From c928aa5849a690b3e393ceb6f8bc42b3a7b0ce97 Mon Sep 17 00:00:00 2001 From: Verena Chung <9377970+vpchung@users.noreply.github.com> Date: Fri, 20 Oct 2023 10:36:04 -0700 Subject: [PATCH] feat(openchallenges): 2023-10 DB update (#2255) * use snake case for column headers * only add platforms with challenge count to db * other general updates * fix datetime format * fix REF error --- .../src/main/resources/db/challenges.csv | 100 ++-- .../main/resources/db/contribution_roles.csv | 90 ++-- .../src/main/resources/db/incentives.csv | 482 ++++++++++-------- .../src/main/resources/db/platforms.csv | 33 +- .../main/resources/db/submission_types.csv | 27 +- .../main/resources/db/contribution_roles.csv | 90 ++-- .../src/main/resources/db/organizations.csv | 3 +- 7 files changed, 431 insertions(+), 394 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 0208486e99..d86a98baa2 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv +++ b/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv @@ -1,8 +1,8 @@ -"id","slug","name","headline","description","avatar_url","website_url","status","difficulty","platform","doi","start_date","end_date","createdAt","updatedAt" -"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","2023-10-18 00:40:10" +"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","2023-10-18 00:38:47" +"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" @@ -33,8 +33,8 @@ "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","2023-10-14 05:38:38" -"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","2023-10-14 05:38:39" +"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","","NCI Genomic Data Commons,Harvard Chan School, Blue Collar Bioinformatics,ENCODE DCC, Stanford,CCHMC, Barski Lab,KnowEnG, UIUC,UCSC,OICR, ICGC,Broad Institute, GATK","","https://www.synapse.org/#!Synapse:syn20968331","completed","good_for_beginners","1","","2019-12-02","2020-02-13","2023-06-23 00:00:00","2023-10-14 05:38:41" "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" @@ -57,10 +57,10 @@ "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","2023-10-16 18:24:57" -"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","2023-10-16 18:25:06" -"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","2023-10-14 05:39:03" -"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","2023-10-14 05:39:03" +"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" @@ -69,10 +69,10 @@ "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","2023-10-14 05:39:11" -"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","2023-10-14 05:39:13" -"73","cagi6-sickkids","CAGI6: SickKids clinical genomes and transcriptomes","","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 0:46:11" -"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 00:40:24" +"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" @@ -111,7 +111,7 @@ "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","2023-10-14 05:39:47" +"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" @@ -162,7 +162,7 @@ "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","2023-10-12 18:15:05" +"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" @@ -208,25 +208,25 @@ "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","2023-09-29 0-08-46" +"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","2023-09-13 0-41-58","2023-09-29 0-12-18" -"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","2023-09-29 0-14-04" -"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","2023-09-29 0-14-06" -"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","2023-09-29 0-16-54" +"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","upcoming","intermediate","14","","2023-10-19","2023-10-21","2023-09-13 23:32:59","2023-09-27 21:08:26" +"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","2023-09-29 3-58-34" +"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","2023-09-29 3-58-32" +"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","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" @@ -245,36 +245,36 @@ "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","2023-09-29 3-55-55" -"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","2023-09-29 3-55-57" -"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","2023-09-29 3-55-57" +"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","2023-09-29 3-55-59" +"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","2023-09-29 3-56-03" -"258","cagi6-sickkids6","CAGI6: SickKids6","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","","\N","2021-12-31","2023-09-28 18:19:48","2023-09-27 21:05:30" -"259","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","2023-09-29 3-56-03" -"260","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" -"261","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" -"262","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" -"263","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" -"264","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" -"265","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" -"266","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" -"267","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","2023-09-29 3-55-21" -"268","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" -"269","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" -"270","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" -"271","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" -"272","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" -"273","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" -"274","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" -"275","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" -"276","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" -"277","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" -"278","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" -"279","qbi-hackathon-2023","QBI HACKATHON 2023","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","2023-10-17 23:15:25" +"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/contribution_roles.csv b/apps/openchallenges/challenge-service/src/main/resources/db/contribution_roles.csv index 33db3fa1c8..7e63f19127 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/db/contribution_roles.csv +++ b/apps/openchallenges/challenge-service/src/main/resources/db/contribution_roles.csv @@ -479,19 +479,19 @@ "478","70","91","data_contributor" "479","71","214","data_contributor" "480","72","179","data_contributor" -"481","275","41","data_contributor" -"482","270","213","data_contributor" +"481","274","41","data_contributor" +"482","269","213","data_contributor" "483","73","91","data_contributor" -"484","274","88","data_contributor" -"485","269","220","data_contributor" 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+"932","274","4","challenge_organizer" +"933","269","4","challenge_organizer" "934","73","4","challenge_organizer" -"935","274","4","challenge_organizer" -"936","269","4","challenge_organizer" +"935","273","4","challenge_organizer" +"936","268","4","challenge_organizer" "937","74","4","challenge_organizer" -"938","268","4","challenge_organizer" -"939","278","4","challenge_organizer" -"940","271","4","challenge_organizer" -"941","272","4","challenge_organizer" -"942","273","4","challenge_organizer" -"943","277","4","challenge_organizer" -"944","276","4","challenge_organizer" -"945","265","330","challenge_organizer" -"946","265","179","challenge_organizer" +"938","267","4","challenge_organizer" +"939","277","4","challenge_organizer" +"940","270","4","challenge_organizer" +"941","271","4","challenge_organizer" +"942","272","4","challenge_organizer" +"943","276","4","challenge_organizer" +"944","275","4","challenge_organizer" +"945","264","330","challenge_organizer" +"946","264","179","challenge_organizer" "947","80","9","challenge_organizer" "948","81","9","challenge_organizer" "949","157","9","challenge_organizer" @@ -965,7 +965,7 @@ "964","247","333","data_contributor" "965","250","196","data_contributor" "966","248","209","data_contributor" -"967","259","197","data_contributor" +"967","258","197","data_contributor" "968","232","354","data_contributor" "969","223","196","data_contributor" "970","255","197","data_contributor" @@ -987,23 +987,25 @@ "986","253","347","data_contributor" "987","254","220","data_contributor" "988","256","179","data_contributor" -"989","273","220","data_contributor" -"990","269","220","data_contributor" +"989","272","220","data_contributor" +"990","268","220","data_contributor" "991","74","220","data_contributor" -"992","268","217","data_contributor" -"993","275","349","data_contributor" -"994","270","213","data_contributor" -"995","278","350","data_contributor" -"996","274","256","data_contributor" 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+"1005","262","355","challenge_organizer" +"1006","263","355","challenge_organizer" +"1007","260","126","data_contributor" +"1008","264","330","challenge_organizer" +"1009","279","356","data_contributor" +"1010","279","356","challenge_organizer" diff --git a/apps/openchallenges/challenge-service/src/main/resources/db/incentives.csv b/apps/openchallenges/challenge-service/src/main/resources/db/incentives.csv index 6768ef42b6..07993bc495 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/db/incentives.csv +++ b/apps/openchallenges/challenge-service/src/main/resources/db/incentives.csv @@ -51,227 +51,261 @@ "50","publication","38" "51","speaking_engagement","38" "52","monetary","46" -"53","publication","84" -"54","publication","88" -"55","other","88" -"56","publication","89" -"57","speaking_engagement","89" -"58","publication","90" -"59","speaking_engagement","90" -"60","other","91" -"61","monetary","99" -"62","monetary","101" -"63","monetary","102" 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df5dff08d6..0092f9bb2a 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/db/platforms.csv +++ b/apps/openchallenges/challenge-service/src/main/resources/db/platforms.csv @@ -1,18 +1,15 @@ -"id","slug","name","avatar_url","website_url","createdAt","updatedAt" -"1","synapse","Synapse","logo/synapse.png","https://synapse.org/","2023-08-09 23:01:32","2023-10-14 05:51:49" -"2","drupal","Drupal","logo/drupal.png","https://www.drupal.org/","2023-08-09 23:01:32","2023-09-28 18:15:12" -"3","cami","CAMI","logo/cami.png","https://data.cami-challenge.org/","2023-08-09 23:01:32","2023-08-09 23:02:05" -"4","casp","CASP","logo/casp.png","https://predictioncenter.org/","2023-08-09 23:01:32","2023-09-28 20:37:31" -"5","grand-challenge","Grand Challenge","logo/grand-challenge.png","https://grand-challenge.org/","2023-08-09 23:01:32","2023-08-09 23:02:06" -"6","precision-fda","precisionFDA","logo/precisionfda.png","https://precision.fda.gov/challenges","2023-08-09 23:01:32","2023-08-09 23:02:06" -"7","easychair","EasyChair","logo/easy-chair.jpg","https://easychair.org/","2023-08-09 23:01:32","2023-09-29 2-32-50" -"8","kaggle","Kaggle","logo/kaggle.png","https://www.kaggle.com/","2023-08-09 23:01:32","2023-09-29 2-32-46" -"9","codalab","CodaLab","logo/codalab.jpg","https://codalab.lisn.upsaclay.fr/","2023-08-09 23:01:32","2023-09-28 20:37:18" -"10","codabench","CodaBench","logo/codalab.jpg","https://www.codabench.org/","2023-08-09 23:01:32","2023-08-09 23:02:08" -"11","openml","OpenML","logo/openml.jpg","https://www.openml.org/","2023-08-09 23:01:32","2023-09-28 23:54:43" -"12","papers-with-code","PapersWithCode","logo/papers-with-code.jpg","https://paperswithcode.com/","2023-08-09 23:01:32","2023-09-28 23:55:04" -"13","eterna","Eterna","logo/eterna.svg","https://eternagame.org/","2023-08-09 23:01:32","2023-08-14 16:39:27" -"14","other","Other","","","2023-08-09 23:01:32","2023-08-10 6-25-36" -"15","nightingale-os","Nightingale OS","logo/nightingale-os.jpeg","https://app.nightingalescience.org/","2023-08-22 15:58:49","2023-10-16 20:43:10" -"16","evalai","EvalAI","logo/evalai.png","https://eval.ai/","2023-09-15 16:00:34","2023-09-15 16:05:29" -"17","cache","CACHE","logo/cache.png","https://cache-challenge.org/","2023-10-16 18:43:36","2023-10-16 18:51:47" +"id","slug","name","avatar_url","website_url","created_at","updated_at" +"1","synapse","Synapse","logo/synapse.png","https://synapse.org/","2023-08-09 23:01:32","2023-10-19 21:50:21" +"2","drupal","Drupal","logo/drupal.png","https://www.drupal.org/","2023-08-09 23:01:32","2023-10-19 21:50:23" +"3","cami","CAMI","logo/cami.png","https://data.cami-challenge.org/","2023-08-09 23:01:32","2023-10-19 21:50:25" +"5","grand-challenge","Grand Challenge","logo/grand-challenge.png","https://grand-challenge.org/","2023-08-09 23:01:32","2023-10-19 21:50:26" +"6","precision-fda","precisionFDA","logo/precisionfda.png","https://precision.fda.gov/challenges","2023-08-09 23:01:32","2023-10-19 21:50:28" +"7","easychair","EasyChair","logo/easy-chair.jpg","https://easychair.org/","2023-08-09 23:01:32","2023-10-19 21:50:27" +"8","kaggle","Kaggle","logo/kaggle.png","https://www.kaggle.com/","2023-08-09 23:01:32","2023-10-19 21:50:28" +"9","codalab","CodaLab","logo/codalab.jpg","https://codalab.lisn.upsaclay.fr/","2023-08-09 23:01:32","2023-10-19 21:50:30" +"10","codabench","CodaBench","logo/codalab.jpg","https://www.codabench.org/","2023-08-09 23:01:32","2023-10-19 21:50:30" +"13","eterna","Eterna","logo/eterna.svg","https://eternagame.org/","2023-08-09 23:01:32","2023-10-19 21:50:33" +"14","other","Other","","","2023-08-09 23:01:32","2023-10-19 21:50:34" +"15","nightingale-os","Nightingale OS","logo/nightingale-os.jpeg","https://app.nightingalescience.org/","2023-08-22 15:58:49","2023-10-19 21:50:35" +"16","evalai","EvalAI","logo/evalai.png","https://eval.ai/","2023-09-15 16:00:34","2023-10-19 21:50:36" +"17","cache","CACHE","logo/cache.png","https://cache-challenge.org/","2023-10-16 18:43:36","2023-10-19 21:50:36" diff --git a/apps/openchallenges/challenge-service/src/main/resources/db/submission_types.csv b/apps/openchallenges/challenge-service/src/main/resources/db/submission_types.csv index cb1444bcdd..5b2addd19c 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/db/submission_types.csv +++ b/apps/openchallenges/challenge-service/src/main/resources/db/submission_types.csv @@ -267,17 +267,17 @@ "266","prediction_file","258" "267","prediction_file","259" "268","prediction_file","260" -"269","prediction_file","261" -"270","container_image","261" -"271","prediction_file","262" -"272","container_image","262" -"273","prediction_file","263" -"274","container_image","263" -"275","prediction_file","264" -"276","container_image","264" -"277","prediction_file","265" -"278","container_image","265" -"279","other","266" +"269","container_image","260" 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b/apps/openchallenges/organization-service/src/main/resources/db/contribution_roles.csv @@ -479,19 +479,19 @@ "478","70","91","data_contributor" "479","71","214","data_contributor" "480","72","179","data_contributor" -"481","275","41","data_contributor" -"482","270","213","data_contributor" +"481","274","41","data_contributor" +"482","269","213","data_contributor" "483","73","91","data_contributor" -"484","274","88","data_contributor" -"485","269","220","data_contributor" +"484","273","88","data_contributor" +"485","268","220","data_contributor" "486","74","222","data_contributor" -"487","268","217","data_contributor" -"488","278","221","data_contributor" -"489","271","173","data_contributor" -"490","272","173","data_contributor" -"491","273","220","data_contributor" -"492","277","218","data_contributor" -"493","276","41","data_contributor" +"487","267","217","data_contributor" +"488","277","221","data_contributor" +"489","270","173","data_contributor" 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-"943","277","4","challenge_organizer" -"944","276","4","challenge_organizer" -"945","265","330","challenge_organizer" -"946","265","179","challenge_organizer" +"938","267","4","challenge_organizer" +"939","277","4","challenge_organizer" +"940","270","4","challenge_organizer" +"941","271","4","challenge_organizer" +"942","272","4","challenge_organizer" +"943","276","4","challenge_organizer" +"944","275","4","challenge_organizer" +"945","264","330","challenge_organizer" +"946","264","179","challenge_organizer" "947","80","9","challenge_organizer" "948","81","9","challenge_organizer" "949","157","9","challenge_organizer" @@ -965,7 +965,7 @@ "964","247","333","data_contributor" "965","250","196","data_contributor" "966","248","209","data_contributor" -"967","259","197","data_contributor" +"967","258","197","data_contributor" "968","232","354","data_contributor" "969","223","196","data_contributor" "970","255","197","data_contributor" @@ -987,23 +987,25 @@ "986","253","347","data_contributor" "987","254","220","data_contributor" "988","256","179","data_contributor" -"989","273","220","data_contributor" -"990","269","220","data_contributor" +"989","272","220","data_contributor" +"990","268","220","data_contributor" "991","74","220","data_contributor" -"992","268","217","data_contributor" -"993","275","349","data_contributor" -"994","270","213","data_contributor" -"995","278","350","data_contributor" -"996","274","256","data_contributor" -"997","277","218","data_contributor" -"998","276","351","data_contributor" -"999","271","173","data_contributor" -"1000","272","173","data_contributor" -"1001","258","347","data_contributor" -"1002","260","345","data_contributor" -"1003","261","355","challenge_organizer" -"1004","262","355","challenge_organizer" -"1005","263","355","challenge_organizer" -"1006","264","355","challenge_organizer" -"1007","261","126","data_contributor" -"1008","265","330","challenge_organizer" +"992","267","217","data_contributor" +"993","274","349","data_contributor" +"994","269","213","data_contributor" +"995","277","350","data_contributor" +"996","273","256","data_contributor" +"997","276","218","data_contributor" +"998","275","351","data_contributor" +"999","270","173","data_contributor" +"1000","271","173","data_contributor" +"1001","73","347","data_contributor" +"1002","259","345","data_contributor" +"1003","260","355","challenge_organizer" +"1004","261","355","challenge_organizer" +"1005","262","355","challenge_organizer" +"1006","263","355","challenge_organizer" +"1007","260","126","data_contributor" +"1008","264","330","challenge_organizer" +"1009","279","356","data_contributor" +"1010","279","356","challenge_organizer" diff --git a/apps/openchallenges/organization-service/src/main/resources/db/organizations.csv b/apps/openchallenges/organization-service/src/main/resources/db/organizations.csv index 178745791e..20a956acf2 100644 --- a/apps/openchallenges/organization-service/src/main/resources/db/organizations.csv +++ b/apps/openchallenges/organization-service/src/main/resources/db/organizations.csv @@ -1,4 +1,4 @@ -"id","name","email","login","avatar_url","website_url","description","challenge_count","createdAt","updatedAt","acronym" +"id","name","email","login","avatar_url","website_url","description","challenge_count","created_at","updated_at","acronym" "1","Dialogue on Reverse Engineering Assessment and Methods","dream@sagebionetworks.org","dream","logo/dream.png","https://dreamchallenges.org","Together, we share a vision to enable individuals and groups to collaborate openly so that the “wisdom of the crowd” provides the greatest impact on science and human health.","71","2023-08-04 07:33:09","2023-10-14 05:51:21","DREAM" "3","Critical Assessment of protein Function Annotation","","cafa","logo/cafa.png","https://www.biofunctionprediction.org/cafa/","The Critical Assessment of protein Function Annotation algorithms (CAFA) is an experiment designed to assess the performance of computational methods dedicated to predicting protein function, often using a time challenge. Briefly, CAFA organizers provide a large number of unannotated or incompletely annotated protein sequences. The predictors then predict the function of these proteins by associating them with Gene Ontology terms or Human Phenoytpe Ontology terms. Following the prediction deadline, there is a wait period of several months during which some proteins whose functions were unknown will receive experimental verification. Those proteins constitute the benchmark set, against which the methods are tested. Other data sources include experiments by wet lab collaborators and biocuration dedicated to CAFA.","2","2023-06-23 00:00:00","2023-07-26 20:13:18","CAFA" "4","Critical Assessment of Genome Interpretation","CAGI@genomeinterpretation.org","cagi","logo/CAGI.png","https://genomeinterpretation.org/challenges.html","The Critical Assessment of Genome Interpretation (CAGI) is a community experiment to objectively assess computational methods for predicting the phenotypic impacts of genomic variation. CAGI participants are provided genetic variants and make predictions of resulting phenotypes. These predictions are evaluated against experimental data by independent assessors.","26","2023-06-23 00:00:00","2023-10-16 21:23:31","CAGI" @@ -346,3 +346,4 @@ "353","Seattle Children's Hospital","","seattle-childrens-hospital","","https://www.seattlechildrens.org/","","1","2023-10-06 20:39:31","2023-10-06 20:41:02","" "354","Albrechts-Universität zu Kiel","","albrechts-universität-zu-kiel-","","https://www.uni-kiel.de/en/","","3","2023-10-06 20:40:42","2023-10-14 05:10:24","" "355","CACHE","","cache","logo/cache.png","https://cache-challenge.org/","","4","2023-10-06 21:25:30","2023-10-16 20:56:36","" +"356","NIDDK","","niddk","","https://www.niddk.nih.gov/","","1","2023-10-18 17:07:25","2023-10-18 17:09:40",""