","","\N","2024-01-08","2024-01-31 22:49:27","2024-02-05 16:57:02" +"495","justraigs","Justified Referral in AI Glaucoma Screening","AI-based screening for glaucoma","This challenge builds upon the success of AIROGS, that we organized in the context of ISBI 2022. This time, we ask participants to not only classify fundus images of eyes as referable glaucoma, but also to identify up to 10 different glaucomatous features in these images. The data set contains more than 110.000 carefully labeled fundus images. Participants can win up to € 3000 in this challenge!","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/749/Logo_XlR2vwf.png","https://justraigs.grand-challenge.org/","active","5","","","2024-01-08","2024-04-21","2024-01-31 22:49:29","2024-02-05 16:57:42" +"496","lightmycells","Light My Cells: Bright Field to Fluorescence Imaging Challenge","Enhance biology and microscopy","Join the Light My Cells France-Bioimaging challenge! Enhance biology and microscopy by contributing to the development of new image-to-image deep labelling methods. The task: predict the best-focused output images of several fluorescently labelled organelles from label-free transmitted light input images. Dive into the future of imaging with us! #LightMyCellsChallenge","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/750/logo_light_my_cells.png","https://lightmycells.grand-challenge.org/","upcoming","5","","","\N","\N","2024-01-31 22:49:33","2024-02-05 16:58:06" +"497","hack-rare-disease","Harvard Rare Disease Hackathon 2024","Are you a student interested in using AI/ML to tackle rare diseases? Join us!","This March 2-3, join us for the 2024 Harvard Rare Disease Hackathon, where students will gather on Harvard’s campus to set forth their own data-driven solutions for rare diseases. Participants will have the chance to analyze public and patient-sourced genomic and clinical datasets, and will be challenged to produce deliverables for participating patient organizations. These deliverables may take the form of a data report, computational tool, or web/mobile application that improves the lives of patients or furthers research progress. Participation is free and open to all undergraduate and graduate students who register with their .edu email address.","","https://www.harvard-rarediseases.org/","upcoming","\N","","","2024-03-02","2024-03-03","2024-02-06 00:12:34","2024-02-06 0:41:24" +"498","dreaming","Diminished Reality for Emerging Applications in Medicine through Inpainting","","The Diminished Reality for Emerging Applications in Medicine through Inpainting (DREAMING) challenge seeks to pioneer the integration of Diminished Reality (DR) into oral and maxillofacial surgery. While Augmented Reality (AR) has been extensively explored in medicine, DR remains largely uncharted territory. DR involves virtually removing real objects from the environment by replacing them with their background. Recent inpainting methods present an opportunity for real-time DR applications without scene knowledge. DREAMING focuses on implementing such methods to fill obscured regions in surgery scenes with realistic backgrounds, emphasizing the complex facial anatomy and patient diversity. The challenge provides a dataset of synthetic yet photorealistic surgery scenes featuring humans, simulating an operating room setting. Participants are tasked with developing algorithms that seamlessly remove disruptions caused by medical instruments and hands, offering surgeons an unimpeded ...","https://rumc-gcorg-p-public.s3.amazonaws.com/b/752/isbi_dreaming_banner_gc_297CU3H.x10.jpeg","https://dreaming.grand-challenge.org/","active","5","","","2024-01-08","2024-04-27","2024-02-12 21:56:27","2024-02-12 22:00:06" From e7bdf548b4510f36f1434f4c761cdff4b2056896 Mon Sep 17 00:00:00 2001 From: verena <9377970+vpchung@users.noreply.github.com> Date: Sat, 17 Feb 2024 00:29:31 +0000 Subject: [PATCH 07/14] update script that dumps db csv files to include `operation` --- apps/openchallenges/db-update/update_db_csv.py | 1 + 1 file changed, 1 insertion(+) diff --git a/apps/openchallenges/db-update/update_db_csv.py b/apps/openchallenges/db-update/update_db_csv.py index 888db4d3a2..4c29545e34 100644 --- a/apps/openchallenges/db-update/update_db_csv.py +++ b/apps/openchallenges/db-update/update_db_csv.py @@ -51,6 +51,7 @@ def get_challenge_data(wks, sheet_name="challenges"): "status", "platform", "doi", + "operation", "start_date", "end_date", "created_at", From 00c492422767b7d65ef66cdd78f5e95816888108 Mon Sep 17 00:00:00 2001 From: verena <9377970+vpchung@users.noreply.github.com> Date: Sat, 17 Feb 2024 00:30:35 +0000 Subject: [PATCH 08/14] update openapi specs --- .../api-description/build/challenge.openapi.yaml | 14 +++++++------- .../api-description/build/openapi.yaml | 14 +++++++------- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/libs/openchallenges/api-description/build/challenge.openapi.yaml b/libs/openchallenges/api-description/build/challenge.openapi.yaml index 3cde8ae773..f17f86b6d5 100644 --- a/libs/openchallenges/api-description/build/challenge.openapi.yaml +++ b/libs/openchallenges/api-description/build/challenge.openapi.yaml @@ -382,13 +382,6 @@ components: minLength: 0 maxLength: 1000 example: This is an example description of the challenge. - ChallengeOperation: - description: The EDAM operation class of the challenge. - type: string - maxLength: 16 - pattern: ^$|^operation_\d+$ - nullable: true - example: operation_0004 ChallengeDoi: description: The DOI of the challenge. type: string @@ -396,6 +389,13 @@ components: maxLength: 120 nullable: true example: https://doi.org/123/abc + ChallengeOperation: + description: The EDAM operation class of the challenge. + type: string + maxLength: 16 + pattern: ^$|^operation_\d+$ + nullable: true + example: operation_0004 ChallengePlatformId: description: The unique identifier of a challenge platform. type: integer diff --git a/libs/openchallenges/api-description/build/openapi.yaml b/libs/openchallenges/api-description/build/openapi.yaml index 628e133f95..9d3c213a3f 100644 --- a/libs/openchallenges/api-description/build/openapi.yaml +++ b/libs/openchallenges/api-description/build/openapi.yaml @@ -534,13 +534,6 @@ components: minLength: 0 maxLength: 1000 example: This is an example description of the challenge. - ChallengeOperation: - description: The EDAM operation class of the challenge. - type: string - maxLength: 16 - pattern: ^$|^operation_\d+$ - nullable: true - example: operation_0004 ChallengeDoi: description: The DOI of the challenge. type: string @@ -548,6 +541,13 @@ components: maxLength: 120 nullable: true example: 'https://doi.org/123/abc' + ChallengeOperation: + description: The EDAM operation class of the challenge. + type: string + maxLength: 16 + pattern: ^$|^operation_\d+$ + nullable: true + example: operation_0004 ChallengePlatformId: description: The unique identifier of a challenge platform. type: integer From 8f1367d78b9493d62a28f21b5b6d7d171f299d21 Mon Sep 17 00:00:00 2001 From: verena <9377970+vpchung@users.noreply.github.com> Date: Sat, 17 Feb 2024 00:42:42 +0000 Subject: [PATCH 09/14] use `operation_id` instead of `operation` --- .../challenge-service/src/main/resources/db/challenges.csv | 2 +- .../main/resources/db/migration/V1.0.0__create_tables.sql | 2 +- apps/openchallenges/db-update/update_db_csv.py | 2 +- .../api-description/build/challenge.openapi.yaml | 6 +++--- libs/openchallenges/api-description/build/openapi.yaml | 6 +++--- .../api-description/src/components/schemas/Challenge.yaml | 4 ++-- .../{ChallengeOperation.yaml => ChallengeOperationId.yaml} | 0 7 files changed, 11 insertions(+), 11 deletions(-) rename libs/openchallenges/api-description/src/components/schemas/{ChallengeOperation.yaml => ChallengeOperationId.yaml} (100%) diff --git a/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv b/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv index 443279a94c..94626a9d4e 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv +++ b/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv @@ -1,4 +1,4 @@ -"id","slug","name","headline","description","avatar_url","website_url","status","platform","doi","operation","start_date","end_date","created_at","updated_at" +"id","slug","name","headline","description","avatar_url","website_url","status","platform","doi","operation_id","start_date","end_date","created_at","updated_at" "1","network-topology-and-parameter-inference","Network Topology and Parameter Inference","Optimize methods to estimate biology model parameters","Participants are asked to develop and/or apply optimization methods, including the selection of the most informative experiments, to accurately estimate parameters and predict outcomes of perturbations in Systems Biology models.","","https://www.synapse.org/#!Synapse:syn2821735","completed","1","","","2012-06-01","2012-10-01","2023-11-15 22:40:15","2023-11-16 18:31:42" "2","breast-cancer-prognosis","Breast Cancer Prognosis","Predict breast cancer survival from clinical and genomic data","The goal of the breast cancer prognosis Challenge is to assess the accuracy of computational models designed to predict breast cancer survival, based on clinical information about the patient's tumor as well as genome-wide molecular profiling data including gene expression and copy number profiles.","","https://www.synapse.org/#!Synapse:syn2813426","completed","1","","","2012-07-12","2012-10-15","2023-11-14 20:36:32","2023-11-14 20:17:33" "3","phil-bowen-als-prediction-prize4life","Phil Bowen ALS Prediction Prize4Life","Seeking treatment to halt ALS's fatal loss of motor function","Amyotrophic Lateral Sclerosis (ALS), or Lou Gehrig's disease, is a fatal neurological condition causing the death of nerve cells in the brain and spinal cord, resulting in a progressive loss of motor function while cognitive functions persist. Typically emerging around age 50, it affects about five in 100,000 people worldwide, with familial hereditary forms as the only known risk factors (5-10% of cases). There is currently no cure for ALS. The FDA-approved drug Riluzole extends life by a few months. ALS patients, on average, have a life expectancy of 2-5 years, with 10% experiencing slower disease progression. Astrophysicist Stephen Hawking, living with ALS for 49 years, is an exceptional case. The DREAM-Phil Bowen ALS Prediction Prize4Life, or ""ALS Prediction Prize,"" utilizes the PRO-ACT database with clinical data from over 7,500 ALS patients. This collaboration with DREAM aims to expedite ALS treatment discovery. Prize4Life, a non-profit, collaborates with NEALS and ALS Ther...","","https://www.synapse.org/#!Synapse:syn2826267","completed","1","","","2012-06-01","2012-10-01","2023-11-01 22:09:02","2023-11-13 17:16:16" diff --git a/apps/openchallenges/challenge-service/src/main/resources/db/migration/V1.0.0__create_tables.sql b/apps/openchallenges/challenge-service/src/main/resources/db/migration/V1.0.0__create_tables.sql index d87536cbd3..96c9539152 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/db/migration/V1.0.0__create_tables.sql +++ b/apps/openchallenges/challenge-service/src/main/resources/db/migration/V1.0.0__create_tables.sql @@ -37,7 +37,7 @@ CREATE TABLE `challenge` `status` ENUM('upcoming', 'active', 'completed'), `platform_id` int, `doi` varchar(120), - `operation` varchar(16), + `operation_id` varchar(16), `start_date` DATE, `end_date` DATE, `created_at` DATETIME DEFAULT CURRENT_TIMESTAMP, diff --git a/apps/openchallenges/db-update/update_db_csv.py b/apps/openchallenges/db-update/update_db_csv.py index 4c29545e34..f8fce2606c 100644 --- a/apps/openchallenges/db-update/update_db_csv.py +++ b/apps/openchallenges/db-update/update_db_csv.py @@ -51,7 +51,7 @@ def get_challenge_data(wks, sheet_name="challenges"): "status", "platform", "doi", - "operation", + "operation_id", "start_date", "end_date", "created_at", diff --git a/libs/openchallenges/api-description/build/challenge.openapi.yaml b/libs/openchallenges/api-description/build/challenge.openapi.yaml index f17f86b6d5..eb175c12e7 100644 --- a/libs/openchallenges/api-description/build/challenge.openapi.yaml +++ b/libs/openchallenges/api-description/build/challenge.openapi.yaml @@ -389,7 +389,7 @@ components: maxLength: 120 nullable: true example: https://doi.org/123/abc - ChallengeOperation: + ChallengeOperationId: description: The EDAM operation class of the challenge. type: string maxLength: 16 @@ -495,8 +495,8 @@ components: $ref: '#/components/schemas/ChallengeDescription' doi: $ref: '#/components/schemas/ChallengeDoi' - operation: - $ref: '#/components/schemas/ChallengeOperation' + operationId: + $ref: '#/components/schemas/ChallengeOperationId' status: $ref: '#/components/schemas/ChallengeStatus' platform: diff --git a/libs/openchallenges/api-description/build/openapi.yaml b/libs/openchallenges/api-description/build/openapi.yaml index 9d3c213a3f..a94992207e 100644 --- a/libs/openchallenges/api-description/build/openapi.yaml +++ b/libs/openchallenges/api-description/build/openapi.yaml @@ -541,7 +541,7 @@ components: maxLength: 120 nullable: true example: 'https://doi.org/123/abc' - ChallengeOperation: + ChallengeOperationId: description: The EDAM operation class of the challenge. type: string maxLength: 16 @@ -647,8 +647,8 @@ components: $ref: '#/components/schemas/ChallengeDescription' doi: $ref: '#/components/schemas/ChallengeDoi' - operation: - $ref: '#/components/schemas/ChallengeOperation' + operationId: + $ref: '#/components/schemas/ChallengeOperationId' status: $ref: '#/components/schemas/ChallengeStatus' platform: diff --git a/libs/openchallenges/api-description/src/components/schemas/Challenge.yaml b/libs/openchallenges/api-description/src/components/schemas/Challenge.yaml index 2fdbc95b1d..9381760a44 100644 --- a/libs/openchallenges/api-description/src/components/schemas/Challenge.yaml +++ b/libs/openchallenges/api-description/src/components/schemas/Challenge.yaml @@ -15,8 +15,8 @@ properties: # type: array # items: $ref: ChallengeDoi.yaml - operation: - $ref: ChallengeOperation.yaml + operationId: + $ref: ChallengeOperationId.yaml status: $ref: ChallengeStatus.yaml platform: diff --git a/libs/openchallenges/api-description/src/components/schemas/ChallengeOperation.yaml b/libs/openchallenges/api-description/src/components/schemas/ChallengeOperationId.yaml similarity index 100% rename from libs/openchallenges/api-description/src/components/schemas/ChallengeOperation.yaml rename to libs/openchallenges/api-description/src/components/schemas/ChallengeOperationId.yaml From d6da0fa6b94a3a933df23d8465f10e879840b48b Mon Sep 17 00:00:00 2001 From: verena <9377970+vpchung@users.noreply.github.com> Date: Sat, 17 Feb 2024 00:50:43 +0000 Subject: [PATCH 10/14] prepare to display operation on profile --- .../challenge-overview/challenge-overview.component.html | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/libs/openchallenges/challenge/src/lib/challenge-overview/challenge-overview.component.html b/libs/openchallenges/challenge/src/lib/challenge-overview/challenge-overview.component.html index a900fa808c..e32e508497 100644 --- a/libs/openchallenges/challenge/src/lib/challenge-overview/challenge-overview.component.html +++ b/libs/openchallenges/challenge/src/lib/challenge-overview/challenge-overview.component.html @@ -49,6 +49,12 @@

Challenge Details

Not available + Submission Type Date: Sat, 17 Feb 2024 00:56:02 +0000 Subject: [PATCH 11/14] remove extra whitespace --- .../src/main/resources/db/migration/V1.0.0__create_tables.sql | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/apps/openchallenges/challenge-service/src/main/resources/db/migration/V1.0.0__create_tables.sql b/apps/openchallenges/challenge-service/src/main/resources/db/migration/V1.0.0__create_tables.sql index 96c9539152..882a57db0a 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/db/migration/V1.0.0__create_tables.sql +++ b/apps/openchallenges/challenge-service/src/main/resources/db/migration/V1.0.0__create_tables.sql @@ -37,7 +37,7 @@ CREATE TABLE `challenge` `status` ENUM('upcoming', 'active', 'completed'), `platform_id` int, `doi` varchar(120), - `operation_id` varchar(16), + `operation_id` varchar(16), `start_date` DATE, `end_date` DATE, `created_at` DATETIME DEFAULT CURRENT_TIMESTAMP, From 1cbcb586b9f777558bc0cd0311b16ae2048a51a5 Mon Sep 17 00:00:00 2001 From: verena <9377970+vpchung@users.noreply.github.com> Date: Tue, 20 Feb 2024 00:03:06 +0000 Subject: [PATCH 12/14] update java files + openAPI spec --- .../service/api/ChallengeApiDelegate.java | 2 +- .../service/model/dto/ChallengeDto.java | 31 +++++++++++++++++++ .../src/main/resources/openapi.yaml | 15 +++++++++ 3 files changed, 47 insertions(+), 1 deletion(-) diff --git a/apps/openchallenges/challenge-service/src/main/java/org/sagebionetworks/openchallenges/challenge/service/api/ChallengeApiDelegate.java b/apps/openchallenges/challenge-service/src/main/java/org/sagebionetworks/openchallenges/challenge/service/api/ChallengeApiDelegate.java index dd53f24c45..51e7a92347 100644 --- a/apps/openchallenges/challenge-service/src/main/java/org/sagebionetworks/openchallenges/challenge/service/api/ChallengeApiDelegate.java +++ b/apps/openchallenges/challenge-service/src/main/java/org/sagebionetworks/openchallenges/challenge/service/api/ChallengeApiDelegate.java @@ -36,7 +36,7 @@ default ResponseEntity getChallenge(Long challengeId) { for (MediaType mediaType : MediaType.parseMediaTypes(request.getHeader("Accept"))) { if (mediaType.isCompatibleWith(MediaType.valueOf("application/json"))) { String exampleString = - "{ \"avatarUrl\" : \"https://openchallenges.io\", \"endDate\" : \"2017-07-21T00:00:00.000+00:00\", \"description\" : \"This is an example description of the challenge.\", \"platform\" : { \"name\" : \"name\", \"id\" : 1, \"slug\" : \"example-challenge-platform\" }, \"starredCount\" : 100, \"createdAt\" : \"2022-07-04T22:19:11Z\", \"incentives\" : [ \"publication\", \"publication\" ], \"submissionTypes\" : [ \"container_image\", \"container_image\" ], \"websiteUrl\" : \"https://openchallenges.io\", \"name\" : \"name\", \"id\" : 1, \"categories\" : [ \"featured\", \"featured\" ], \"headline\" : \"Example challenge headline\", \"slug\" : \"awesome-challenge\", \"startDate\" : \"2017-07-21T00:00:00.000+00:00\", \"doi\" : \"https://doi.org/123/abc\", \"status\" : \"active\", \"inputDataTypes\" : [ { \"name\" : \"gene expression\", \"id\" : 1, \"slug\" : \"gene-expression\" }, { \"name\" : \"gene expression\", \"id\" : 1, \"slug\" : \"gene-expression\" } ], \"updatedAt\" : \"2022-07-04T22:19:11Z\" }"; + "{ \"avatarUrl\" : \"https://openchallenges.io\", \"endDate\" : \"2017-07-21T00:00:00.000+00:00\", \"description\" : \"This is an example description of the challenge.\", \"platform\" : { \"name\" : \"name\", \"id\" : 1, \"slug\" : \"example-challenge-platform\" }, \"starredCount\" : 100, \"createdAt\" : \"2022-07-04T22:19:11Z\", \"incentives\" : [ \"publication\", \"publication\" ], \"submissionTypes\" : [ \"container_image\", \"container_image\" ], \"websiteUrl\" : \"https://openchallenges.io\", \"name\" : \"name\", \"operationId\" : \"operation_0004\", \"id\" : 1, \"categories\" : [ \"featured\", \"featured\" ], \"headline\" : \"Example challenge headline\", \"slug\" : \"awesome-challenge\", \"startDate\" : \"2017-07-21T00:00:00.000+00:00\", \"doi\" : \"https://doi.org/123/abc\", \"status\" : \"active\", \"inputDataTypes\" : [ { \"name\" : \"gene expression\", \"id\" : 1, \"slug\" : \"gene-expression\" }, { \"name\" : \"gene expression\", \"id\" : 1, \"slug\" : \"gene-expression\" } ], \"updatedAt\" : \"2022-07-04T22:19:11Z\" }"; ApiUtil.setExampleResponse(request, "application/json", exampleString); break; } diff --git a/apps/openchallenges/challenge-service/src/main/java/org/sagebionetworks/openchallenges/challenge/service/model/dto/ChallengeDto.java b/apps/openchallenges/challenge-service/src/main/java/org/sagebionetworks/openchallenges/challenge/service/model/dto/ChallengeDto.java index 653c93b9cb..bf93090aef 100644 --- a/apps/openchallenges/challenge-service/src/main/java/org/sagebionetworks/openchallenges/challenge/service/model/dto/ChallengeDto.java +++ b/apps/openchallenges/challenge-service/src/main/java/org/sagebionetworks/openchallenges/challenge/service/model/dto/ChallengeDto.java @@ -39,6 +39,9 @@ public class ChallengeDto { @JsonProperty("doi") private String doi = null; + @JsonProperty("operationId") + private String operationId = null; + @JsonProperty("status") private ChallengeStatusDto status; @@ -230,6 +233,31 @@ public void setDoi(String doi) { this.doi = doi; } + public ChallengeDto operationId(String operationId) { + this.operationId = operationId; + return this; + } + + /** + * The EDAM operation class of the challenge. + * + * @return operationId + */ + @Pattern(regexp = "^$|^operation_\\d+$") + @Size(max = 16) + @Schema( + name = "operationId", + example = "operation_0004", + description = "The EDAM operation class of the challenge.", + required = false) + public String getOperationId() { + return operationId; + } + + public void setOperationId(String operationId) { + this.operationId = operationId; + } + public ChallengeDto status(ChallengeStatusDto status) { this.status = status; return this; @@ -572,6 +600,7 @@ public boolean equals(Object o) { && Objects.equals(this.headline, challenge.headline) && Objects.equals(this.description, challenge.description) && Objects.equals(this.doi, challenge.doi) + && Objects.equals(this.operationId, challenge.operationId) && Objects.equals(this.status, challenge.status) && Objects.equals(this.platform, challenge.platform) && Objects.equals(this.websiteUrl, challenge.websiteUrl) @@ -596,6 +625,7 @@ public int hashCode() { headline, description, doi, + operationId, status, platform, websiteUrl, @@ -621,6 +651,7 @@ public String toString() { sb.append(" headline: ").append(toIndentedString(headline)).append("\n"); sb.append(" description: ").append(toIndentedString(description)).append("\n"); sb.append(" doi: ").append(toIndentedString(doi)).append("\n"); + sb.append(" operationId: ").append(toIndentedString(operationId)).append("\n"); sb.append(" status: ").append(toIndentedString(status)).append("\n"); sb.append(" platform: ").append(toIndentedString(platform)).append("\n"); sb.append(" websiteUrl: ").append(toIndentedString(websiteUrl)).append("\n"); diff --git a/apps/openchallenges/challenge-service/src/main/resources/openapi.yaml b/apps/openchallenges/challenge-service/src/main/resources/openapi.yaml index 3acd2da00c..a232537793 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/openapi.yaml +++ b/apps/openchallenges/challenge-service/src/main/resources/openapi.yaml @@ -577,6 +577,13 @@ components: maxLength: 120 nullable: true type: string + ChallengeOperationId: + description: The EDAM operation class of the challenge. + example: operation_0004 + maxLength: 16 + nullable: true + pattern: ^$|^operation_\d+$ + type: string ChallengePlatformId: description: The unique identifier of a challenge platform. example: 1 @@ -711,6 +718,7 @@ components: - container_image websiteUrl: https://openchallenges.io name: name + operationId: operation_0004 id: 1 categories: - featured @@ -766,6 +774,13 @@ components: maxLength: 120 nullable: true type: string + operationId: + description: The EDAM operation class of the challenge. + example: operation_0004 + maxLength: 16 + nullable: true + pattern: ^$|^operation_\d+$ + type: string status: $ref: '#/components/schemas/ChallengeStatus' platform: From fecf4999ebc9df1c34d97e5afd666128efff34c5 Mon Sep 17 00:00:00 2001 From: verena <9377970+vpchung@users.noreply.github.com> Date: Tue, 20 Feb 2024 06:54:47 +0000 Subject: [PATCH 13/14] update angular API client --- .../api-client-angular/src/lib/model/challenge.ts | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/libs/openchallenges/api-client-angular/src/lib/model/challenge.ts b/libs/openchallenges/api-client-angular/src/lib/model/challenge.ts index 8f214c7588..7c89dbc423 100644 --- a/libs/openchallenges/api-client-angular/src/lib/model/challenge.ts +++ b/libs/openchallenges/api-client-angular/src/lib/model/challenge.ts @@ -45,6 +45,10 @@ export interface Challenge { * The DOI of the challenge. */ doi?: string | null; + /** + * The EDAM operation class of the challenge. + */ + operationId?: string | null; status: ChallengeStatus; platform?: SimpleChallengePlatform | null; /** From 0232f3e14403aec9663b65287cd3475893f461b0 Mon Sep 17 00:00:00 2001 From: verena <9377970+vpchung@users.noreply.github.com> Date: Tue, 20 Feb 2024 07:18:40 +0000 Subject: [PATCH 14/14] uncomment HTML code --- .../challenge-overview/challenge-overview.component.html | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/libs/openchallenges/challenge/src/lib/challenge-overview/challenge-overview.component.html b/libs/openchallenges/challenge/src/lib/challenge-overview/challenge-overview.component.html index e32e508497..e50667e73b 100644 --- a/libs/openchallenges/challenge/src/lib/challenge-overview/challenge-overview.component.html +++ b/libs/openchallenges/challenge/src/lib/challenge-overview/challenge-overview.component.html @@ -49,12 +49,12 @@

Challenge Details

Not available - + Submission Type