diff --git a/apps/openchallenges/challenge-service/src/main/java/org/sagebionetworks/openchallenges/challenge/service/model/dto/ChallengeDto.java b/apps/openchallenges/challenge-service/src/main/java/org/sagebionetworks/openchallenges/challenge/service/model/dto/ChallengeDto.java
index 6fbd9be280..ab943530e7 100644
--- a/apps/openchallenges/challenge-service/src/main/java/org/sagebionetworks/openchallenges/challenge/service/model/dto/ChallengeDto.java
+++ b/apps/openchallenges/challenge-service/src/main/java/org/sagebionetworks/openchallenges/challenge/service/model/dto/ChallengeDto.java
@@ -480,7 +480,7 @@ public ChallengeDto createdAt(OffsetDateTime createdAt) {
   }
 
   /**
-   * Datetime when metadata was added to the OC database.
+   * Datetime when the object was added to the database.
    *
    * @return createdAt
    */
@@ -489,7 +489,7 @@ public ChallengeDto createdAt(OffsetDateTime createdAt) {
   @Schema(
       name = "createdAt",
       example = "2022-07-04T22:19:11Z",
-      description = "Datetime when metadata was added to the OC database.",
+      description = "Datetime when the object was added to the database.",
       required = true)
   public OffsetDateTime getCreatedAt() {
     return createdAt;
@@ -505,7 +505,7 @@ public ChallengeDto updatedAt(OffsetDateTime updatedAt) {
   }
 
   /**
-   * Datetime when metadata was last modified in the OC database.
+   * Datetime when the object was last modified in the database.
    *
    * @return updatedAt
    */
@@ -514,7 +514,7 @@ public ChallengeDto updatedAt(OffsetDateTime updatedAt) {
   @Schema(
       name = "updatedAt",
       example = "2022-07-04T22:19:11Z",
-      description = "Datetime when metadata was last modified in the OC database.",
+      description = "Datetime when the object was last modified in the database.",
       required = true)
   public OffsetDateTime getUpdatedAt() {
     return updatedAt;
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 865a19984e..d2d67b75e3 100644
--- a/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv
+++ b/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv
@@ -57,29 +57,29 @@
 "56","nih-long-covid-computational-challenge","NIH Long COVID Computational Challenge","Understanding prevalence and outcomes of post-COVID syndrome","The overall prevalence of post-acute sequelae of SARS-CoV-2 (PASC) is currently unknown, but there is growing evidence that more than half of COVID-19 survivors experience at least one symptom of PASC/Long COVID at six months after recovery of the acute illness. Reports also reflect an underlying heterogeneity of symptoms, multi-organ involvement, and persistence of PASC/Long COVID in some patients. Research is ongoing to understand prevalence, duration, and clinical outcomes of PASC/Long COVID. Symptoms of fatigue, cognitive impairment, shortness of breath, and cardiac damage, among others, have been observed in patients who had only mild initial disease. The breadth and complexity of data created in today's health care encounters require advanced analytics to extract meaning from longitudinal data on symptoms, laboratory results, images, functional tests, genomics, mobile health/wearable devices, written notes, electronic health records (EHR), and other relevant data types. Adva...","","https://www.synapse.org/#!Synapse:syn33576900/wiki/618451","completed","1","","2022-08-25","2022-12-15","2023-06-23 00:00:00","2023-10-18 00:39:03"
 "57","bridge2ai","Bridge2AI","What makes a good color palette?","What makes a good color palette?","","","upcoming","1","","\N","\N","2023-06-23 00:00:00","2023-11-20 20:19:26"
 "58","rare-x-open-data-science","RARE-X Open Data Science","Unlocking rare disease mysteries through open science collaboration","The Xcelerate RARE-A Rare Disease Open Science Data Challenge is bringing together researchers and data scientists in a collaborative and competitive environment to make the best use of patient-provided data to solve big unknowns in healthcare. The Challenge will launch to researchers in late May 2023, focused on rare pediatric neurodevelopmental diseases.","","https://www.synapse.org/#!Synapse:syn51198355/wiki/621435","completed","1","","2023-05-17","2023-08-16","2023-06-23 00:00:00","2023-10-14 05:38:59"
-"59","cagi5-regulation-saturation","CAGI5: Regulation saturation","Predicting effects of variants in disease-linked enhancers and promoters","17,500 single nucleotide variants (SNVs) in 5 human disease associated enhancers (including IRF4, IRF6, MYC, SORT1) and 9 promoters (including TERT, LDLR, F9, HBG1) were assessed in a saturation mutagenesis massively parallel reporter assay. Promoters were cloned into a plasmid upstream of a tagged reporter construct, and reporter expression was measured relative to the plasmid DNA to determine the impact of promoter variants. Enhancers were placed upstream of a minimal promoter and assayed similarly. The challenge is to predict the functional effects of these variants in the regulatory regions as measured from the reporter expression.","","https://genomeinterpretation.org/cagi5-regulation-saturation.html","completed","14","","2018-01-04","2018-05-03","2023-06-23 00:00:00","2023-11-01 23:42:37"
-"60","cagi5-calm1","CAGI5: CALM1","Predicting effects of calmodulin variants on yeast growth","Calmodulin is a calcium-sensing protein that modulates the activity of a large number of proteins in the cell. It is involved in many cellular processes, and is especially important for neuron and muscle cell function. Variants that affect calmodulin function have been found to be causally associated with cardiac arrhythmias. A large library of calmodulin missense variants was assessed with respect to their effects on protein function using a high-throughput yeast complementation assay. The challenge is to predict the functional effects of these calmodulin variants on competitive growth in a high-throughput yeast complementation assay.","","https://genomeinterpretation.org/cagi5-calm1.html","completed","14","","2017-10-21","2017-12-20","2023-06-23 00:00:00","2023-10-18 15:35:49"
-"61","cagi5-pcm1","CAGI5: PCM1","Assessing PCM1 variants' impact on zebrafish ventricle","The PCM1 (Pericentriolar Material 1) gene is a component of centriolar satellites occurring around centrosomes in vertebrate cells. Several studies have implicated PCM1 variants as a risk factor for schizophrenia. Ventricular enlargement is one of the most consistent abnormal structural brain findings in schizophrenia Therefore 38 transgenic human PCM1 missense mutations implicated in schizophrenia were assayed in a zebrafish model to determine their impact on the posterior ventricle area. The challenge is to predict whether variants implicated in schizophrenia impact zebrafish ventricular area.","","https://genomeinterpretation.org/cagi5-pcm1.html","completed","14","","2017-11-09","2018-04-19","2023-06-23 00:00:00","2023-10-18 15:35:49"
-"62","cagi5-frataxin","CAGI5: Frataxin","Predicting ΔΔGH20 for Frataxin Variants","Fraxatin is a highly-conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Humans with a frataxin deficiency have the cardio-and neurodegenerative disorder Friedreich's ataxia. A library of eight missense variants was assessed by near and far-UV circular dichroism and intrinsic fluorescence spectra to determine thermodynamic stability at different concentration of denaturant. These were used to calculate a ΔΔGH20 value, the difference in unfolding free energy (ΔGH20) between the mutant and wild-type proteins for each variant. The challenge is to predict ΔΔGH20 for each frataxin variant.","","https://genomeinterpretation.org/cagi5-frataxin.html","completed","14","","2017-11-30","2018-04-18","2023-06-23 00:00:00","2023-10-18 15:35:50"
-"63","cagi5-tpmt","CAGI5: TPMT and p10","Predicting TPMT and PTEN protein stability variants","The gene p10 encodes for PTEN (Phosphatase and TEnsin Homolog), an important secondary messenger molecule promoting cell growth and survival through signaling cascades including those controlled by AKT and mTOR. Thiopurine S-methyl transferase (TPMT) is a key enzyme involved in the metabolism of thiopurine drugs and functions by catalyzing the S-methylation of aromatic and heterocyclic sulfhydryl groups. A library of thousands of PTEN and TPMT mutations was assessed to measure the stability of the variant protein using a multiplexed variant stability profiling (VSP) assay, which detects the presence of EGFP fused to the mutated PTEN and TPMT protein respectively. The stability of the variant protein dictates the abundance of the fusion protein and thus the EGFP level of the cell. The challenge is to predict the effect of each variant on TPMT and/or PTEN protein stability.","","https://genomeinterpretation.org/cagi5-tpmt.html","completed","14","","2017-11-30","2017-12-01","2023-06-23 00:00:00","2023-10-14 05:39:03"
-"64","cagi5-annotate-all-missense","CAGI5: Annotate all nonsynonymous variants","Annotate all nonsynonymous variants","dbNSFP describes 810,848,49 possible protein-altering variants in the human genome. The challenge is to predict the functional effect of every such variant. For the vast majority of these missense variants, the functional impact is not currently known, but experimental and clinical evidence are accruing rapidly. Rather than drawing upon a single discrete dataset as typical with CAGI, predictions will be assessed by comparing with experimental or clinical annotations made available after the prediction submission date, on an ongoing basis. if predictors assent, predictions will also incorporated into dbNSFP.","","https://genomeinterpretation.org/cagi5-annotate-all-missense.html","completed","14","","2017-11-30","2018-05-09","2023-06-23 00:00:00","2023-10-14 05:39:04"
-"65","cagi5-gaa","CAGI5: GAA","Predict enzyme activity of GAA mutants in Pompe disease","Acid alpha-glucosidase (GAA) is a lysosomal alpha-glucosidase. Some mutations in GAA cause a rare disorder, Pompe disease, (Glycogen Storage Disease II). Rare GAA missense variants found in a human population sample have been assayed for enzymatic activity in transfected cell lysates. The assessment of this challenge will include evaluations that recognize novelty of approach. The challenge is to predict the fractional enzyme activity of each mutant protein compared to the wild-type enzyme.","","https://genomeinterpretation.org/cagi5-gaa.html","completed","14","","2017-11-09","2018-04-25","2023-06-23 00:00:00","2023-10-14 05:39:04"
-"66","cagi5-chek2","CAGI5: CHEK2","Estimate CHEK2 gene variant probabilities in Latino breast cancer cases","Variants in the CHEK2 gene are associated with breast cancer. This challenge includes CHEK2 gene variants from approximately 1200 Latino breast cancer cases and 1200 ethnically matched controls. This challenge is to estimate the probability of each gene variant occurring in an individual from the cancer affected cohort.","","https://genomeinterpretation.org/cagi5-chek2.html","completed","14","","2017-12-20","2018-04-24","2023-06-23 00:00:00","2023-10-14 05:39:07"
-"67","cagi5-enigma","CAGI5: ENIGMA","Predict cancer risk from BRCA1/2 gene variants","Breast cancer is the most prevalent cancer among women worldwide. The association between germline mutations in the BRCA1 and BRCA2 genes and the development of cancer has been well established. The most common high-risk mutations associated with breast cancer are those in the autosomal dominant breast cancer genes 1 and 2 (BRCA1 and BRCA2). Mutations in these genes are found in 1-3% of breast cancer cases. The challenge is to predict which variants are associated with increased risk for breast cancer.","","https://genomeinterpretation.org/cagi5-enigma.html","completed","14","","2017-12-20","2018-05-01","2023-06-23 00:00:00","2023-10-14 05:39:08"
-"68","cagi5-mapsy","CAGI5: MaPSy","Predict the impact of genetic variants on splicing mechanisms","The Massively Parallel Splicing Assay (MaPSy) approach was used to screen 797 reported exonic disease mutations using a mini-gene system, assaying both in vivo via transfection in tissue culture, and in vitro via incubation in cell nuclear extract. The challenge is to predict the degree to which a given variant causes changes in splicing.","","https://genomeinterpretation.org/cagi5-mapsy.html","completed","14","","2017-11-29","2018-05-07","2023-06-23 00:00:00","2023-10-14 05:39:08"
-"69","cagi5-vex-seq","CAGI5: Vex-seq","Predict splicing changes from variants in globin gene","A barcoding approach called Variant exon sequencing (Vex-seq) was applied to assess effect of 2,059 natural single nucleotide variants and short indels on splicing of a globin mini-gene construct transfected into HepG2 cells. This is reported as ΔΨ (delta PSI, or Percent Spliced In), between the variant Ψand the reference Ψ. The challenge is to predict ΔΨ for each variant.","","https://genomeinterpretation.org/cagi5-vex-seq.html","completed","14","","2017-12-14","2018-05-02","2023-06-23 00:00:00","2023-10-16 17:51:58"
-"70","cagi5-sickkids5","CAGI5: SickKids clinical genomes","Predict genetic disorders from 30 child genomes and phenotypes","This challenge involves 30 children with suspected genetic disorders who were referred for clinical genome sequencing. Predictors are given the 30 genome sequences, and are also provided with the phenotypic descriptions as shared with the diagnostic laboratory. The challenge is to predict what class of disease is associated with each genome, and which genome corresponds to which clinical description. Predictors may additionally identify the diagnostic variant(s) underlying the predictions, and identify predictive secondary variants conferring high risk of other diseases whose phenotypes are not reported in the clinical descriptions.","","https://genomeinterpretation.org/cagi5-sickkids5.html","completed","14","","2017-12-22","2018-04-26","2023-06-23 00:00:00","2023-10-14 05:39:10"
-"71","cagi5-intellectual-disability","CAGI5: ID Panel","Predict phenotypes and variants from gene panel sequences","The challenge presented here is to use computational methods to predict a patient's clinical phenotype and the causal variant(s) based on analysis of their gene panel sequence data. Sequence data for 74 genes associated with intellectual disability (ID) and/or Autism spectrum disorders (ASD) from a cohort of 150 patients with a range of neurodevelopmental presentations (ID, autism, epilepsy, etc..) have been made available for this challenge. For each patient, predictors must report the causative variants and which of seven phenotypes are present.","","https://genomeinterpretation.org/cagi5-intellectual-disability.html","completed","14","","2017-12-22","2018-04-30","2023-06-23 00:00:00","2023-10-18 15:28:06"
-"72","cagi5-clotting-disease","CAGI5: Clotting disease exomes","Predict venous thromboembolism risk in African Americans","African Americans have a higher incidence of developing venous thromboembolisms (VTE), which includes deep vein thrombosis (DVT) and pulmonary embolism (PE), than people of European ancestry. Participants are provided with exome data and clinical covariates for a cohort of African Americans who have been prescribed Warfarin either because they had experienced a VTE event or had been diagnosed with atrial fibrillation (which predisposes to clotting). The challenge is to distinguish between these conditions. At present, in contrast to European ancestry, there are no genetic methods for anticipating which African Americans are most at risk of a venous thromboembolism, and the results of this challenge may contribute to the development of such tools.","","https://genomeinterpretation.org/cagi5-clotting-disease.html","completed","14","","2017-11-23","2018-04-28","2023-06-23 00:00:00","2023-10-18 15:30:55"
+"59","cagi5-regulation-saturation","CAGI5: Regulation saturation","Predicting effects of variants in disease-linked enhancers and promoters","17,500 single nucleotide variants (SNVs) in 5 human disease associated enhancers (including IRF4, IRF6, MYC, SORT1) and 9 promoters (including TERT, LDLR, F9, HBG1) were assessed in a saturation mutagenesis massively parallel reporter assay. Promoters were cloned into a plasmid upstream of a tagged reporter construct, and reporter expression was measured relative to the plasmid DNA to determine the impact of promoter variants. Enhancers were placed upstream of a minimal promoter and assayed similarly. The challenge is to predict the functional effects of these variants in the regulatory regions as measured from the reporter expression.","","https://genomeinterpretation.org/cagi5-regulation-saturation.html","completed","\N","","2018-01-04","2018-05-03","2023-06-23 00:00:00","2023-12-06 01:09:41"
+"60","cagi5-calm1","CAGI5: CALM1","Predicting effects of calmodulin variants on yeast growth","Calmodulin is a calcium-sensing protein that modulates the activity of a large number of proteins in the cell. It is involved in many cellular processes, and is especially important for neuron and muscle cell function. Variants that affect calmodulin function have been found to be causally associated with cardiac arrhythmias. A large library of calmodulin missense variants was assessed with respect to their effects on protein function using a high-throughput yeast complementation assay. The challenge is to predict the functional effects of these calmodulin variants on competitive growth in a high-throughput yeast complementation assay.","","https://genomeinterpretation.org/cagi5-calm1.html","completed","\N","","2017-10-21","2017-12-20","2023-06-23 00:00:00","2023-10-18 15:35:49"
+"61","cagi5-pcm1","CAGI5: PCM1","Assessing PCM1 variants' impact on zebrafish ventricle","The PCM1 (Pericentriolar Material 1) gene is a component of centriolar satellites occurring around centrosomes in vertebrate cells. Several studies have implicated PCM1 variants as a risk factor for schizophrenia. Ventricular enlargement is one of the most consistent abnormal structural brain findings in schizophrenia Therefore 38 transgenic human PCM1 missense mutations implicated in schizophrenia were assayed in a zebrafish model to determine their impact on the posterior ventricle area. The challenge is to predict whether variants implicated in schizophrenia impact zebrafish ventricular area.","","https://genomeinterpretation.org/cagi5-pcm1.html","completed","\N","","2017-11-09","2018-04-19","2023-06-23 00:00:00","2023-10-18 15:35:49"
+"62","cagi5-frataxin","CAGI5: Frataxin","Predicting ΔΔGH20 for Frataxin Variants","Fraxatin is a highly-conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Humans with a frataxin deficiency have the cardio-and neurodegenerative disorder Friedreich's ataxia. A library of eight missense variants was assessed by near and far-UV circular dichroism and intrinsic fluorescence spectra to determine thermodynamic stability at different concentration of denaturant. These were used to calculate a ΔΔGH20 value, the difference in unfolding free energy (ΔGH20) between the mutant and wild-type proteins for each variant. The challenge is to predict ΔΔGH20 for each frataxin variant.","","https://genomeinterpretation.org/cagi5-frataxin.html","completed","\N","","2017-11-30","2018-04-18","2023-06-23 00:00:00","2023-10-18 15:35:50"
+"63","cagi5-tpmt","CAGI5: TPMT and p10","Predicting TPMT and PTEN protein stability variants","The gene p10 encodes for PTEN (Phosphatase and TEnsin Homolog), an important secondary messenger molecule promoting cell growth and survival through signaling cascades including those controlled by AKT and mTOR. Thiopurine S-methyl transferase (TPMT) is a key enzyme involved in the metabolism of thiopurine drugs and functions by catalyzing the S-methylation of aromatic and heterocyclic sulfhydryl groups. A library of thousands of PTEN and TPMT mutations was assessed to measure the stability of the variant protein using a multiplexed variant stability profiling (VSP) assay, which detects the presence of EGFP fused to the mutated PTEN and TPMT protein respectively. The stability of the variant protein dictates the abundance of the fusion protein and thus the EGFP level of the cell. The challenge is to predict the effect of each variant on TPMT and/or PTEN protein stability.","","https://genomeinterpretation.org/cagi5-tpmt.html","completed","\N","","2017-11-30","2017-12-01","2023-06-23 00:00:00","2023-10-14 05:39:03"
+"64","cagi5-annotate-all-missense","CAGI5: Annotate all nonsynonymous variants","Annotate all nonsynonymous variants","dbNSFP describes 810,848,49 possible protein-altering variants in the human genome. The challenge is to predict the functional effect of every such variant. For the vast majority of these missense variants, the functional impact is not currently known, but experimental and clinical evidence are accruing rapidly. Rather than drawing upon a single discrete dataset as typical with CAGI, predictions will be assessed by comparing with experimental or clinical annotations made available after the prediction submission date, on an ongoing basis. if predictors assent, predictions will also incorporated into dbNSFP.","","https://genomeinterpretation.org/cagi5-annotate-all-missense.html","completed","\N","","2017-11-30","2018-05-09","2023-06-23 00:00:00","2023-10-14 05:39:04"
+"65","cagi5-gaa","CAGI5: GAA","Predict enzyme activity of GAA mutants in Pompe disease","Acid alpha-glucosidase (GAA) is a lysosomal alpha-glucosidase. Some mutations in GAA cause a rare disorder, Pompe disease, (Glycogen Storage Disease II). Rare GAA missense variants found in a human population sample have been assayed for enzymatic activity in transfected cell lysates. The assessment of this challenge will include evaluations that recognize novelty of approach. The challenge is to predict the fractional enzyme activity of each mutant protein compared to the wild-type enzyme.","","https://genomeinterpretation.org/cagi5-gaa.html","completed","\N","","2017-11-09","2018-04-25","2023-06-23 00:00:00","2023-10-14 05:39:04"
+"66","cagi5-chek2","CAGI5: CHEK2","Estimate CHEK2 gene variant probabilities in Latino breast cancer cases","Variants in the CHEK2 gene are associated with breast cancer. This challenge includes CHEK2 gene variants from approximately 1200 Latino breast cancer cases and 1200 ethnically matched controls. This challenge is to estimate the probability of each gene variant occurring in an individual from the cancer affected cohort.","","https://genomeinterpretation.org/cagi5-chek2.html","completed","\N","","2017-12-20","2018-04-24","2023-06-23 00:00:00","2023-10-14 05:39:07"
+"67","cagi5-enigma","CAGI5: ENIGMA","Predict cancer risk from BRCA1/2 gene variants","Breast cancer is the most prevalent cancer among women worldwide. The association between germline mutations in the BRCA1 and BRCA2 genes and the development of cancer has been well established. The most common high-risk mutations associated with breast cancer are those in the autosomal dominant breast cancer genes 1 and 2 (BRCA1 and BRCA2). Mutations in these genes are found in 1-3% of breast cancer cases. The challenge is to predict which variants are associated with increased risk for breast cancer.","","https://genomeinterpretation.org/cagi5-enigma.html","completed","\N","","2017-12-20","2018-05-01","2023-06-23 00:00:00","2023-10-14 05:39:08"
+"68","cagi5-mapsy","CAGI5: MaPSy","Predict the impact of genetic variants on splicing mechanisms","The Massively Parallel Splicing Assay (MaPSy) approach was used to screen 797 reported exonic disease mutations using a mini-gene system, assaying both in vivo via transfection in tissue culture, and in vitro via incubation in cell nuclear extract. The challenge is to predict the degree to which a given variant causes changes in splicing.","","https://genomeinterpretation.org/cagi5-mapsy.html","completed","\N","","2017-11-29","2018-05-07","2023-06-23 00:00:00","2023-10-14 05:39:08"
+"69","cagi5-vex-seq","CAGI5: Vex-seq","Predict splicing changes from variants in globin gene","A barcoding approach called Variant exon sequencing (Vex-seq) was applied to assess effect of 2,059 natural single nucleotide variants and short indels on splicing of a globin mini-gene construct transfected into HepG2 cells. This is reported as ΔΨ (delta PSI, or Percent Spliced In), between the variant Ψand the reference Ψ. The challenge is to predict ΔΨ for each variant.","","https://genomeinterpretation.org/cagi5-vex-seq.html","completed","\N","","2017-12-14","2018-05-02","2023-06-23 00:00:00","2023-10-16 17:51:58"
+"70","cagi5-sickkids5","CAGI5: SickKids clinical genomes","Predict genetic disorders from 30 child genomes and phenotypes","This challenge involves 30 children with suspected genetic disorders who were referred for clinical genome sequencing. Predictors are given the 30 genome sequences, and are also provided with the phenotypic descriptions as shared with the diagnostic laboratory. The challenge is to predict what class of disease is associated with each genome, and which genome corresponds to which clinical description. Predictors may additionally identify the diagnostic variant(s) underlying the predictions, and identify predictive secondary variants conferring high risk of other diseases whose phenotypes are not reported in the clinical descriptions.","","https://genomeinterpretation.org/cagi5-sickkids5.html","completed","\N","","2017-12-22","2018-04-26","2023-06-23 00:00:00","2023-10-14 05:39:10"
+"71","cagi5-intellectual-disability","CAGI5: ID Panel","Predict phenotypes and variants from gene panel sequences","The challenge presented here is to use computational methods to predict a patient's clinical phenotype and the causal variant(s) based on analysis of their gene panel sequence data. Sequence data for 74 genes associated with intellectual disability (ID) and/or Autism spectrum disorders (ASD) from a cohort of 150 patients with a range of neurodevelopmental presentations (ID, autism, epilepsy, etc..) have been made available for this challenge. For each patient, predictors must report the causative variants and which of seven phenotypes are present.","","https://genomeinterpretation.org/cagi5-intellectual-disability.html","completed","\N","","2017-12-22","2018-04-30","2023-06-23 00:00:00","2023-10-18 15:28:06"
+"72","cagi5-clotting-disease","CAGI5: Clotting disease exomes","Predict venous thromboembolism risk in African Americans","African Americans have a higher incidence of developing venous thromboembolisms (VTE), which includes deep vein thrombosis (DVT) and pulmonary embolism (PE), than people of European ancestry. Participants are provided with exome data and clinical covariates for a cohort of African Americans who have been prescribed Warfarin either because they had experienced a VTE event or had been diagnosed with atrial fibrillation (which predisposes to clotting). The challenge is to distinguish between these conditions. At present, in contrast to European ancestry, there are no genetic methods for anticipating which African Americans are most at risk of a venous thromboembolism, and the results of this challenge may contribute to the development of such tools.","","https://genomeinterpretation.org/cagi5-clotting-disease.html","completed","\N","","2017-11-23","2018-04-28","2023-06-23 00:00:00","2023-10-18 15:30:55"
 "73","cagi6-sickkids","CAGI6: SickKids clinical genomes and transcriptomes","Identify genes causing rare diseases using transcriptomics","This challenge involves data from 79 children who were referred to The Hospital for Sick Children's (SickKids) Genome Clinic for genome sequencing because of suspected but undiagnosed genetic disorders. Research subjects are consented for sharing of their sequence data and phenotype information with researchers working to understand the molecular causes of rare disease. When a candidate disease variant believed to be related to the phenotype is identified, the variant is adjudicated and confirmed in a clinical setting. In this challenge, transcriptomic and phenotype data from a subset of the “solved” (diagnosed) and “unsolved” SickKids patients will be provided, along with corresponding genomic sequence data. The challenge is to use a transcriptome-driven approach to identify the gene(s) and molecular mechanisms underlying the phenotypic descriptions in each case. For the unsolved cases, prioritized variants from the participating teams will be examined to see if additional diagno...","","https://genomeinterpretation.org/cagi6-sickkids.html","completed","1","","2021-08-04","2021-12-31","2023-06-23 00:00:00","2023-11-02 18:02:23"
 "74","cagi6-cam","CAGI6: CaM","Predict the impact of point mutations on calmodulin stability","Calmodulin (CaM) is a ubiquitous calcium (Ca2+) sensor protein interacting with more than 200 molecular partners, thereby regulating a variety of biological processes. Missense point mutations in the genes encoding CaM have been associated with ventricular tachycardia and sudden cardiac death. A library encompassing up to 17 point mutations was assessed by far-UV circular dichroism (CD) by measuring melting temperature (Tm) and percentage of unfolding (%unfold) upon thermal denaturation at pH and salt concentration that mimic the physiological conditions. The challenge is to predict: the Tm and %unfold values for isolated CaM variants under Ca2+-saturating conditions (Ca2+-CaM) and in the Ca2+-free (apo) state;  whether the point mutation stabilizes or destabilizes the protein (based on Tm and %unfold).","","https://genomeinterpretation.org/cagi6-cam.html","completed","1","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-18 15:32:37"
 "75","cami-ii","CAMI II","Assemble and classify microbial genomes in complex samples","CAMI II offers several challenges-an assembly, a genome binning, a taxonomic binning and a taxonomic profiling challenge, on several multi-sample data sets from different environments, including long and short read data. This includes a marine data set and a high-strain diversity data set, with a third data set to follow later. A pathogen detection challenge on a clinical sample is also provided.","","https://www.microbiome-cosi.org/cami/cami/cami2","completed","3","","2019-01-14","2021-01-31","2023-06-23 00:00:00","2023-10-17 23:15:00"
-"76","camda18-metasub-forensics","CAMDA18-MetaSUB Forensics","Build a metagenomic map of mass-transit systems globally","The MetaSUB International Consortium is building a longitudinal metagenomic map of mass-transit systems and other public spaces across the globe. The consortium maintains a strategic partnership with CAMDA and this year provides data from global City Sampling Days for the first-ever multi-city forensic analyses.","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#metasub_forensics_challenge","completed","14","","\N","\N","2023-06-23 00:00:00","2023-11-01 20:37:34"
-"77","camda18-cmap-drug-safety","CAMDA18-CMap Drug Safety","Predict drug toxicity using cell-based gene expression data","Attrition in drug discovery and development due to safety / toxicity issues remains a significant concern, and there are strong efforts to identify and mitigate risk as early as possible. Drug-induced liver injury (DILI) is one of the primary problems in drug development and regulatory clearance due to the poor performance of existing preclinical models. There is a pressing need to evaluate alternative methods for predicting DILI, with great hopes being placed in modern approaches from statistics and machine learning applied to genome scale profiling data. A critical question thus is if we can better integrate, understand, and exploit information from cell-based screens like the Broad Institute Connectivity Map (CMap, Science 313, Nature Reviews Cancer 7).  This CAMDA challenge focuses on understanding or predicting drug induced liver injury in humans from cell-based screens, specifically the CMap gene expression responses of two different cancer cell lines (MCF7 and PC3) to 276 d...","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#cmap_drug_safety_challenge","completed","14","","\N","\N","2023-06-23 00:00:00","2023-11-01 20:37:35"
-"78","camda18-cancer-data-integration","CAMDA18-Cancer Data Integration","Unify data integration approaches for breast cancer and neuroblastoma","Examine the power of data integration in a real-world clinical settings. Many approaches work well on some data-sets yet not on others. We here challenge you to demonstrate a unified single approach to data-integration that matches or outperforms the current state of the art on two different diseases, breast cancer and neuroblastoma. Breast cancer affects about 3 million women every year (McGuire et al, Cancers 7), and this number is growing fast, especially in developed countries. Can you improve on the large Metabric study (Curtis et al., Nature 486, and Dream Challenge, Margolin et al, Sci Transl Med 5)? The cohort is biologically heterogeneous with all five distinct PAM50 breast cancer subtypes represented. Matched profiles for microarray and copy number data as well as clinical information (survival times, multiple prognostic markers, therapy data) are available for about 2,000 patients. Neuroblastoma is the most common extracranial solid tumor in children. The base study com...","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#cancer_data_integration_challenge","completed","14","","\N","\N","2023-06-23 00:00:00","2023-11-01 20:37:36"
+"76","camda18-metasub-forensics","CAMDA18-MetaSUB Forensics","Build a metagenomic map of mass-transit systems globally","The MetaSUB International Consortium is building a longitudinal metagenomic map of mass-transit systems and other public spaces across the globe. The consortium maintains a strategic partnership with CAMDA and this year provides data from global City Sampling Days for the first-ever multi-city forensic analyses.","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#metasub_forensics_challenge","completed","\N","","\N","\N","2023-06-23 00:00:00","2023-11-01 20:37:34"
+"77","camda18-cmap-drug-safety","CAMDA18-CMap Drug Safety","Predict drug toxicity using cell-based gene expression data","Attrition in drug discovery and development due to safety / toxicity issues remains a significant concern, and there are strong efforts to identify and mitigate risk as early as possible. Drug-induced liver injury (DILI) is one of the primary problems in drug development and regulatory clearance due to the poor performance of existing preclinical models. There is a pressing need to evaluate alternative methods for predicting DILI, with great hopes being placed in modern approaches from statistics and machine learning applied to genome scale profiling data. A critical question thus is if we can better integrate, understand, and exploit information from cell-based screens like the Broad Institute Connectivity Map (CMap, Science 313, Nature Reviews Cancer 7).  This CAMDA challenge focuses on understanding or predicting drug induced liver injury in humans from cell-based screens, specifically the CMap gene expression responses of two different cancer cell lines (MCF7 and PC3) to 276 d...","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#cmap_drug_safety_challenge","completed","\N","","\N","\N","2023-06-23 00:00:00","2023-11-01 20:37:35"
+"78","camda18-cancer-data-integration","CAMDA18-Cancer Data Integration","Unify data integration approaches for breast cancer and neuroblastoma","Examine the power of data integration in a real-world clinical settings. Many approaches work well on some data-sets yet not on others. We here challenge you to demonstrate a unified single approach to data-integration that matches or outperforms the current state of the art on two different diseases, breast cancer and neuroblastoma. Breast cancer affects about 3 million women every year (McGuire et al, Cancers 7), and this number is growing fast, especially in developed countries. Can you improve on the large Metabric study (Curtis et al., Nature 486, and Dream Challenge, Margolin et al, Sci Transl Med 5)? The cohort is biologically heterogeneous with all five distinct PAM50 breast cancer subtypes represented. Matched profiles for microarray and copy number data as well as clinical information (survival times, multiple prognostic markers, therapy data) are available for about 2,000 patients. Neuroblastoma is the most common extracranial solid tumor in children. The base study com...","","http://camda2018.bioinf.jku.at/doku.php/contest_dataset#cancer_data_integration_challenge","completed","\N","","\N","\N","2023-06-23 00:00:00","2023-11-01 20:37:36"
 "79","cafa-4","CAFA 4","Assess algorithms for predicting protein function","The goal of the Critical Assessment of Functional Annotation(CAFA) challenge is to evaluate automated protein function prediction algorithms in the task of predicting Gene Ontology and Human Phenotype Ontology terms for a given set of protein sequences. For the GO-based predictions, the evaluation will be carried out for the Molecular Function Ontology, Biological Process Ontology and Cellular Component Ontology. Participants develop protein function prediction algorithms using training protein sequence data and submit their predictions on target protein sequence data.","","https://www.biofunctionprediction.org/cafa/","completed","1","","2019-10-21","2020-02-12","2023-06-23 00:00:00","2023-10-14 05:39:20"
-"80","casp13","CASP13","CASP assesses protein structure prediction methods","CASP (Critical Assessment of Structure Prediction) is a community wide experiment to determine and advance the state of the art in modeling protein structure from amino acid sequence. Every two years, participants are invited to submit models for a set of proteins for which the experimental structures are not yet public. Independent assessors then compare the models with experiment. Assessments and results are published in a special issue of the journal PROTEINS. In the most recent CASP round, CASP12, nearly 100 groups from around the world submitted more than 50,000 models on 82 modeling targets","","https://predictioncenter.org/casp13/index.cgi","completed","14","","2018-04-18","2018-08-20","2023-06-23 00:00:00","2023-10-17 22:52:29"
-"81","casp14","CASP14","Assess progress in protein structure prediction","CASP (Critical Assessment of Structure Prediction) is a community wide experiment to determine and advance the state of the art in modeling protein structure from amino acid sequence. Every two years, participants are invited to submit models for a set of proteins for which the experimental structures are not yet public. Independent assessors then compare the models with experiment. Assessments and results are published in a special issue of the journal PROTEINS. In the most recent CASP round, CASP14, nearly 100 groups from around the world submitted more than 67,000 models on 90 modeling targets.","","https://predictioncenter.org/casp14/index.cgi","completed","14","","2020-05-04","2020-09-07","2023-06-23 00:00:00","2023-10-17 22:47:26"
+"80","casp13","CASP13","CASP assesses protein structure prediction methods","CASP (Critical Assessment of Structure Prediction) is a community wide experiment to determine and advance the state of the art in modeling protein structure from amino acid sequence. Every two years, participants are invited to submit models for a set of proteins for which the experimental structures are not yet public. Independent assessors then compare the models with experiment. Assessments and results are published in a special issue of the journal PROTEINS. In the most recent CASP round, CASP12, nearly 100 groups from around the world submitted more than 50,000 models on 82 modeling targets","","https://predictioncenter.org/casp13/index.cgi","completed","\N","","2018-04-18","2018-08-20","2023-06-23 00:00:00","2023-10-17 22:52:29"
+"81","casp14","CASP14","Assess progress in protein structure prediction","CASP (Critical Assessment of Structure Prediction) is a community wide experiment to determine and advance the state of the art in modeling protein structure from amino acid sequence. Every two years, participants are invited to submit models for a set of proteins for which the experimental structures are not yet public. Independent assessors then compare the models with experiment. Assessments and results are published in a special issue of the journal PROTEINS. In the most recent CASP round, CASP14, nearly 100 groups from around the world submitted more than 67,000 models on 90 modeling targets.","","https://predictioncenter.org/casp14/index.cgi","completed","\N","","2020-05-04","2020-09-07","2023-06-23 00:00:00","2023-10-17 22:47:26"
 "82","cfsan-pathogen-detection","CFSAN Pathogen Detection","Rapidly identify food sources of outbreaks","In the U.S. alone, one in six individuals, an estimated 48 million people, fall prey to foodborne illness, resulting in 128,000 hospitalizations and 3,000 deaths per year. Economic burdens are estimated cumulatively at $152 billion dollars annually, including $39 billion due to contamination of fresh and processed produce. One longstanding problem is the ability to rapidly identify the food-source associated with the outbreak being investigated. The faster an outbreak is identified and the increased certainty that a given source (e.g., papayas from Mexico) and patients are linked, the faster the outbreak can be stopped, limiting morbidity and mortality.  In the last few years, the application of next-generation sequencing (NGS) technology for whole genome sequencing (WGS) of foodborne pathogens has revolutionized food pathogen outbreak surveillance. WGS of foodborne pathogens enables high-resolution identification of pathogens isolated from food or environmental samples. These pat...","","https://precision.fda.gov/challenges/2","completed","6","","2018-02-15","2018-04-26","2023-06-23 00:00:00","2023-10-14 05:39:23"
 "83","cdrh-biothreat","CDRH Biothreat","Identify infectious diseases from clinical samples using sequencing technology","Many infectious diseases have similar signs and symptoms, making it challenging for healthcare providers to identify the disease-causing agent. Clinical samples are often tested by multiple test methods to help reveal the microbe that is causing the infectious disease. The results of these test methods can help healthcare professionals determine the best treatment for patients. Today, High-Throughput Sequencing (HTS) or Next Generation Sequencing (NGS) technology has the capability, as a single test, to accomplish what might have required several different tests in the past.  NGS technology may allow the diagnosis of infections without prior knowledge of disease(s) cause. NGS technology can potentially reveal the presence of all microorganisms in a patient sample. Using infectious disease NGS (ID-NGS) technology, each microbial pathogen may be identified by its unique genomic fingerprint. The vision of ID-NGS technology is to further improve patient care by delivering diagnostics ...","","https://precision.fda.gov/challenges/3","completed","6","","2018-08-03","2018-10-18","2023-06-23 00:00:00","2023-10-14 05:39:24"
 "84","multi-omics-enabled-sample-mislabeling-correction","Multi-omics Enabled Sample Mislabeling Correction","Identify and correct sample and data mislabeling events","In biomedical research, sample mislabeling (accidental swapping of patient samples) or data mislabeling (accidental swapping of patient omics data) has been a long-standing problem that contributes to irreproducible results and invalid conclusions. These problems are particularly prevalent in large scale multi-omics studies, in which multiple different omics experiments are carried out at different time periods and/or in different labs. Human errors could arise during sample transferring, sample tracking, large-scale data generation, and data sharing/management. Thus, there is a pressing need to identify and correct sample and data mislabeling events to ensure the right data for the right patient. Simultaneous use of multiple types of omics platforms to characterize a large set of biological samples, as utilized in The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects, has been demonstrated as a powerful approach to understanding the ...","","https://precision.fda.gov/challenges/4","completed","6","https://doi.org/10.1038/s41591-018-0180-x","2018-09-24","2018-12-19","2023-06-23 00:00:00","2023-11-14 19:07:58"
@@ -91,7 +91,7 @@
 "90","covid-19-precision-immunology-app-a-thon","COVID-19 Precision Immunology App-a-thon","Seeking insights on COVID-19 pathophysiology to enable effective strategies","The novel coronavirus disease 2019 (COVID-19), a respiratory disease caused by a new type of coronavirus, known as “severe acute respiratory syndrome coronavirus 2” or SARS-CoV-2, was declared a global pandemic by the World Health Organization on March 11, 2020. To date, the Johns Hopkins University COVID-19 dashboard reports over 62 million confirmed cases worldwide, with a wide range of disease severity from asymptomatic to deaths (over 1.46 million). To effectively combat the widespread transmission of COVID-19 infection and save lives especially of those vulnerable individuals, it is imperative to better understand its pathophysiology to enable effective diagnosis, prognosis and treatment strategies using rapidly shared data.","","https://precision.fda.gov/challenges/12/","completed","6","","2020-11-30","2021-01-29","2023-06-23 00:00:00","2023-10-14 05:39:29"
 "91","smarter-food-safety-low-cost-tech-enabled-traceability","Smarter Food Safety Low Cost Tech-Enabled Traceability","Seeking affordable tech solutions for food traceability","The motivation is tapping into new technologies and integrating data streams will help to advance the widespread, consistent implementation of traceability systems across the food industry. However, the affordability of such technologies, particularly for smaller companies, can be a barrier to implementing tech-enabled traceability systems.    FDA's New Era of Smarter Food Safety initiative strives to work with stakeholders to explore low-cost or no-cost options so that our approaches are inclusive of and viable for human and animal food operations of all sizes. Democratizing the benefits of digitizing data will allow the entire food system to move more rapidly towards digital traceability systems. The primary goal is to encourage stakeholders, including technology providers, public health advocates, entrepreneurs, and innovators from all disciplines and around the world, to develop traceability hardware, software, or data analytics platforms that are low-cost or no-cost to the en...","","https://precision.fda.gov/challenges/13","completed","6","","2021-06-01","2021-07-30","2023-06-23 00:00:00","2023-10-17 23:05:49"
 "92","tumor-mutational-burden-tmb-challenge-phase-1","Tumor Mutational Burden (TMB) Challenge Phase 1","Standardize tumor mutational burden (TMB) calculation in cancer research","Tumor mutational burden (TMB) is generally defined as the number of mutations detected in a patient's tumor sample per megabase of DNA sequenced. However different algorithms use different methods for calculating TMB. Mutations in genes in tumor cells may lead to the creation of neoantigens, which have the potential to activate an immune system response against the tumor, and the likelihood of an immune system response may increase with the number of mutations. Thus, TMB is a biomarker for some immunotherapy drugs, called immune checkpoint inhibitors, such as those that target the PD-1 and PD-L1 pathways (Chan et al., 2019).  An outstanding problem is the lack of standardization for TMB calculation and reporting between different assays. To address this problem, the Friends of Cancer Research convened a working group of industry and regulatory stakeholders to develop guidance and tools for TMB harmonization. Results from the first phase of this effort were presented at AACR 2020 (...","","https://precision.fda.gov/challenges/17","completed","6","","2021-06-21","2021-09-13","2023-06-23 00:00:00","2023-11-02 18:28:46"
-"93","kits21","KiTS21","Contest seeks best kidney tumor segmentation system","The 2021 Kidney and Kidney Tumor Segmentation challenge (abbreviated KiTS21) is a competition in which teams compete to develop the best system for automatic semantic segmentation of renal tumors and surrounding anatomy. Kidney cancer is one of the most common malignancies in adults around the world, and its incidence is thought to be increasing [1]. Fortunately, most kidney tumors are discovered early while they're still localized and operable. However, there are important questions concerning management of localized kidney tumors that remain unanswered [2], and metastatic renal cancer remains almost uniformly fatal [3]. Kidney tumors are notorious for their conspicuous appearance in computed tomography (CT) imaging, and this has enabled important work by radiologists and surgeons to study the relationship between tumor size, shape, and appearance and its prospects for treatment [4,5,6]. It's laborious work, however, and it relies on assessments that are often subjective and impr...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/594/rendering_dimmed.png","https://kits21.grand-challenge.org/","completed","5","","2021-08-23","2021-09-17","2023-06-23 00:00:00","2023-11-28 00:29:34"
+"93","kits21","KiTS21","Contest seeks best kidney tumor segmentation system","The 2021 Kidney and Kidney Tumor Segmentation challenge (abbreviated KiTS21) is a competition in which teams compete to develop the best system for automatic semantic segmentation of renal tumors and surrounding anatomy. Kidney cancer is one of the most common malignancies in adults around the world, and its incidence is thought to be increasing [1]. Fortunately, most kidney tumors are discovered early while they're still localized and operable. However, there are important questions concerning management of localized kidney tumors that remain unanswered [2], and metastatic renal cancer remains almost uniformly fatal [3]. Kidney tumors are notorious for their conspicuous appearance in computed tomography (CT) imaging, and this has enabled important work by radiologists and surgeons to study the relationship between tumor size, shape, and appearance and its prospects for treatment [4,5,6]. It's laborious work, however, and it relies on assessments that are often subjective and impr...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/594/rendering_dimmed.png","https://kits21.grand-challenge.org/","completed","5","","2021-08-23","2021-09-17","2023-06-23 00:00:00","2023-11-28 00:32:11"
 "94","realnoisemri","RealNoiseMRI","Brain MRI reconstruction challenge with realistic noise","In recent years, there is a growing focus on the application of fast magnetic resonance imaging (MRI) based on prior knowledge. In the 1980s and 2000s the community used either purely mathematical models such as the partial Fourier transform or solutions derived through advanced engineering such as parallel imaging to speed up MRI acquisition. Since the mid-2000's, compressed sensing and artificial intelligence have been employed to speed up MRI acquisition. These newer methods rely on under sampling the data acquired in Fourier (aka k-) space and then interpolating or augmenting k-space data based on training data content. One of the underlying problems for the development of fast imaging techniques, that just as in e.g. [1], it is common to use a fully sampled image as ground truth and then under sample it in k-space in order to simulate under sampled data. The problem with this approach is that in cases were the under sampled data is corrupted, through e.g. motion, this under s...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/597/Logo_Challenge.png","https://realnoisemri.grand-challenge.org/","completed","5","","2021-09-21","2021-12-06","2023-06-23 00:00:00","2023-11-27 20:40:05"
 "95","deep-generative-model-challenge-for-da-in-surgery","Deep Generative Model Challenge for DA in Surgery","Challenge aims to adapt algorithms from simulation to mitral valve surgery","Mitral regurgitation (MR) is the second most frequent indication for valve surgery in Europe and may occur for organic or functional causes [1]. Mitral valve repair, although considerably more difficult, is prefered over mitral valve replacement, since the native tissue of the valve is preserved. It is a complex on-pump heart surgery, often conducted only by a handful of surgeons in high-volume centers. Minimally invasive procedures, which are performed with endoscopic video recordings, became more and more popular in recent years. However, data availability and data privacy concerns are still an issue for the development of automatic scene analysis algorithms. The AdaptOR challenge aims to address these issues by formulating a domain adaptation problem from simulation to surgery. We provide a smaller number of datasets from real surgeries, and a larger number of annotated recordings of training and planning sessions from a physical mitral valve simulator. The goal is to reduce th...","","https://adaptor2021.github.io/","completed","1","","2021-04-01","2021-07-16","2023-06-23 00:00:00","2023-10-14 05:39:34"
 "96","aimdatathon","AIM Datathon 2020","AI in Medicine (AIM) Datathon 2020","Join the AI in Medicine ( AIM ) Datathon 2020","","https://www.kaggle.com/competitions/aimdatathon","completed","8","","2020-11-09","2020-11-22","2023-06-23 00:00:00","2023-11-15 22:43:19"
@@ -143,10 +143,10 @@
 "142","tweet-mental-health-classification","Tweet Mental Health Classification","Build models to classify tweets to determine mental health","Build Models to classify tweets to determine mental health","","https://www.kaggle.com/competitions/tweet-mental-health-classification","completed","8","","2021-12-27","2022-01-31","2023-06-23 00:00:00","2023-10-14 05:40:07"
 "143","ml-olympiad-good-health-and-well-being","ML Olympiad - GOOD HEALTH AND WELL BEING","Use your ML expertise to classify if a patient has heart disease or not","Use your ML expertise to classify if a patient has heart disease or not","","https://www.kaggle.com/competitions/ml-olympiad-good-health-and-well-being","completed","8","","2022-02-03","2022-03-01","2023-06-23 00:00:00","2023-10-16 18:15:20"
 "144","rsna-breast-cancer-detection","RSNA Screening Mammography Breast Cancer Detection","Find breast cancers in screening mammograms","Find breast cancers in screening mammograms","","https://www.kaggle.com/competitions/rsna-breast-cancer-detection","completed","8","","2022-11-28","2023-02-27","2023-06-23 00:00:00","2023-10-14 05:40:12"
-"145","biocreative-vii-text-mining-drug-and-chemical-protein-interactions-drugprot","BioCreative VII: Text mining drug and chemical-protein interactions (DrugProt)","Develop systems to extract drug-gene relations from text","With the rapid accumulation of biomedical literature, it is getting increasingly challenging to exploit efficiently drug-related information described in the scientific literature. One of the most relevant aspects of drugs and chemical compounds are their relationships with certain biomedical entities, in particular genes and proteins.  The aim of the DrugProt track (similar to the previous CHEMPROT task of BioCreative VI) is to promote the development and evaluation of systems that are able to automatically detect in relations between chemical compounds/drug and genes/proteins.  There are a range of different types of drug-gene/protein interactions, and their systematic extraction and characterization is essential to analyze, predict and explore key biomedical properties underlying high impact biomedical applications. These application scenarios include use cases related to drug discovery, drug repurposing, drug design, metabolic engineering, modeling drug response, pharmacogenet...","","https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/","completed","14","","2021-06-15","2021-09-22","2023-06-23 00:00:00","2023-11-01 20:37:37"
-"146","extended-literature-ai-for-drug-induced-liver-injury","Extended Literature AI for Drug Induced Liver Injury","Develop ML tools to analyze drug texts for liver injury data","Unexpected Drug-Induced Liver Injury (DILI) still is one of the main killers of promising novel drug candidates. It is a clinically significant disease that can lead to severe outcomes such as acute liver failure and even death. It remains one of the primary liabilities in drug development and regulatory clearance due to the limited performance of mandated preclinical models even today. The free text of scientific publications is still the main medium carrying DILI results from clinical practice or experimental studies. The textual data still has to be analysed manually. This process, however, is tedious and prone to human mistakes or omissions, as results are very rarely available in a standardized form or organized form. There is thus great hope that modern techniques from machine learning or natural language processing could provide powerful tools to better process and derive the underlying knowledge within free form texts. The pressing need to faster process potential drug can...","","http://camda2022.bioinf.jku.at/contest_dataset#extended_literature_ai_for_drug_induced_liver_injury","completed","14","","\N","2022-05-20","2023-06-23 00:00:00","2023-11-01 20:37:38"
-"147","anti-microbial-resistance-forensics","Anti-Microbial Resistance Forensics","Classifying bacteriophages to understand microbial evolution","Bacteriophages, being the re-occuring mystery in the history of science are believed to be they key for understanding of microbial evolution and the transfer of AMR genes. Recent studies show that there is a significant correlation between occurence of Phages and AMR genes, indicating that they are indeed taking part in the spread of them. While taking part in AMR dissemination the phages are also considered as the potential alternative to antibiotics. In such contradictory world there is a huge potential as well as urgent need for precise classification, description and analysis of capabilities. Due to pandemic of SARS-CoV-2, advance in phylogenetic algorithms and k-mer based methods have been extremely rapid and those improvements are witing to be adapted to different branches of life sciences.","","http://camda2022.bioinf.jku.at/contest_dataset#anti-microbial_resistance_forensics","completed","14","","\N","2022-05-20","2023-06-23 00:00:00","2023-10-14 05:40:14"
-"148","disease-maps-to-modelling-covid-19","Disease Maps to Modelling COVID-19","Suggest drugs candidate for repurposing","The Disease Maps to modeling COVID-19 Challenge provides highly detailed expert-curated molecular mechanistic maps for COVID-19. Combine them with available omic data to expand the current biological knowledge on COVID-19 mechanism of infection and downstream consequences. The main topic for this year's challenge is drug repurposing with the possibility of Real World Data based validation of the most promising candidates suggested.","","http://camda2022.bioinf.jku.at/contest_dataset#disease_maps_to_modelling_covid-19","completed","14","","\N","2022-05-20","2023-06-23 00:00:00","2023-11-14 19:23:47"
+"145","biocreative-vii-text-mining-drug-and-chemical-protein-interactions-drugprot","BioCreative VII: Text mining drug and chemical-protein interactions (DrugProt)","Develop systems to extract drug-gene relations from text","With the rapid accumulation of biomedical literature, it is getting increasingly challenging to exploit efficiently drug-related information described in the scientific literature. One of the most relevant aspects of drugs and chemical compounds are their relationships with certain biomedical entities, in particular genes and proteins.  The aim of the DrugProt track (similar to the previous CHEMPROT task of BioCreative VI) is to promote the development and evaluation of systems that are able to automatically detect in relations between chemical compounds/drug and genes/proteins.  There are a range of different types of drug-gene/protein interactions, and their systematic extraction and characterization is essential to analyze, predict and explore key biomedical properties underlying high impact biomedical applications. These application scenarios include use cases related to drug discovery, drug repurposing, drug design, metabolic engineering, modeling drug response, pharmacogenet...","","https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/","completed","\N","","2021-06-15","2021-09-22","2023-06-23 00:00:00","2023-11-01 20:37:37"
+"146","extended-literature-ai-for-drug-induced-liver-injury","Extended Literature AI for Drug Induced Liver Injury","Develop ML tools to analyze drug texts for liver injury data","Unexpected Drug-Induced Liver Injury (DILI) still is one of the main killers of promising novel drug candidates. It is a clinically significant disease that can lead to severe outcomes such as acute liver failure and even death. It remains one of the primary liabilities in drug development and regulatory clearance due to the limited performance of mandated preclinical models even today. The free text of scientific publications is still the main medium carrying DILI results from clinical practice or experimental studies. The textual data still has to be analysed manually. This process, however, is tedious and prone to human mistakes or omissions, as results are very rarely available in a standardized form or organized form. There is thus great hope that modern techniques from machine learning or natural language processing could provide powerful tools to better process and derive the underlying knowledge within free form texts. The pressing need to faster process potential drug can...","","http://camda2022.bioinf.jku.at/contest_dataset#extended_literature_ai_for_drug_induced_liver_injury","completed","\N","","\N","2022-05-20","2023-06-23 00:00:00","2023-11-01 20:37:38"
+"147","anti-microbial-resistance-forensics","Anti-Microbial Resistance Forensics","Classifying bacteriophages to understand microbial evolution","Bacteriophages, being the re-occuring mystery in the history of science are believed to be they key for understanding of microbial evolution and the transfer of AMR genes. Recent studies show that there is a significant correlation between occurence of Phages and AMR genes, indicating that they are indeed taking part in the spread of them. While taking part in AMR dissemination the phages are also considered as the potential alternative to antibiotics. In such contradictory world there is a huge potential as well as urgent need for precise classification, description and analysis of capabilities. Due to pandemic of SARS-CoV-2, advance in phylogenetic algorithms and k-mer based methods have been extremely rapid and those improvements are witing to be adapted to different branches of life sciences.","","http://camda2022.bioinf.jku.at/contest_dataset#anti-microbial_resistance_forensics","completed","\N","","\N","2022-05-20","2023-06-23 00:00:00","2023-10-14 05:40:14"
+"148","disease-maps-to-modelling-covid-19","Disease Maps to Modelling COVID-19","Suggest drugs candidate for repurposing","The Disease Maps to modeling COVID-19 Challenge provides highly detailed expert-curated molecular mechanistic maps for COVID-19. Combine them with available omic data to expand the current biological knowledge on COVID-19 mechanism of infection and downstream consequences. The main topic for this year's challenge is drug repurposing with the possibility of Real World Data based validation of the most promising candidates suggested.","","http://camda2022.bioinf.jku.at/contest_dataset#disease_maps_to_modelling_covid-19","completed","\N","","\N","2022-05-20","2023-06-23 00:00:00","2023-11-14 19:23:47"
 "149","crowdsourced-evaluation-of-inchi-based-tautomer-identification","Crowdsourced Evaluation of InChI-based Tautomer Identification","Test a modified InChi algorithm","This challenge focuses on the International Chemical Identifier (InChI), which was developed and is maintained under the auspices of the International Union of Pure and Applied Chemistry (IUPAC) and the InChI Trust. The InChI Trust, the IUPAC Working Group on Tautomers, and the U.S. Food and Drug Administration (FDA) call on the scientific community dealing with chemical repositories/data sets and analytics of compounds to test the recently modified InChI algorithm, which was designed for advanced recognition of tautomers. Participants will evaluate this algorithm against real chemical samples in this Crowdsourced Evaluation of InChI-based Tautomer Identification.","","https://precision.fda.gov/challenges/29","completed","6","","2022-11-01","2023-03-01","2023-06-23 00:00:00","2023-11-14 19:21:10"
 "150","nctr-indel-calling-from-oncopanel-sequencing-challenge-phase-2","NCTR Indel Calling from Oncopanel Sequencing Challenge Phase 2","Calling from oncopanel sequencing data","The high value of clinically actionable information obtained by oncopanel sequencing makes it a crucial tool for precision oncology[1,2]. With the surge in availability of oncopanels, it is critical to ensure that they have been thoroughly tested and are properly used. FDA has initiated the Sequencing Quality Control phase II (SEQC2) project[3] to develop standard analysis protocols and quality control metrics for fit-for-purpose use of Next Generation Sequencing (NGS) data including oncopanel sequencing to inform regulatory science research and precision medicine. The Oncopanel Sequencing Working Group of FDA-led SEQC2 has developed a reference sample[4] suitable for benchmarking oncopanels and comprehensively assessed the analytical performance of several oncopanels[1,2]. The genomic deoxyribonucleic acid (gDNA) reference sample was derived from ten Universal Human Reference RNA (UHRR, Agilent Technologies, Inc) cell-lines and made publicly available by Agilent.  Substantial gen...","","https://precision.fda.gov/challenges/22","completed","6","","2022-07-11","2022-07-26","2023-06-23 00:00:00","2023-11-15 22:53:48"
 "151","nctr-indel-calling-from-oncopanel-sequencing-data-challenge-phase-1","NCTR Indel Calling from Oncopanel Sequencing Data Challenge Phase 1","Identify indels in oncopanel sequencing datasets","The high value of clinically actionable information obtained by oncopanel sequencing makes it a crucial tool for precision oncology[1,2]. With the surge in availability of oncopanels, it is critical to ensure that they have been thoroughly tested and are properly used. FDA has initiated the Sequencing Quality Control phase II (SEQC2) project[3] to develop standard analysis protocols and quality control metrics for fit-for-purpose use of Next Generation Sequencing (NGS) data including oncopanel sequencing to inform regulatory science research and precision medicine. The Oncopanel Sequencing Working Group of FDA-led SEQC2 has developed a reference sample[4] suitable for benchmarking oncopanels and comprehensively assessed the analytical performance of several oncopanels[1,2]. The genomic deoxyribonucleic acid (gDNA) reference sample was derived from ten Universal Human Reference RNA (UHRR, Agilent Technologies, Inc) cell-lines and made publicly available by Agilent.  Substantial gen...","","https://precision.fda.gov/challenges/21","completed","6","","2022-05-02","2022-07-08","2023-06-23 00:00:00","2023-11-14 19:24:33"
@@ -155,9 +155,9 @@
 "154","predicting-gene-expression-using-millions-of-random-promoter-sequences","Predicting Gene Expression Using Millions of Random Promoter Sequences","Decoding gene expression regulation to understand disease","Decoding how gene expression is regulated is critical to understanding disease. Regulatory DNA is decoded by the cell in a process termed “cis-regulatory logic”, where proteins called Transcription Factors (TFs) bind to specific DNA sequences within the genome and work together to produce as output a level of gene expression for downstream adjacent genes. This process is exceedingly complex to model as a large number of parameters is needed to fully describe the process (see Rationale, de Boer et al. 2020; Zeitingler J. 2020). Understanding the cis-regulatory logic of the human genome is an important goal and would provide insight into the origins of many diseases. However, learning models from human data is challenging due to limitations in the diversity of sequences present within the human genome (e.g. extensive repetitive DNA), the vast number of cell types that differ in how they interpret regulatory DNA, limited reporter assay data, and substantial technical biases present i...","","https://www.synapse.org/#!Synapse:syn28469146/wiki/617075","completed","1","","2022-06-15","2022-08-07","2023-06-23 00:00:00","2023-10-14 05:40:21"
 "155","brats-2023","BraTS 2023","Benchmarking brain tumor segmentation with expanded dataset","The International Brain Tumor Segmentation (BraTS) challenge. BraTS, since 2012, has focused on the generation of a benchmarking environment and dataset for the delineation of adult brain gliomas. The focus of this year’s challenge remains the generation of a common benchmarking environment, but its dataset is substantially expanded to ~4,500 cases towards addressing additional i) populations (e.g., sub-Saharan Africa patients), ii) tumors (e.g., meningioma), iii) clinical concerns (e.g., missing data), and iv) technical considerations (e.g., augmentations). Specifically, the focus of BraTS 2023 is to identify the current state-of-the-art algorithms for addressing (Task 1) the same adult glioma population as in the RSNA-ANSR-MICCAI BraTS challenge, as well as (Task 2) the underserved sub-Saharan African brain glioma patient population, (Task 3) intracranial meningioma, (Task 4) brain metastasis, (Task 5) pediatric brain tumor patients, (Task 6) global & local missing data, (Task 7...","","https://www.synapse.org/brats","completed","1","","2023-06-01","2023-08-25","2023-06-23 00:00:00","2023-10-26 23:20:21"
 "156","cagi7","CAGI7","The seventh round of CAGI","There have been six editions of CAGI experiments, held between 2010 and 2022. The seventh round of CAGI is planned to take place over the Summer of 2024.","","https://genomeinterpretation.org/challenges.html","upcoming","1","","\N","\N","2023-08-04 21:47:38","2023-11-20 20:19:08"
-"157","casp15","CASP15","Establish the state-of-art in modeling proteins and protein complexes","CASP14 (2020) saw an enormous jump in the accuracy of single protein and domain models such that many are competitive with experiment. That advance is largely the result of the successful application of deep learning methods, particularly by the AlphaFold and, since that CASP, RosettaFold. As a consequence, computed protein structures are becoming much more widely used in a broadening range of applications. CASP has responded to this new landscape with a revised set of modeling categories. Some old categories have been dropped (refinement, contact prediction, and aspects of model accuracy estimation) and new ones have been added (RNA structures, protein ligand complexes, protein ensembles, and accuracy estimation for protein complexes). We are also strengthening our interactions with our partners CAPRI and CAMEO. We hope that these changes will maximize the insight that CASP15 provides, particularly in new applications of deep learning.","","https://predictioncenter.org/casp15/index.cgi","completed","14","","2022-04-18","\N","2023-08-04 21:52:12","2023-09-28 23:09:59"
+"157","casp15","CASP15","Establish the state-of-art in modeling proteins and protein complexes","CASP14 (2020) saw an enormous jump in the accuracy of single protein and domain models such that many are competitive with experiment. That advance is largely the result of the successful application of deep learning methods, particularly by the AlphaFold and, since that CASP, RosettaFold. As a consequence, computed protein structures are becoming much more widely used in a broadening range of applications. CASP has responded to this new landscape with a revised set of modeling categories. Some old categories have been dropped (refinement, contact prediction, and aspects of model accuracy estimation) and new ones have been added (RNA structures, protein ligand complexes, protein ensembles, and accuracy estimation for protein complexes). We are also strengthening our interactions with our partners CAPRI and CAMEO. We hope that these changes will maximize the insight that CASP15 provides, particularly in new applications of deep learning.","","https://predictioncenter.org/casp15/index.cgi","completed","\N","","2022-04-18","\N","2023-08-04 21:52:12","2023-09-28 23:09:59"
 "158","synthrad2023","SynthRAD2023","Automatic generation of synthetic computed tomography (sCT) for radiotherapy","This challenge aims to provide the first platform offering public data evaluation metrics to compare the latest developments in sCT generation methods. The accepted challenge design approved by MICCAI can be found at https://doi.org/10.5281/zenodo.7746019. A type 2 challenge will be run, where the participant needs to submit their algorithm packaged in a docker both for validation and test.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/678/SynthRAD_square_logo_MnAqldI.png","https://synthrad2023.grand-challenge.org/","completed","5","","2023-04-01","2023-08-22","2023-08-04 21:54:31","2023-11-14 19:24:54"
-"159","synthetic-data-for-instrument-segmentation-in-surgery-syn-iss","Synthetic Data for Instrument Segmentation in Surgery (Syn-ISS)","Surgical instrument segmentation with synthetic data","A common limitation noted by the surgical data science community is the size of datasets and the resources needed to generate training data at scale for building reliable and high-performing machine learning models. Beyond unsupervised and self-supervised approaches another solution within the broader machine learning community has been a growing volume of literature in the use of synthetic data (simulation) for training algorithms than can be applied to real world data. Synthetic data has multiple benefits like free groundtruth at large scale, possibility to collect larger sample of rare events, include anatomical variations, etc. A first step towards proving the validity of using synthetic data for real world applications is to demonstrate the feasibility within the simulation world itself. Our proposed challenge is to train machine learning methods for instrument segmentation using synthetic datasets and test their performance on synthetic datasets. That is, the challenge parti...","","https://www.synapse.org/#!Synapse:syn50908388/wiki/620516","completed","1","","2023-07-19","2023-09-07","2023-08-04 23:49:44","2023-11-14 19:25:04"
+"159","synthetic-data-for-instrument-segmentation-in-surgery-syn-iss","Synthetic Data for Instrument Segmentation in Surgery (Syn-ISS)","Surgical instrument segmentation with synthetic data","A common limitation noted by the surgical data science community is the size of datasets and the resources needed to generate training data at scale for building reliable and high-performing machine learning models. Beyond unsupervised and self-supervised approaches another solution within the broader machine learning community has been a growing volume of literature in the use of synthetic data (simulation) for training algorithms than can be applied to real world data. Synthetic data has multiple benefits like free groundtruth at large scale, possibility to collect larger sample of rare events, include anatomical variations, etc. A first step towards proving the validity of using synthetic data for real world applications is to demonstrate the feasibility within the simulation world itself. Our proposed challenge is to train machine learning methods for instrument segmentation using synthetic datasets and test their performance on synthetic datasets. That is, the challenge parti...","","https://www.synapse.org/#!Synapse:syn50908388/wiki/620516","completed","1","","2023-07-19","2023-09-07","2023-08-04 23:49:44","2023-12-06 07:16:20"
 "160","pitvis","PitVis","Surgical workflow and instrument recognition in endonasal surgery","The pituitary gland, found just off the base of the brain, is commonly known as “the master gland”, performing essential functions required for sustaining human life. Clinically relevant tumours that have grown on the pituitary gland have an estimated prevalence of 1 in 1000 of the population, and if left untreated can be life-limiting. The “gold standard” treatment is endoscopic pituitary surgery, where the tumour is directly removed by entering through a nostril. This surgery is particularly challenging due to the small working space which limits both vision and instrument manoeuvrability and thus can lead to poor surgical technique causing adverse outcomes for the patient. Computer-assisted intervention can help overcome these challenges by providing guidance for senior surgeons and operative staff during surgery, and for junior surgeons during training.","","https://www.synapse.org/#!Synapse:syn51232283/wiki/","completed","1","","2023-06-29","2023-09-10","2023-08-04 23:58:01","2023-10-26 23:20:30"
 "161","mvseg2023","MVSEG2023","Single frame 3D trans-esophageal echocardiography","Mitral valve (MV) disease is a common pathologic problem occurring in approximately 2 % of the general population but climbing to 10 % in those over the age of 75. The preferred intervention for mitral regurgitation is valve repair, due to superior patient outcomes compared to those following valve replacement. Mitral valve interventions are technically challenging due to the functional and anatomical complexity of mitral pathologies. Repair must be tailored to the patient-specific anatomy and pathology, which requires considerable expert training and experience. Automatic segmentation of the mitral valve leaflets from 3D transesophageal echocardiography (TEE) may play an important role in treatment planning, as well as physical and computational modelling of patient-specific valve pathologies and potential repair approaches. This may have important implications in the drive towards personalized care and has the potential to impact clinical outcomes for those undergoing mitral val...","","https://www.synapse.org/#!Synapse:syn51186045/wiki/621356","completed","1","","2023-05-29","2023-08-07","2023-08-05 0-04-36","2023-11-14 19:25:13"
 "162","crossmoda23","crossMoDA23","Medical imaging benchmark for unsupervised domain adaptation","Domain Adaptation (DA) has recently raised strong interest in the medical imaging community. By encouraging algorithms to be robust to unseen situations or different input data domains, Domain Adaptation improves the applicability of machine learning approaches to various clinical settings. While a large variety of DA techniques has been proposed, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly address single-class problems. To tackle these limitations, the crossMoDA challenge introduced the first large and multi-class dataset for unsupervised cross-modality Domain Adaptation. From an application perspective, crossMoDA focuses on MRI segmentation for Vestibular Schwannoma. Compared to the previous crossMoDA instance, which made use of multi-institutional data acquired in controlled conditions for radiosurgery planning and focused on a 2 class segmentation task (tumour and cochlea), the...","","https://www.synapse.org/#!Synapse:syn51236108/wiki/621615","completed","1","","2023-04-15","2023-07-10","2023-08-05 0-13-23","2023-11-14 19:27:00"
@@ -165,7 +165,7 @@
 "164","cafa-5-protein-function-prediction","CAFA 5: Protein Function Prediction","Predict the biological function of a protein","The goal of this competition is to predict the function of a set of proteins. You will develop a model trained on the amino-acid sequences of the proteins and on other data. Your work will help ​​researchers better understand the function of proteins, which is important for discovering how cells, tissues, and organs work. This may also aid in the development of new drugs and therapies for various diseases.","","https://www.kaggle.com/competitions/cafa-5-protein-function-prediction","completed","8","","2023-04-18","2023-08-21","2023-08-05 5-18-40","2023-10-19 00:13:14"
 "165","rsna-2023-abdominal-trauma-detection","RSNA 2023 Abdominal Trauma Detection","Detect and classify traumatic abdominal injuries","Traumatic injury is the most common cause of death in the first four decades of life and a major public health problem around the world. There are estimated to be more than 5 million annual deaths worldwide from traumatic injury. Prompt and accurate diagnosis of traumatic injuries is crucial for initiating appropriate and timely interventions, which can significantly improve patient outcomes and survival rates. Computed tomography (CT) has become an indispensable tool in evaluating patients with suspected abdominal injuries due to its ability to provide detailed cross-sectional images of the abdomen.  Interpreting CT scans for abdominal trauma, however, can be a complex and time-consuming task, especially when multiple injuries or areas of subtle active bleeding are present. This challenge seeks to harness the power of artificial intelligence and machine learning to assist medical professionals in rapidly and precisely detecting injuries and grading their severity. The development...","","https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection","completed","8","","2023-07-26","2023-10-13","2023-08-05 5-24-09","2023-09-28 23:14:12"
 "166","hubmap-hacking-the-human-vasculature","HuBMAP: Hacking the Human Vasculature","Microvascular structures from healthy human kidney tissue images","The goal of this competition is to segment instances of microvascular structures, including capillaries, arterioles, and venules. You'll create a model trained on 2D PAS-stained histology images from healthy human kidney tissue slides. Your help in automating the segmentation of microvasculature structures will improve researchers' understanding of how the blood vessels are arranged in human tissues.","","https://www.kaggle.com/competitions/hubmap-hacking-the-human-vasculature","completed","8","","2023-05-22","2023-07-31","2023-08-05 5-31-12","2023-11-14 19:25:45"
-"167","amp-parkinsons-disease-progression-prediction","AMP(R)-Parkinson's Disease Progression Prediction","Predict clinical and molecular progression of the disease","The goal of this competition is to predict MDS-UPDR scores, which measure progression in patients with Parkinson's disease. The Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is a comprehensive assessment of both motor and non-motor symptoms associated with Parkinson's. You will develop a model trained on data of protein and peptide levels over time in subjects with Parkinson’s disease versus normal age-matched control subjects. Your work could help provide important breakthrough information about which molecules change as Parkinson’s disease progresses.","","https://www.kaggle.com/competitions/amp-parkinsons-disease-progression-prediction","completed","8","","2023-02-16","2023-05-18","2023-08-05 5-37-12","2023-11-14 19:25:52"
+"167","amp-parkinsons-disease-progression-prediction","AMP(R)-Parkinson's Disease Progression Prediction","Predict clinical and molecular progression of the disease","The goal of this competition is to predict MDS-UPDR scores, which measure progression in patients with Parkinson's disease. The Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is a comprehensive assessment of both motor and non-motor symptoms associated with Parkinson's. You will develop a model trained on data of protein and peptide levels over time in subjects with Parkinson’s disease versus normal age-matched control subjects. Your work could help provide important breakthrough information about which molecules change as Parkinson’s disease progresses.","","https://www.kaggle.com/competitions/amp-parkinsons-disease-progression-prediction","completed","8","","2023-02-16","2023-05-18","2023-08-05 5-37-12","2023-12-06 22:44:19"
 "168","open-problems-multimodal","Open Problems -Multimodal Single-Cell Integration","Predict how DNA, RNA & protein measurements co-vary in single cells","The goal of this competition is to predict how DNA, RNA, and protein measurements co-vary in single cells as bone marrow stem cells develop into more mature blood cells. You will develop a model trained on a subset of 300,000-cell time course dataset of CD34+ hematopoietic stem and progenitor cells (HSPC) from four human donors at five time points generated for this competition by Cellarity, a cell-centric drug creation company. In the test set, taken from an unseen later time point in the dataset, competitors will be provided with one modality and be tasked with predicting a paired modality measured in the same cell. The added challenge of this competition is that the test data will be from a later time point than any time point in the training data. Your work will help accelerate innovation in methods of mapping genetic information across layers of cellular state. If we can predict one modality from another, we may expand our understanding of the rules governing these complex re...","","https://www.kaggle.com/competitions/open-problems-multimodal","completed","8","","2022-08-15","2022-11-15","2023-08-05 5-43-25","2023-10-10 19:52:41"
 "169","multi-atlas-labeling-beyond-the-cranial-vault","Multi-Atlas Labeling Beyond the Cranial Vault","Innovative multi-atlas labeling for soft tissue segmentation on clinical CT","Multi-atlas labeling has proven to be an effective paradigm for creating segmentation algorithms from training data. These approaches have been extraordinarily successful for brain and cranial structures (e.g., our prior MICCAI workshops-MLSF’11, MAL’12, SATA’13). After the original challenges closed, the data continue to drive scientific innovation; 144 groups have registered for the 2012 challenge (brain only) and 115 groups for the 2013 challenge (brain/heart/canine leg). However, innovation in application outside of the head and to soft tissues has been more limited. This workshop will provide a snapshot of the current progress in the field through extended discussions and provide researchers an opportunity to characterize their methods on a newly created and released standardized dataset of abdominal anatomy on clinically acquired CT. The datasets will be freely available both during and after the challenge. We have two separate new challenges-abdomen and cervix on routinely ...","","https://www.synapse.org/#!Synapse:syn3193805/wiki/89480","active","1","","2015-04-15","\N","2023-08-07 20:21:22","2023-10-10 19:52:39"
 "170","hubmap-organ-segmentation","HuBMAP + HPA: Hacking the Human Body","Segment multi-organ functional tissue units","In this competition, you’ll identify and segment functional tissue units (FTUs) across five human organs. You'll build your model using a dataset of tissue section images, with the best submissions segmenting FTUs as accurately as possible. If successful, you'll help accelerate the world’s understanding of the relationships between cell and tissue organization. With a better idea of the relationship of cells, researchers will have more insight into the function of cells that impact human health. Further, the Human Reference Atlas constructed by HuBMAP will be freely available for use by researchers and pharmaceutical companies alike, potentially improving and prolonging human life.","","https://www.kaggle.com/competitions/hubmap-organ-segmentation","completed","8","","2022-06-22","2022-09-22","2023-08-08 16:30:22","2023-11-02 18:44:27"
@@ -181,9 +181,9 @@
 "180","lish-moa","Mechanisms of Action (MoA) Prediction","Improve the algorithm that classifies drugs based on their biological activity","Can you improve the algorithm that classifies drugs based on their biological activity?","","https://www.kaggle.com/competitions/lish-moa","completed","8","","2020-09-03","2020-11-30","2023-08-08 19:09:31","2023-11-14 19:33:49"
 "181","recursion-cellular-image-classification","Recursion Cellular Image Classification","CellSignal-Disentangling biological signal in cellular images","This competition will have you disentangling experimental noise from real biological signals. Your entry will classify images of cells under one of 1,108 different genetic perturbations. You can help eliminate the noise introduced by technical execution and environmental variation between experiments. If successful, you could dramatically improve the industry’s ability to model cellular images according to their relevant biology. In turn, applying AI could greatly decrease the cost of treatments, and ensure these treatments get to patients faster.","","https://www.kaggle.com/competitions/recursion-cellular-image-classification","completed","8","","2019-06-27","2019-09-26","2023-08-08 19:38:42","2023-11-14 19:34:11"
 "182","tlvmc-parkinsons-freezing-gait-prediction","Parkinson's Freezing of Gait Prediction","Event detection from wearable sensor data","The goal of this competition is to detect freezing of gait (FOG), a debilitating symptom that afflicts many people with Parkinson’s disease. You will develop a machine learning model trained on data collected from a wearable 3D lower back sensor. Your work will help researchers better understand when and why FOG episodes occur. This will improve the ability of medical professionals to optimally evaluate, monitor, and ultimately, prevent FOG events.","","https://www.kaggle.com/competitions/tlvmc-parkinsons-freezing-gait-prediction","completed","8","","2023-03-09","2023-06-08","2023-08-08 19:47:54","2023-10-10 19:53:08"
-"183","chaimeleon","CHAIMELEON Open Challenges","AI-powered solutions driving innovation in cancer diagnosis and treatment","The CHAIMELEON Open Challenges is a competition designed to train and refine AI models to answer clinical questions about five types of cancer-prostate, lung, breast, colon, and rectal. Participants are challenged to collaborate and develop innovative AI-powered solutions that can significantly impact cancer diagnosis, management, and treatment. They will be evaluated considering a balance between the performance of their AI algorithms to predict different clinical endpoints such as disease staging, treatment response or progression free survival and their trustworthiness.  The challenges are open to the whole scientific and tech community interested in AI. They are a unique opportunity to showcase how AI can be used to advance medical research and improve patient outcomes within the CHAIMELEON project.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/744/Logo_Grand_Challenge_-_2.png","https://chaimeleon.grand-challenge.org/","active","5","","2023-11-02","2023-11-30","2023-08-09 17:13:09","2023-11-14 19:34:50"
+"183","chaimeleon","CHAIMELEON Open Challenges","AI-powered solutions driving innovation in cancer diagnosis and treatment","The CHAIMELEON Open Challenges is a competition designed to train and refine AI models to answer clinical questions about five types of cancer-prostate, lung, breast, colon, and rectal. Participants are challenged to collaborate and develop innovative AI-powered solutions that can significantly impact cancer diagnosis, management, and treatment. They will be evaluated considering a balance between the performance of their AI algorithms to predict different clinical endpoints such as disease staging, treatment response or progression free survival and their trustworthiness.  The challenges are open to the whole scientific and tech community interested in AI. They are a unique opportunity to showcase how AI can be used to advance medical research and improve patient outcomes within the CHAIMELEON project.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/744/Logo_Grand_Challenge_-_2.png","https://chaimeleon.grand-challenge.org/","completed","5","","2023-11-02","2023-11-30","2023-08-09 17:13:09","2023-11-14 19:34:50"
 "184","topcow23","Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA","Segment the Circle of Willis (CoW) vessel components for both CTA and MRA","The aim of the challenge is to extract the CoW angio-architecture from 3D angiographic imaging by segmentation of the vessel components. There are two sub-tasks-binary segmentation of CoW vessels, and multi-class CoW anatomical segmentation. We release a new dataset of joint-modalities, CTA and MRA of the same patient cohort, both with annotations of the anatomy of CoW. Our challenge has two tracks for the same segmentation task, namely CTA track and MRA track. We made use of the clinical information from both modalities during our annotation. And participants can pick whichever modality they want, both CTA and MRA, and choose to tackle the task for either modality.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/733/TopCow_logo.jpg","https://topcow23.grand-challenge.org/","completed","5","","2023-08-20","2023-09-25","2023-08-09 17:16:22","2023-11-08 00:53:26"
-"185","circle-of-willis-intracranial-artery-classification-and-quantification-challenge-2023","Circle of Willis Intracranial Artery Classification and Quantification Challenge 2023","Classify the circle of Willis (CoW) configuration and quantification","The purpose of this challenge is to compare automatic methods for classification of the circle of Willis (CoW) configuration and quantification of the CoW major artery diameters and bifurcation angles.","","https://crown.isi.uu.nl/","completed","14","","2023-05-01","2023-08-15","2023-08-09 22:13:24","2023-09-28 23:24:54"
+"185","circle-of-willis-intracranial-artery-classification-and-quantification-challenge-2023","Circle of Willis Intracranial Artery Classification and Quantification Challenge 2023","Classify the circle of Willis (CoW) configuration and quantification","The purpose of this challenge is to compare automatic methods for classification of the circle of Willis (CoW) configuration and quantification of the CoW major artery diameters and bifurcation angles.","","https://crown.isi.uu.nl/","completed","\N","","2023-05-01","2023-08-15","2023-08-09 22:13:24","2023-09-28 23:24:54"
 "186","making-sense-of-electronic-health-record-ehr-race-and-ethnicity-data","Making Sense of Electronic Health Record (EHR) Race and Ethnicity Data","Make sense of electronic health record race and ethnicity data","The urgency of the coronavirus disease 2019 (COVID-19) pandemic has heightened interest in the use of real-world data (RWD) to obtain timely information about patients and populations and has focused attention on EHRs. The pandemic has also heightened awareness of long-standing racial and ethnic health disparities along a continuum from underlying social determinants of health, exposure to risk, access to insurance and care, quality of care, and responses to treatments. This highlighted the potential that EHRs can be used to describe and contribute to our understanding of racial and ethnic health disparities and their solutions. The OMB Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity provides minimum standards for maintaining, collecting, and presenting data on race and ethnicity for all Federal reporting purposes, and defines the two separate constructs of race and ethnicity.","","https://precision.fda.gov/challenges/30","completed","6","","2023-05-31","2023-06-23","2023-08-10 18:28:06","2023-11-14 19:34:58"
 "187","the-veterans-cardiac-health-and-ai-model-predictions-v-champs","The Veterans Cardiac Health and AI Model Predictions (V-CHAMPS)","Predict cardiovascular health related outcomes in veterans","To better understand the risk and protective factors in the Veteran population, the VHA IE and its collaborating partners are calling upon the public to develop AI/ML models to predict cardiovascular health outcomes, including readmission and mortality, using synthetically generated Veteran health records.  The Challenge consists of two Phases-Phase 1 is focused on synthetic data. In this Phase of the Challenge, AI/ML models will be developed by Challenge participants and trained and tested on the synthetic data sets provided to them, with a view towards predicting outcome variables for Veterans who have been diagnosed with chronic heart failure (please note that in Phase 1, the data is synthetic Veteran health records). Phase 2 will focus on validating and further exploring the limits of the AI/ML models. During this Phase, high-performing AI/ML models from Phase 1 will be brought into the VA system and validated on the real-world Veterans health data within the VHA. These models...","","https://precision.fda.gov/challenges/31","completed","6","","2023-05-25","2023-08-02","2023-08-10 21:41:10","2023-11-14 19:35:53"
 "188","predicting-high-risk-breast-cancer-phase-1","Predicting High Risk Breast Cancer - Phase 1","Predicting High Risk Breast Cancer-a Nightingale OS & AHLI data challenge","Every year, 40 million women get a mammogram; some go on to have an invasive biopsy to better examine a concerning area. Underneath these routine tests lies a deep—and disturbing—mystery. Since the 1990s, we have found far more ‘cancers’, which has in turn prompted vastly more surgical procedures and chemotherapy. But death rates from metastatic breast cancer have hardly changed. When a pathologist looks at a biopsy slide, she is looking for known signs of cancer-tubules, cells with atypical looking nuclei, evidence of rapid cell division. These features, first identified in 1928, still underlie critical decisions today-which women must receive urgent treatment with surgery and chemotherapy? And which can be prescribed “watchful waiting”, sparing them invasive procedures for cancers that would not harm them? There is already evidence that algorithms can predict which cancers will metastasize and harm patients on the basis of the biopsy image. Fascinatingly, these algorithms also h...","","https://app.nightingalescience.org/contests/3jmp2y128nxd","completed","15","","2022-06-01","2023-01-12","2023-08-22 17:07:00","2023-10-12 17:55:10"
@@ -214,55 +214,55 @@
 "213","dream-6-flowcap2-molecular-classification-of-acute-myeloid-leukemia-challenge","DREAM 6 - FlowCAP2 Molecular Classification of Acute Myeloid Leukemia Challenge","Diagnose acute myeloid leukemia from patient data using flow cytometry","Flow cytometry (FCM) has been widely used by immunologists and cancer biologists for more than 30 years as a biomedical research tool to distinguish different cell types in mixed populations, based on the expression of cellular markers. It has also become a widely used diagnostic tool for clinicians to identify abnormal cell populations associated with disease. In the last decade, advances in instrumentation and reagent technologies have enabled simultaneous single-cell measurement of tens of surface and intracellular markers, as well as tens of signaling molecules, positioning FCM to play an even bigger role in medicine and systems biology [1,2]. However, the rapid expansion of FCM applications has outpaced the functionality of traditional analysis tools used to interpret FCM data such that scientists are faced with the daunting prospect of manually identifying interesting cell populations in 20 dimensional data from a collection of millions of cells. For these reasons a reliable...","","https://www.synapse.org/#!Synapse:syn2887788/wiki/72178","completed","1","https://doi.org/10.1038/nmeth.2365","2011-06-01","2011-09-30","2023-09-12 21:28:19","2023-11-14 19:36:22"
 "214","dream-6-alternative-splicing-challenge","DREAM 6 - Alternative Splicing Challenge","Compare mRNA-seq methods on primate and rhino transcripts","The goal of the mRNA-seq alternative splicing challenge is to assess the accuracy of the reconstruction of alternatively spliced mRNA transcripts from Illumina short-read mRNA-seq. Reconstructed transcripts will be scored against Pacific Biosciences long-read mRNA-seq. The ensuing analysis of the transcriptomes from mandrill and rhinoceros fibroblasts and their derived induced pluripotent stem cells (iPSC), as well as the transcriptome for human Embrionic Stem Cells (hESC) is an opportunity to discover novel biology as well as investigate species-bias of different methods.","","https://www.synapse.org/#!Synapse:syn2817724/wiki/","completed","1","","2011-08-09","\N","2023-09-12 21:28:25","2023-10-12 17:54:50"
 "215","causalbench-challenge","CausalBench Challenge","Gene network inference from single-cell perturbation data","Mapping gene-gene interactions in cellular systems is a fundamental step in early-stage drug discovery that helps generate hypotheses on what molecular mechanisms may effectively be targeted by potential future medicines. In the CausalBench Challenge, we invite the machine-learning community to advance the state-of-the-art in deriving gene-gene networks from large-scale real-world perturbational single-cell datasets to improve our ability to glean causal insights into disease-relevant biology.","","https://www.gsk.ai/causalbench-challenge/","completed","16","https://doi.org/10.48550/arXiv.2308.15395","2023-03-01","2023-04-21","2023-09-12 21:28:25","2023-11-14 19:36:27"
-"216","iclr-computational-geometry-and-topology-challenge-2022","ICLR Computational Geometry & Topology Challenge 2022","Advancing computational geometry and topology with python","The purpose of this challenge is to foster reproducible research in geometric (deep) learning, by crowdsourcing the open-source implementation of learning algorithms on manifolds. Participants are asked to contribute code for a published/unpublished algorithm, following Scikit-Learn/Geomstats' or pytorch's APIs and computational primitives, benchmark it, and demonstrate its use in real-world scenarios.","","https://github.com/geomstats/challenge-iclr-2022","completed","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","Advancing computational geometry and topology with python","The purpose of this challenge is to push forward the fields of computational differential geometry and topology, by creating the best data analysis, computational method, or numerical experiment relying on state-of-the-art geometric and topological Python packages.","","https://github.com/geomstats/challenge-iclr-2021","completed","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"
+"216","iclr-computational-geometry-and-topology-challenge-2022","ICLR Computational Geometry & Topology Challenge 2022","Advancing computational geometry and topology with python","The purpose of this challenge is to foster reproducible research in geometric (deep) learning, by crowdsourcing the open-source implementation of learning algorithms on manifolds. Participants are asked to contribute code for a published/unpublished algorithm, following Scikit-Learn/Geomstats' or pytorch's APIs and computational primitives, benchmark it, and demonstrate its use in real-world scenarios.","","https://github.com/geomstats/challenge-iclr-2022","completed","\N","","\N","2022-04-04","2023-09-13 16:54:06","2023-10-19 23:28:44"
+"217","iclr-computational-geometry-and-topology-challenge-2021","ICLR Computational Geometry & Topology Challenge 2021","Advancing computational geometry and topology with python","The purpose of this challenge is to push forward the fields of computational differential geometry and topology, by creating the best data analysis, computational method, or numerical experiment relying on state-of-the-art geometric and topological Python packages.","","https://github.com/geomstats/challenge-iclr-2021","completed","\N","https://doi.org/10.48550/arXiv.2108.09810","\N","2021-05-02","2023-09-13 17:02:12","2023-10-19 23:28:44"
 "218","genedisco-challenge","GeneDisco Challenge","Exploring experimental design with active learning for genetics","The GeneDisco challenge is a machine learning community challenge for evaluating batch active learning algorithms for exploring the vast experimental design space in genetic perturbation experiments. Genetic perturbation experiments, using for example CRISPR technologies to perturb the genome, are a vital component of early-stage drug discovery, including target discovery and target validation. The GeneDisco challenge is organized in conjunction with the Machine Learning for Drug Discovery workshop at ICLR-22.","","https://www.gsk.ai/genedisco-challenge/","completed","16","https://doi.org/10.48550/arXiv.2110.11875","2022-01-31","2022-03-31","2023-09-13 17:20:30","2023-10-19 23:32:43"
 "219","hidden-treasures-warm-up","Hidden Treasures: Warm Up","Assess genome sequencing software accuracy with unknown variants","In the context of human genome sequencing, software pipelines typically involve a wide range of processing elements, including aligning sequencing reads to a reference genome and subsequently identifying variants (differences). One way of assessing the performance of such pipelines is by using well-characterized datasets such as Genome in a Bottle’s NA12878. However, because the existing NGS reference datasets are very limited and have been widely used to train/develop software pipelines, benchmarking of pipeline performance would ideally be done on samples with unknown variants.  This challenge will provide a unique opportunity for participants to investigate the accuracy of their pipelines by testing the ability to find in silico injected variants in FASTQ files from exome sequencing of reference cell lines. It will be a warm up for the community ahead of a more difficult in silico challenge to come in the fall. This challenge will provide users with a FASTQ file of a NA12878 se...","","https://precision.fda.gov/challenges/1","completed","6","","2017-07-17","2017-09-13","2023-09-13 23:31:39","2023-10-12 17:55:23"
-"220","data-management-and-graph-extraction-for-large-models-in-the-biomedical-space","Data management and graph extraction for large models in the biomedical space","Advancing biomedical knowledge graphs","This fall, CMU Libraries is hosting a hackathon in partnership with DNAnexus on the topic of data management and graph extraction for large models in the biomedical space. The hackathon will be held in person at CMU, October 19-21, 2023.  The hackathon is a collaborative, rather than competitive, event, with each team working on a dedicated part of the problem. The teams will be focused on the following topics-1) Knowledge graph-based validation for variant (genomic) assertions; 2) Continuous monitoring for RLHF and flexible infrastructure for layering assertions with rollback; 3) Flexible tokenization of complex data types; 4) Assertion tracking in large models; 5) Column headers for data harmonization.  The outputs are often published as preprints or on the F1000 hackathon channel.  Contact Ben Busby (bbusby@dnanexus.com) with any questions about the hackathon or serving as a team lead.","","https://library.cmu.edu/about/news/2023-08/hackathon-2023","completed","14","","2023-10-19","2023-10-21","2023-09-13 23:32:59","2023-11-14 19:36:32"
-"221","cagi2-asthma-twins","CAGI2: Asthma discordant monozygotic twins","Identify genetic differences between asthmatic and healthy twins","The dataset includes whole genomes of 8 pairs of discordant monozygotic twins (randomly numbered from 1 to 16) that is, in each pair identical twins one has asthma and one does not. In addition, RNA sequencing data for each individual is provided. One of the twins in each pair suffers from asthma while the other twin is healthy.","","https://genomeinterpretation.org/cagi2-asthma-twins.html","completed","14","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 18:11:42"
-"222","cagi4-bipolar","CAGI4: Bipolar disorder","Predicting bipolar disorder from exome data","Bipolar disorder (BD) is a serious mental illness characterized by recurrent episodes of manias and depression, which are syndromes of abnormal mood, thinking and behavior. It affects 1.0-4.5% of the population [1], and it is among the major causes of disability worldwide. This challenge involved the prediction of which of a set of individuals have been diagnosed with bipolar disorder, given exome data. 500 of the 1000 exome samples were provided for training.","","https://genomeinterpretation.org/cagi4-bipolar.html","completed","14","","\N","2016-04-04","2023-09-28 18:19:48","2023-09-28 18:25:17"
-"223","cagi3-brca","CAGI3: BRCA1 & BRCA2","Assess hereditary cancer risk via BRCA gene analysis","In normal cells, the BRCA1 and BRCA2 genes are involved in homologous recombination for double strand break repair and ensure the stability of a cell's genetic material. Mutations in these genes have been linked to development of breast and ovarian cancer. Myriad Genetics created the BRACAnalysis test in order to assess a woman’s risk of developing hereditary breast or ovarian cancer based on detection of mutations in the BRCA1 and BRCA2 genes. This test has become the standard of care in identification of individuals with hereditary breast and ovarian cancer (HBOC) syndrome. It is based on proprietary methods.","","https://genomeinterpretation.org/cagi3-brca.html","completed","14","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-19 23:32:48"
-"224","cagi2-breast-cancer-pkg","CAGI2: Breast cancer pharmacogenomics","Exploring CHEK2 as a candidate gene for cancer susceptibility","Cell-cycle-checkpoint kinase 2 (CHEK2; OMIM #604373) is a protein that plays an important role in the maintenance of genome integrity and in the regulation of the G2/M cell cycle checkpoint. CHEK2 has been shown to interact with other proteins involved in DNA repair processes such as BRCA1 and TP53. These findings render CHEK2 an 23 attractive candidate susceptibility gene for a variety of cancers.","","https://genomeinterpretation.org/cagi2-breast-cancer-pkg.html","completed","14","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 17:46:22"
-"225","cagi4-2eqtl","CAGI4: eQTL causal SNPs","Identify regulatory variants causing gene expression differences","Identifying the causal alleles responsible for variation in expression of human genes has been particularly difficult. This is an important problem, as genome-wide association studies (GWAS) suggest that much of the variation underlying common traits and diseases maps within regions of the genome that do not encode protein. A massively parallel reporter assay (MPRA) has been applied to thousands of single nucleotide polymorphisms (SNPs) and small insertion/deletion polymorphisms in linkage disequilibrium (LD) with cis-expression quantitative trait loci (eQTLs). The results identify variants showing differential expression between alleles. The challenge is to identify the regulatory sequences and the expression-modulating variants (emVars) underlying each eQTL and estimate their effects in the assay.","","https://genomeinterpretation.org/cagi4-2eqtl.html","completed","14","","\N","2016-04-04","2023-09-28 18:19:48","2023-09-29 3-58-33"
-"226","cagi1-cbs","CAGI1: CBS","Seeking to understand CBS enzyme function in cysteine production","CBS is a vitamin-dependent enzyme involved in cysteine biosynthesis. The human CBS requires two cofactors for function, vitamin B6 and heme. Homocystinuria due to CBS deficiency (OMIM #236200) is a recessive inborn error of sulfur amino acid metabolism.","","https://genomeinterpretation.org/cagi1-cbs.html","completed","14","","\N","2010-12-10","2023-09-28 18:19:48","2023-10-12 17:46:07"
-"227","cagi2-cbs","CAGI2: CBS","Developing treatment for homocystinuria caused by cbs deficiency","CBS is a vitamin-dependent enzyme involved in cysteine biosynthesis. The human CBS requires two cofactors for function, vitamin B6 and heme. Homocystinuria due to CBS deficiency (OMIM #236200) is a recessive inborn error of sulfur amino acid metabolism.","","https://genomeinterpretation.org/cagi2-cbs.html","completed","14","","\N","2011-10-06","2023-09-28 18:19:48","2023-11-15 22:46:32"
-"228","cagi1-chek2","CAGI1: CHEK2","Variants in the ATM & CHEK2 genes are associated with breast cancer","Predictors will be provided with 41 rare missense, nonsense, splicing, and indel variants in CHEK2.","","https://genomeinterpretation.org/cagi1-chek2.html","completed","14","","\N","2010-12-10","2023-09-28 18:19:48","2023-10-19 23:32:57"
-"229","cagi3-fch","CAGI3: FCH","Seeking to understand genetic basis of common hyperlipidemia disorder","Familial combined hyperlipidemia (FCH; OMIM 14380) the most prevalent hyperlipidemia, is a complex metabolic disorder characterized by variable occurrence of elevated low-density lipoprotein cholesterol (LDL-C) level and high triglycerides (TG)—a condition that is commonly associated with coronary artery disease (CAD).","","https://genomeinterpretation.org/cagi3-fch.html","completed","14","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-01 22:26:44"
-"230","cagi3-ha","CAGI3: HA","Raising HDL levels to reduce heart disease risk","Hypoalphalipoproteinemia (HA; OMIM #604091) is characterized by severely decreased serum high-density lipoprotein cholesterol (HDL-C) levels and low apolipoprotein A1 (APOA1). Low HDL-C is a risk factor for coronary artery disease.","","https://genomeinterpretation.org/cagi3-ha.html","completed","14","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-15 22:46:55"
-"231","cagi2-croshn-s","CAGI2: Crohn's disease","Seeking genes linked to Crohn's, an inflammatory bowel disease","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/cagi2-croshn-s.html","completed","14","","\N","2011-10-06","2023-09-28 18:19:48","2023-11-15 22:46:26"
-"232","cagi3-crohn-s","CAGI3: Crohn's disease","Understanding the genetics behind Crohn's disease","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/cagi3-crohn-s.html","completed","14","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-15 22:46:27"
-"233","cagi4-chron-s-exome","CAGI4: Crohn's exomes","Seeking to understand genetic basis of Crohn's bowel disease","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/cagi4-chron-s-exome.html","completed","14","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-15 22:46:23"
-"234","cagi4-hopkins","CAGI4: Hopkins clinical panel","Exonic sequences of 83 genes linked to 14 diseases analyzed","The Johns Hopkins challenge, provided by the Johns Hopkins DNA Diagnostic Laboratory (http://www.hopkinsmedicine.org/dnadiagnostic), comprised of exonic sequence for 83 genes associated with one of 14 disease classes, including 5 decoys","","https://genomeinterpretation.org/cagi4-hopkins.html","completed","14","","\N","2016-04-04","2023-09-28 18:19:48","2023-10-12 17:45:27"
-"235","cagi2-mouse-exomes","CAGI2: Mouse exomes","Predict causative variants from exome sequencing data","Predictors were given SNVs and indels found from exome sequencing. Causative variants had been identified for the L11Jus74 and Sofa phenotypes by the use of traditional breeding crosses,47 and the predictions were compared to these results, which were unpublished at the time of the CAGI submissions. The L11Jus74 phenotype is caused by two SNVs (chr11-102258914A> and chr11-77984176A>T), whereas a 15-nucleotide deletion in the Pfas gene is responsible for the Sofa phenotype. The predictions for Frg and Stn phenotypes could not be compared to experimental data, as the causative variants could not successfully be mapped by linkage","","https://genomeinterpretation.org/cagi2-mouse-exomes.html","completed","14","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 17:45:19"
-"236","cagi3-mrn-mre11","CAGI3: MRE11","Genomes are subject to constant threat by damaging agents that generate dna","Predict probability of pathogenicity (a number between 0 and 1) for individual rare variants of MRE11 and NBS1.","","https://genomeinterpretation.org/cagi3-mrn.html","completed","14","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-15 22:47:19"
-"237","cagi4-naglu","CAGI4: NAGLU","Predicting enzymatic activity of NAGLU mutants","NAGLU is a lysosomal glycohydrolyase. Deficiency of NAGLU causes the rare disorder Mucopolysaccharidosis IIIB or Sanfilippo B disease. Naturally occurring NAGLU mutants have been assayed for enzymatic activity in transfected cell lysates. The challenge is to predict the fractional activity of each mutant protein compared to the wild-type enzyme.","","https://genomeinterpretation.org/cagi4-naglu.html","completed","14","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-15 22:47:24"
-"238","cagi4-npm-alk","CAGI4: NPM: ALK","Predicting kinase activity of NPM-ALK fusion mutants","NPM-ALK is a fusion protein in which the ALK tyrosine kinase is constitutively activated, contributing to cancer. NPM-ALK constructs with mutations in the kinase domain have been assayed in extracts of transfected cells. The challenge is to predict the kinase activity and the Hsp90 binding affinity of the mutant proteins relative to the reference NPM-ALK fusion protein.","","https://genomeinterpretation.org/cagi4-npm-alk.html","completed","14","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-15 22:47:32"
-"239","cagi3-mrn-nbs1","CAGI3: NBS1","Predicting Pathogenicity of Rare MRE11 and NBS1 Variants","Predict probability of pathogenicity (a number between 0 and 1) for individual rare variants of MRE11 and NBS1.","","https://genomeinterpretation.org/cagi3-mrn.html","completed","14","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-16 18:38:55"
-"240","cagi3-p16","CAGI3: p16","Assessing p16 protein variants' effects on cell growth","Evaluate how different variants of p16 protein impact its ability to block cell proliferation.","","https://genomeinterpretation.org/cagi3-p16.html","completed","14","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-20 23:28:57"
-"241","cagi2-p53","CAGI2: p53 reactivation","Predictors are asked to submit predictions on the effect of the cancer rescue","The transcription factor p53 is a central tumor suppressor protein that controls DNA repair, cell cycle arrest, and apoptosis (programmed cell death). About half of human cancers have p53 mutations that inactivate p53. Over 250,000 US deaths yearly are due to tumors that express full-length p53 that has been inactivated by a single point mutation. For the past several years, the group of Rick Lathrop at University of California, Irvine, has been engaged in a complete functional census of p53 second-site suppressor (“cancer rescue”) mutations. These cancer rescue mutations are additional amino acids changes (to otherwise cancerous p53 mutations), which have been found to rescue p53 tumor suppressor function, reactivating otherwise inactive p53. These intragenic rescue mutations reactivate cancer mutant p53 in yeast and human cell assays by providing structural changes that compensate for the cancer mutation.","","https://genomeinterpretation.org/cagi2-p53.html","completed","14","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-20 23:28:58"
-"242","cagi1-pgp","CAGI1: PGP","CAGI challenges utilizing public genomic and phenotypic data resources","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/cagi1-pgp.html","completed","14","","\N","2010-12-10","2023-09-28 18:19:48","2023-09-27 21:05:22"
-"243","cagi2-pgp","CAGI2: PGP","CAGI challenges utilizing public genomic and phenotypic data resources","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/cagi2-pgp.html","completed","14","","\N","2011-10-06","2023-09-28 18:19:48","2023-09-27 21:05:23"
-"244","cagi3-pgp","CAGI3: PGP","CAGI challenges utilizing public genomic and phenotypic data resources","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/cagi3-pgp.html","completed","14","","\N","2013-04-25","2023-09-28 18:19:48","2023-09-27 21:05:23"
-"245","cagi4-pgp","CAGI4: PGP","CAGI challenges utilizing public genomic and phenotypic data resources","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/cagi4-pgp.html","completed","14","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-01 22:26:17"
-"246","cagi4-pyruvate-kinase","CAGI4: Pyruvate kinase","Predicting mutation impacts on pyruvate kinase activity and regulation","Pyruvate kinase catalyzes the last step in glycolysis and is regulated by allosteric effectors. Variants in the gene encoding the isozymes expressed in red blood cells and liver, including missense variants mapping near the effector binding sites, cause PK deficiency. A large set of single amino acid mutations in the liver enzyme has been assayed in E. coli extracts for the effect on allosteric regulation of enzyme activity. The challenge is to predict the impacts of mutations on enzyme activity and allosteric regulation.","","https://genomeinterpretation.org/cagi4-pyruvate-kinase.html","completed","14","","\N","2015-01-11","2023-09-28 18:19:48","2023-11-01 22:26:25"
-"247","cagi2-rad50","CAGI2: RAD50","Assessing RAD50 variants for breast cancer risk","RAD50 is a candidate intermediate-risk breast cancer susceptibility gene. The RAD50 data provided for CAGI challenge include a list of potentially interesting sequence variants observed from sequencing RAD50 gene in about 1,400 breast cancer cases and 1,200 ethnically matched controls. Variants in the list were observed between 1 and 20 times.","","https://genomeinterpretation.org/cagi2-rad50.html","completed","14","","\N","2011-10-06","2023-09-28 18:19:48","2023-11-15 22:47:40"
-"248","cagi2-risksnps","CAGI2: riskSNPs","Exploring molecular mechanisms linking SNPs to disease risk","The goal of this experiment is to explore current understanding of the molecular level mechanisms underlying associations between SNPs and disease risk, incorporating expertise in each of the known mechanism areas, and as far as possible assigning possible mechanisms for each association locus. The correct mechanisms are unknown, so there can be no ranking of accuracy-that is not the point of the experiment. Rather, the goal is to ascertain which mechanisms appear most relevant, how confidently they can be assigned, and what fraction of loci can currently be assigned plausible mechanisms.","","https://genomeinterpretation.org/cagi2-risksnps.html","completed","14","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-19 23:33:11"
-"249","cagi3-risksnps","CAGI3: riskSNPs","Exploring molecular mechanisms linking SNPs to disease risk","The goal of this experiment is to explore current understanding of the molecular level mechanisms underlying associations between SNPs and disease risk, incorporating expertise in each of the known mechanism areas, and as far as possible assigning possible mechanisms for each association locus. The correct mechanisms are unknown, so there can be no ranking of accuracy-that is not the point of the experiment. Rather, the goal is to ascertain which mechanisms appear most relevant, how confidently they can be assigned, and what fraction of loci can currently be assigned plausible mechanisms.","","https://genomeinterpretation.org/cagi3-risksnps.html","completed","14","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-19 23:33:13"
-"250","cagi2-nav1-5","CAGI2: SCN5A","Predict the effect of SCN5A mutants in cardiac electrophysiology","The cardiac action potential (AP) is the sum of a number of distinct ionic currents. It can be divided into five phases (phase 0‐4). From pacemaker cells of the SA node the initial depolarizing wave front will spread throughout the cardiomyocytes via gap junctions. If the depolarization is sufficient voltage‐dependent sodium channels (Nav1.5) are activated and allow Na+ influx. This results in a further depolarization of the membrane which will lead to opening of even more Nav channels. This positive feedback mechanism is seen as the rapid upstroke in the initial phase (phase 0) of the action potential. Nav1.5 is encoded by SCN5A and mutations in this gene have been associated with various diseases such as Atrial fibrillation, Long QT syndrome, Cardiac Conduction Defect, Sick Sinus Disease, and Brugada Syndrome (BrS).","","https://genomeinterpretation.org/cagi2-nav1.5.html","completed","14","","\N","2011-10-06","2023-09-28 18:19:48","2023-11-14 19:36:54"
-"251","cagi2-mr-1","CAGI2: Shewanella oneidensis strain MR-1","How MR-1 affect the fitness of that gene in a given condition","Predictors are asked to submit predictions on how insertions in the given gene of MR-1 affect the fitness of that gene in a given condition (stressor).","","https://genomeinterpretation.org/cagi2-mr-1.html","completed","14","","\N","2011-10-06","2023-09-28 18:19:55","2023-11-14 19:37:40"
-"252","cagi3-mr-1","CAGI3: Shewanella oneidensis strain MR-1","How MR-1 affect the fitness of that gene in a given condition","Predictors are asked to submit predictions on how insertions in the given gene of MR-1 affect the fitness of that gene in a given condition (stressor).","","https://genomeinterpretation.org/cagi3-mr-1.html","completed","14","","\N","2013-04-25","2023-09-28 18:20:01","2023-11-14 19:37:47"
-"253","cagi4-sickkids","CAGI4: SickKids","Match genome sequence to clinical descriptions","Realizing the promise of precision medicine will require developing methods for interpreting genome sequence data to infer individuals’ phenotypic traits and predispositions to disease. This challenge involves 25 children with suspected genetic disorders who were referred for clinical genome sequencing. Predictors are given their genome sequences and their clinical phenotypic descriptions, as provided to the diagnostic laboratory, and asked to predict which genome corresponds to which clinical description. Additionally, identify the diagnostic variants underlying the predictions. Optionally, identify predictive secondary variants conferring high risk of other diseases whose phenotypes are not reported in the clinical descriptions.","","https://genomeinterpretation.org/cagi4-sickkids.html","completed","14","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-14 19:38:30"
-"254","cagi4-sumo-ligase","CAGI4: SUMO ligase","Predict effect of the variants on SUMO ligase","SUMO ligase identifies target proteins and covalently attaches SUMO to them, thereby modulating the functions of hundreds of proteins including proteins implicated in cancer, neurodegeneration, and other diseases. A large library of missense mutations in human SUMO ligase has been assessed for competitive growth in a high-throughput yeast-based complementation assay. The challenge is to predict the effect of mutations on function, as measured by the change in fractional representation of each mutant SUMO ligase clone, relative to wild-type clones, in a competitive yeast growth assay.","","https://genomeinterpretation.org/cagi4-sumo-ligase.html","completed","14","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-14 19:38:48"
-"255","cagi3-splicing","CAGI3: TP53 splicing","Which TP53 mutations potentially contribute to cancer","The function of exonic splicing regulatory elements can be undermined by DNA sequence variation and in some cases can contribute to pathogenesis. Thousands of disease-causing mutations disrupt exonic splicing regulatory elements. These data suggest that >25 percent of missense mutations may impact pre-mRNA splicing rather than mRNA translation. Using minigene constructs derived from a fragment of the TP53 gene, we have experimentally determined if each mutation influences splicing fidelity in HEK293T cells. We hope that CAGI participants will be able to predict the outcome of our experiments. A long-term goal will be the computational prioritization of disease-causing mutations prior to experimental validation. This contribution is expected to have major impacts in understanding the pathogenic basis of disease-causing mutations.","","https://genomeinterpretation.org/cagi3-splicing.html","completed","14","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-14 19:39:00"
-"256","cagi4-warfarin","CAGI4: Warfarin exomes","Predict the therapeutic doses of warfarin","With over 33 million prescriptions in 2011, warfarin is the most commonly used anticoagulant for preventing thromboembolic events. Warfarin has a twenty-fold inter-individual dose variability and a narrow therapeutic index, and it is responsible for a third of adverse drug event hospitalizations in older Americans [2]. Alternatives to warfarin, such as direct thrombin inhibitors and factor Xa inhibitors, are now available. However, these are more expensive, irreversible, and may cause a higher rate of acute coronary events compared to warfarin [3,4]. Thus, warfarin remains a mainstay of anticoagulant therapy, and better methods of dosing warfarin will lead to fewer adverse events due to overcoagulation.","","https://genomeinterpretation.org/cagi4-warfarin.html","completed","14","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-14 19:39:08"
+"220","data-management-and-graph-extraction-for-large-models-in-the-biomedical-space","Data management and graph extraction for large models in the biomedical space","Advancing biomedical knowledge graphs","This fall, CMU Libraries is hosting a hackathon in partnership with DNAnexus on the topic of data management and graph extraction for large models in the biomedical space. The hackathon will be held in person at CMU, October 19-21, 2023.  The hackathon is a collaborative, rather than competitive, event, with each team working on a dedicated part of the problem. The teams will be focused on the following topics-1) Knowledge graph-based validation for variant (genomic) assertions; 2) Continuous monitoring for RLHF and flexible infrastructure for layering assertions with rollback; 3) Flexible tokenization of complex data types; 4) Assertion tracking in large models; 5) Column headers for data harmonization.  The outputs are often published as preprints or on the F1000 hackathon channel.  Contact Ben Busby (bbusby@dnanexus.com) with any questions about the hackathon or serving as a team lead.","","https://library.cmu.edu/about/news/2023-08/hackathon-2023","completed","\N","","2023-10-19","2023-10-21","2023-09-13 23:32:59","2023-11-14 19:36:32"
+"221","cagi2-asthma-twins","CAGI2: Asthma discordant monozygotic twins","Identify genetic differences between asthmatic and healthy twins","The dataset includes whole genomes of 8 pairs of discordant monozygotic twins (randomly numbered from 1 to 16) that is, in each pair identical twins one has asthma and one does not. In addition, RNA sequencing data for each individual is provided. One of the twins in each pair suffers from asthma while the other twin is healthy.","","https://genomeinterpretation.org/cagi2-asthma-twins.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 18:11:42"
+"222","cagi4-bipolar","CAGI4: Bipolar disorder","Predicting bipolar disorder from exome data","Bipolar disorder (BD) is a serious mental illness characterized by recurrent episodes of manias and depression, which are syndromes of abnormal mood, thinking and behavior. It affects 1.0-4.5% of the population [1], and it is among the major causes of disability worldwide. This challenge involved the prediction of which of a set of individuals have been diagnosed with bipolar disorder, given exome data. 500 of the 1000 exome samples were provided for training.","","https://genomeinterpretation.org/cagi4-bipolar.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-09-28 18:25:17"
+"223","cagi3-brca","CAGI3: BRCA1 & BRCA2","Assess hereditary cancer risk via BRCA gene analysis","In normal cells, the BRCA1 and BRCA2 genes are involved in homologous recombination for double strand break repair and ensure the stability of a cell's genetic material. Mutations in these genes have been linked to development of breast and ovarian cancer. Myriad Genetics created the BRACAnalysis test in order to assess a woman’s risk of developing hereditary breast or ovarian cancer based on detection of mutations in the BRCA1 and BRCA2 genes. This test has become the standard of care in identification of individuals with hereditary breast and ovarian cancer (HBOC) syndrome. It is based on proprietary methods.","","https://genomeinterpretation.org/cagi3-brca.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-19 23:32:48"
+"224","cagi2-breast-cancer-pkg","CAGI2: Breast cancer pharmacogenomics","Exploring CHEK2 as a candidate gene for cancer susceptibility","Cell-cycle-checkpoint kinase 2 (CHEK2; OMIM #604373) is a protein that plays an important role in the maintenance of genome integrity and in the regulation of the G2/M cell cycle checkpoint. CHEK2 has been shown to interact with other proteins involved in DNA repair processes such as BRCA1 and TP53. These findings render CHEK2 an 23 attractive candidate susceptibility gene for a variety of cancers.","","https://genomeinterpretation.org/cagi2-breast-cancer-pkg.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 17:46:22"
+"225","cagi4-2eqtl","CAGI4: eQTL causal SNPs","Identify regulatory variants causing gene expression differences","Identifying the causal alleles responsible for variation in expression of human genes has been particularly difficult. This is an important problem, as genome-wide association studies (GWAS) suggest that much of the variation underlying common traits and diseases maps within regions of the genome that do not encode protein. A massively parallel reporter assay (MPRA) has been applied to thousands of single nucleotide polymorphisms (SNPs) and small insertion/deletion polymorphisms in linkage disequilibrium (LD) with cis-expression quantitative trait loci (eQTLs). The results identify variants showing differential expression between alleles. The challenge is to identify the regulatory sequences and the expression-modulating variants (emVars) underlying each eQTL and estimate their effects in the assay.","","https://genomeinterpretation.org/cagi4-2eqtl.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-09-29 3-58-33"
+"226","cagi1-cbs","CAGI1: CBS","Seeking to understand CBS enzyme function in cysteine production","CBS is a vitamin-dependent enzyme involved in cysteine biosynthesis. The human CBS requires two cofactors for function, vitamin B6 and heme. Homocystinuria due to CBS deficiency (OMIM #236200) is a recessive inborn error of sulfur amino acid metabolism.","","https://genomeinterpretation.org/cagi1-cbs.html","completed","\N","","\N","2010-12-10","2023-09-28 18:19:48","2023-10-12 17:46:07"
+"227","cagi2-cbs","CAGI2: CBS","Developing treatment for homocystinuria caused by cbs deficiency","CBS is a vitamin-dependent enzyme involved in cysteine biosynthesis. The human CBS requires two cofactors for function, vitamin B6 and heme. Homocystinuria due to CBS deficiency (OMIM #236200) is a recessive inborn error of sulfur amino acid metabolism.","","https://genomeinterpretation.org/cagi2-cbs.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-11-15 22:46:32"
+"228","cagi1-chek2","CAGI1: CHEK2","Variants in the ATM & CHEK2 genes are associated with breast cancer","Predictors will be provided with 41 rare missense, nonsense, splicing, and indel variants in CHEK2.","","https://genomeinterpretation.org/cagi1-chek2.html","completed","\N","","\N","2010-12-10","2023-09-28 18:19:48","2023-10-19 23:32:57"
+"229","cagi3-fch","CAGI3: FCH","Seeking to understand genetic basis of common hyperlipidemia disorder","Familial combined hyperlipidemia (FCH; OMIM 14380) the most prevalent hyperlipidemia, is a complex metabolic disorder characterized by variable occurrence of elevated low-density lipoprotein cholesterol (LDL-C) level and high triglycerides (TG)—a condition that is commonly associated with coronary artery disease (CAD).","","https://genomeinterpretation.org/cagi3-fch.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-01 22:26:44"
+"230","cagi3-ha","CAGI3: HA","Raising HDL levels to reduce heart disease risk","Hypoalphalipoproteinemia (HA; OMIM #604091) is characterized by severely decreased serum high-density lipoprotein cholesterol (HDL-C) levels and low apolipoprotein A1 (APOA1). Low HDL-C is a risk factor for coronary artery disease.","","https://genomeinterpretation.org/cagi3-ha.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-15 22:46:55"
+"231","cagi2-croshn-s","CAGI2: Crohn's disease","Seeking genes linked to Crohn's, an inflammatory bowel disease","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/cagi2-croshn-s.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-11-15 22:46:26"
+"232","cagi3-crohn-s","CAGI3: Crohn's disease","Understanding the genetics behind Crohn's disease","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/cagi3-crohn-s.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-15 22:46:27"
+"233","cagi4-chron-s-exome","CAGI4: Crohn's exomes","Seeking to understand genetic basis of Crohn's bowel disease","Crohn's disease (CD [MIM 266600]) a form of inflammatory bowel disease (IBD) is a complex genetic disorder characterized by chronic relapsing inflammation that can involve any part of the gastrointestinal tract.","","https://genomeinterpretation.org/cagi4-chron-s-exome.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-15 22:46:23"
+"234","cagi4-hopkins","CAGI4: Hopkins clinical panel","Exonic sequences of 83 genes linked to 14 diseases analyzed","The Johns Hopkins challenge, provided by the Johns Hopkins DNA Diagnostic Laboratory (http://www.hopkinsmedicine.org/dnadiagnostic), comprised of exonic sequence for 83 genes associated with one of 14 disease classes, including 5 decoys","","https://genomeinterpretation.org/cagi4-hopkins.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-10-12 17:45:27"
+"235","cagi2-mouse-exomes","CAGI2: Mouse exomes","Predict causative variants from exome sequencing data","Predictors were given SNVs and indels found from exome sequencing. Causative variants had been identified for the L11Jus74 and Sofa phenotypes by the use of traditional breeding crosses,47 and the predictions were compared to these results, which were unpublished at the time of the CAGI submissions. The L11Jus74 phenotype is caused by two SNVs (chr11-102258914A> and chr11-77984176A>T), whereas a 15-nucleotide deletion in the Pfas gene is responsible for the Sofa phenotype. The predictions for Frg and Stn phenotypes could not be compared to experimental data, as the causative variants could not successfully be mapped by linkage","","https://genomeinterpretation.org/cagi2-mouse-exomes.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-12 17:45:19"
+"236","cagi3-mrn-mre11","CAGI3: MRE11","Genomes are subject to constant threat by damaging agents that generate dna","Predict probability of pathogenicity (a number between 0 and 1) for individual rare variants of MRE11 and NBS1.","","https://genomeinterpretation.org/cagi3-mrn.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-15 22:47:19"
+"237","cagi4-naglu","CAGI4: NAGLU","Predicting enzymatic activity of NAGLU mutants","NAGLU is a lysosomal glycohydrolyase. Deficiency of NAGLU causes the rare disorder Mucopolysaccharidosis IIIB or Sanfilippo B disease. Naturally occurring NAGLU mutants have been assayed for enzymatic activity in transfected cell lysates. The challenge is to predict the fractional activity of each mutant protein compared to the wild-type enzyme.","","https://genomeinterpretation.org/cagi4-naglu.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-15 22:47:24"
+"238","cagi4-npm-alk","CAGI4: NPM: ALK","Predicting kinase activity of NPM-ALK fusion mutants","NPM-ALK is a fusion protein in which the ALK tyrosine kinase is constitutively activated, contributing to cancer. NPM-ALK constructs with mutations in the kinase domain have been assayed in extracts of transfected cells. The challenge is to predict the kinase activity and the Hsp90 binding affinity of the mutant proteins relative to the reference NPM-ALK fusion protein.","","https://genomeinterpretation.org/cagi4-npm-alk.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-15 22:47:32"
+"239","cagi3-mrn-nbs1","CAGI3: NBS1","Predicting Pathogenicity of Rare MRE11 and NBS1 Variants","Predict probability of pathogenicity (a number between 0 and 1) for individual rare variants of MRE11 and NBS1.","","https://genomeinterpretation.org/cagi3-mrn.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-16 18:38:55"
+"240","cagi3-p16","CAGI3: p16","Assessing p16 protein variants' effects on cell growth","Evaluate how different variants of p16 protein impact its ability to block cell proliferation.","","https://genomeinterpretation.org/cagi3-p16.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-20 23:28:57"
+"241","cagi2-p53","CAGI2: p53 reactivation","Predictors are asked to submit predictions on the effect of the cancer rescue","The transcription factor p53 is a central tumor suppressor protein that controls DNA repair, cell cycle arrest, and apoptosis (programmed cell death). About half of human cancers have p53 mutations that inactivate p53. Over 250,000 US deaths yearly are due to tumors that express full-length p53 that has been inactivated by a single point mutation. For the past several years, the group of Rick Lathrop at University of California, Irvine, has been engaged in a complete functional census of p53 second-site suppressor (“cancer rescue”) mutations. These cancer rescue mutations are additional amino acids changes (to otherwise cancerous p53 mutations), which have been found to rescue p53 tumor suppressor function, reactivating otherwise inactive p53. These intragenic rescue mutations reactivate cancer mutant p53 in yeast and human cell assays by providing structural changes that compensate for the cancer mutation.","","https://genomeinterpretation.org/cagi2-p53.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-20 23:28:58"
+"242","cagi1-pgp","CAGI1: PGP","CAGI challenges utilizing public genomic and phenotypic data resources","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/cagi1-pgp.html","completed","\N","","\N","2010-12-10","2023-09-28 18:19:48","2023-09-27 21:05:22"
+"243","cagi2-pgp","CAGI2: PGP","CAGI challenges utilizing public genomic and phenotypic data resources","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/cagi2-pgp.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-09-27 21:05:23"
+"244","cagi3-pgp","CAGI3: PGP","CAGI challenges utilizing public genomic and phenotypic data resources","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/cagi3-pgp.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-09-27 21:05:23"
+"245","cagi4-pgp","CAGI4: PGP","CAGI challenges utilizing public genomic and phenotypic data resources","Participants in the project make their full sequence and phenotypic profile data publicly available. The four CAGI challenges were based on prerelease samples from this resource.","","https://genomeinterpretation.org/cagi4-pgp.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-01 22:26:17"
+"246","cagi4-pyruvate-kinase","CAGI4: Pyruvate kinase","Predicting mutation impacts on pyruvate kinase activity and regulation","Pyruvate kinase catalyzes the last step in glycolysis and is regulated by allosteric effectors. Variants in the gene encoding the isozymes expressed in red blood cells and liver, including missense variants mapping near the effector binding sites, cause PK deficiency. A large set of single amino acid mutations in the liver enzyme has been assayed in E. coli extracts for the effect on allosteric regulation of enzyme activity. The challenge is to predict the impacts of mutations on enzyme activity and allosteric regulation.","","https://genomeinterpretation.org/cagi4-pyruvate-kinase.html","completed","\N","","\N","2015-01-11","2023-09-28 18:19:48","2023-11-01 22:26:25"
+"247","cagi2-rad50","CAGI2: RAD50","Assessing RAD50 variants for breast cancer risk","RAD50 is a candidate intermediate-risk breast cancer susceptibility gene. The RAD50 data provided for CAGI challenge include a list of potentially interesting sequence variants observed from sequencing RAD50 gene in about 1,400 breast cancer cases and 1,200 ethnically matched controls. Variants in the list were observed between 1 and 20 times.","","https://genomeinterpretation.org/cagi2-rad50.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-11-15 22:47:40"
+"248","cagi2-risksnps","CAGI2: riskSNPs","Exploring molecular mechanisms linking SNPs to disease risk","The goal of this experiment is to explore current understanding of the molecular level mechanisms underlying associations between SNPs and disease risk, incorporating expertise in each of the known mechanism areas, and as far as possible assigning possible mechanisms for each association locus. The correct mechanisms are unknown, so there can be no ranking of accuracy-that is not the point of the experiment. Rather, the goal is to ascertain which mechanisms appear most relevant, how confidently they can be assigned, and what fraction of loci can currently be assigned plausible mechanisms.","","https://genomeinterpretation.org/cagi2-risksnps.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-19 23:33:11"
+"249","cagi3-risksnps","CAGI3: riskSNPs","Exploring molecular mechanisms linking SNPs to disease risk","The goal of this experiment is to explore current understanding of the molecular level mechanisms underlying associations between SNPs and disease risk, incorporating expertise in each of the known mechanism areas, and as far as possible assigning possible mechanisms for each association locus. The correct mechanisms are unknown, so there can be no ranking of accuracy-that is not the point of the experiment. Rather, the goal is to ascertain which mechanisms appear most relevant, how confidently they can be assigned, and what fraction of loci can currently be assigned plausible mechanisms.","","https://genomeinterpretation.org/cagi3-risksnps.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-10-19 23:33:13"
+"250","cagi2-nav1-5","CAGI2: SCN5A","Predict the effect of SCN5A mutants in cardiac electrophysiology","The cardiac action potential (AP) is the sum of a number of distinct ionic currents. It can be divided into five phases (phase 0‐4). From pacemaker cells of the SA node the initial depolarizing wave front will spread throughout the cardiomyocytes via gap junctions. If the depolarization is sufficient voltage‐dependent sodium channels (Nav1.5) are activated and allow Na+ influx. This results in a further depolarization of the membrane which will lead to opening of even more Nav channels. This positive feedback mechanism is seen as the rapid upstroke in the initial phase (phase 0) of the action potential. Nav1.5 is encoded by SCN5A and mutations in this gene have been associated with various diseases such as Atrial fibrillation, Long QT syndrome, Cardiac Conduction Defect, Sick Sinus Disease, and Brugada Syndrome (BrS).","","https://genomeinterpretation.org/cagi2-nav1.5.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-11-14 19:36:54"
+"251","cagi2-mr-1","CAGI2: Shewanella oneidensis strain MR-1","How MR-1 affect the fitness of that gene in a given condition","Predictors are asked to submit predictions on how insertions in the given gene of MR-1 affect the fitness of that gene in a given condition (stressor).","","https://genomeinterpretation.org/cagi2-mr-1.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:55","2023-11-14 19:37:40"
+"252","cagi3-mr-1","CAGI3: Shewanella oneidensis strain MR-1","How MR-1 affect the fitness of that gene in a given condition","Predictors are asked to submit predictions on how insertions in the given gene of MR-1 affect the fitness of that gene in a given condition (stressor).","","https://genomeinterpretation.org/cagi3-mr-1.html","completed","\N","","\N","2013-04-25","2023-09-28 18:20:01","2023-11-14 19:37:47"
+"253","cagi4-sickkids","CAGI4: SickKids","Match genome sequence to clinical descriptions","Realizing the promise of precision medicine will require developing methods for interpreting genome sequence data to infer individuals’ phenotypic traits and predispositions to disease. This challenge involves 25 children with suspected genetic disorders who were referred for clinical genome sequencing. Predictors are given their genome sequences and their clinical phenotypic descriptions, as provided to the diagnostic laboratory, and asked to predict which genome corresponds to which clinical description. Additionally, identify the diagnostic variants underlying the predictions. Optionally, identify predictive secondary variants conferring high risk of other diseases whose phenotypes are not reported in the clinical descriptions.","","https://genomeinterpretation.org/cagi4-sickkids.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-14 19:38:30"
+"254","cagi4-sumo-ligase","CAGI4: SUMO ligase","Predict effect of the variants on SUMO ligase","SUMO ligase identifies target proteins and covalently attaches SUMO to them, thereby modulating the functions of hundreds of proteins including proteins implicated in cancer, neurodegeneration, and other diseases. A large library of missense mutations in human SUMO ligase has been assessed for competitive growth in a high-throughput yeast-based complementation assay. The challenge is to predict the effect of mutations on function, as measured by the change in fractional representation of each mutant SUMO ligase clone, relative to wild-type clones, in a competitive yeast growth assay.","","https://genomeinterpretation.org/cagi4-sumo-ligase.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-14 19:38:48"
+"255","cagi3-splicing","CAGI3: TP53 splicing","Which TP53 mutations potentially contribute to cancer","The function of exonic splicing regulatory elements can be undermined by DNA sequence variation and in some cases can contribute to pathogenesis. Thousands of disease-causing mutations disrupt exonic splicing regulatory elements. These data suggest that >25 percent of missense mutations may impact pre-mRNA splicing rather than mRNA translation. Using minigene constructs derived from a fragment of the TP53 gene, we have experimentally determined if each mutation influences splicing fidelity in HEK293T cells. We hope that CAGI participants will be able to predict the outcome of our experiments. A long-term goal will be the computational prioritization of disease-causing mutations prior to experimental validation. This contribution is expected to have major impacts in understanding the pathogenic basis of disease-causing mutations.","","https://genomeinterpretation.org/cagi3-splicing.html","completed","\N","","\N","2013-04-25","2023-09-28 18:19:48","2023-11-14 19:39:00"
+"256","cagi4-warfarin","CAGI4: Warfarin exomes","Predict the therapeutic doses of warfarin","With over 33 million prescriptions in 2011, warfarin is the most commonly used anticoagulant for preventing thromboembolic events. Warfarin has a twenty-fold inter-individual dose variability and a narrow therapeutic index, and it is responsible for a third of adverse drug event hospitalizations in older Americans [2]. Alternatives to warfarin, such as direct thrombin inhibitors and factor Xa inhibitors, are now available. However, these are more expensive, irreversible, and may cause a higher rate of acute coronary events compared to warfarin [3,4]. Thus, warfarin remains a mainstay of anticoagulant therapy, and better methods of dosing warfarin will lead to fewer adverse events due to overcoagulation.","","https://genomeinterpretation.org/cagi4-warfarin.html","completed","\N","","\N","2016-04-04","2023-09-28 18:19:48","2023-11-14 19:39:08"
 "257","cagi6-calmodulin","CAGI6: Calmodulin","Predict competitive growth score of different Calmodulin variants","Calmodulin (CaM) is a ubiquitous calcium (Ca2+) sensor protein interacting with more than 200 molecular partners, thereby regulating a variety of biological processes. Missense point mutations in the genes encoding CaM have been associated with ventricular tachycardia and sudden cardiac death. A library encompassing up to 17 point mutations was assessed by far-UV circular dichroism (CD) by measuring melting temperature (Tm) and percentage of unfolding (%unfold) upon thermal denaturation at pH and salt concentration that mimic the physiological conditions. The challenge is to predict- the Tm and %unfold values for isolated CaM variants under Ca2+-saturating conditions (Ca2+-CaM) and in the Ca2+-free (apo) state;  whether the point mutation stabilizes or destabilizes the protein (based on Tm and %unfold).","","https://genomeinterpretation.org/cagi6-cam.html","completed","1","","\N","2021-12-31","2023-09-28 18:19:48","2023-11-15 22:47:52"
-"258","cagi2-splicing","CAGI2: splicing","Compare exons to understand the mechanisms underlying pre-mRNA splicing errors","Accurate precursor mRNA (pre-mRNA) splicing is required for the expression of protein coding genes from the human genome. In this process, intervening sequences (introns) are removed from pre-mRNA and coding/regulatory sequences (exons) are ligated together generating a mature mRNA. A large ribonucleoprotein machine called the spliceosome assembles de novo upon every nascent intron and catalyzes the chemical steps of splicing.","","https://genomeinterpretation.org/cagi2-splicing.html","completed","14","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-18 15:32:55"
+"258","cagi2-splicing","CAGI2: splicing","Compare exons to understand the mechanisms underlying pre-mRNA splicing errors","Accurate precursor mRNA (pre-mRNA) splicing is required for the expression of protein coding genes from the human genome. In this process, intervening sequences (introns) are removed from pre-mRNA and coding/regulatory sequences (exons) are ligated together generating a mature mRNA. A large ribonucleoprotein machine called the spliceosome assembles de novo upon every nascent intron and catalyzes the chemical steps of splicing.","","https://genomeinterpretation.org/cagi2-splicing.html","completed","\N","","\N","2011-10-06","2023-09-28 18:19:48","2023-10-18 15:32:55"
 "259","cagi6-lc-arsa","CAGI6: ARSA","Predict the effect of naturally occurring missense mutations","Metachromatic Leukodystrophy (MLD) is an autosomal recessive, lysosomal-storage disease caused by mutations in Arylsulfatase A (ARSA) and toxic accumulation of sulfatide substrate. Genome sequencing has revealed hundreds of protein-altering, ARSA missense variants, but the functional effect of most variants remains unknown. ARSA enzyme activity using a high-throughput cellular assay was measured for a large set of variants of known significance and variants of unknown significance. The challenge is to predict the fractional enzyme activity of each mutant protein compared to the wildtype protein.","","https://genomeinterpretation.org/cagi6-lc-arsa.html","completed","1","","\N","2022-11-16","2023-09-28 18:20:23","2023-11-14 19:39:42"
 "260","predict-hits-for-the-wdr-domain-of-lrrk2","CACHE1: Predict Hits for The WDR Domain of LRRK2","Finding ligands targeting the central cavity of the WDR domain of LRRK2","The first CACHE Challenge target is LRRK2, the most commonly mutated gene in familial Parkinson's Disease.  Participants are asked to find hits for the WD40 repeat (WDR) domain of LRRK2. Read more under Details below.","","https://cache-challenge.org/challenges/predict-hits-for-the-wdr-domain-of-lrrk2","completed","17","","2021-12-01","2022-01-31","2023-09-27 19:01:55","2023-11-14 19:39:53"
 "261","finding-ligands-targeting-the-conserved-rna-binding-site-of-sars-cov-2-nsp13","CACHE2: Finding Ligands Targeting The Conserved RNA Binding Site of SARS-CoV-2 NSP13","Target the NSP13 helicase of SARS-CoV-2","Predicted compounds will be procured and tested at CACHE using both enzymatic and binding assays","","https://cache-challenge.org/challenges/finding-ligands-targeting-the-conserved-rna-binding-site-of-sars-cov-2-nsp13","completed","17","","2022-06-22","2022-09-04","2023-09-27 19:02:43","2023-11-14 19:41:01"
 "262","finding-ligands-targeting-the-macrodomain-of-sars-cov-2-nsp3","CACHE3: Finding ligands targeting the macrodomain of SARS-CoV-2 Nsp3","Study the macrodomain of SARS-CoV-2 Nsp3 for potential therapeutic applications","To predict ligands that bind to the ADPr site of SARS-CoV-2 Nsp3 macrodomain (Mac1).","","https://cache-challenge.org/challenges/finding-ligands-targeting-the-macrodomain-of-sars-cov-2-nsp3","completed","17","","2022-11-02","2023-01-01","2023-09-27 19:03:13","2023-10-16 19:01:19"
 "263","finding-ligands-targeting-the-tkb-domain-of-cblb","CACHE4: Finding ligands targeting the TKB domain of CBLB","Investigate the TKB domain of CBLB to discover novel compounds for treatment","Predict compounds that bind to the closed conformation of the CBLB TKB domain with novel chemical templates and KD below 30 micromolar.","","https://cache-challenge.org/challenges/finding-ligands-targeting-the-tkb-domain-of-cblb","completed","17","","2023-03-09","2023-05-09","2023-09-27 19:03:14","2023-10-16 19:01:22"
-"264","rare-disease-ai-hackathon","Rare Disease AI Hackathon","Advance rare disease diagnosis using artificial intelligence (AI) models","Bring AI and medical experts together to build open source models for rare diseases. Create zero-barrier access to rare disease expertise for patients, researchers and physicians. Use AI to Uncover novel links between rare diseases. Establish validation methods for medical AI models. Jumpstart an open source community for rare disease AI models. Launch models for Beta testing on Hypophosphatasia.ai and EhlersDanlos.ai.","","https://www.rarediseaseaihackathon.org/","active","14","","2023-09-30","2024-01-15","2023-09-27 19:10:40","2023-11-11 07:01:13"
+"264","rare-disease-ai-hackathon","Rare Disease AI Hackathon","Advance rare disease diagnosis using artificial intelligence (AI) models","Bring AI and medical experts together to build open source models for rare diseases. Create zero-barrier access to rare disease expertise for patients, researchers and physicians. Use AI to Uncover novel links between rare diseases. Establish validation methods for medical AI models. Jumpstart an open source community for rare disease AI models. Launch models for Beta testing on Hypophosphatasia.ai and EhlersDanlos.ai.","","https://www.rarediseaseaihackathon.org/","active","\N","","2023-09-30","2024-01-15","2023-09-27 19:10:40","2023-11-11 07:01:13"
 "265","cometh-benchmark","COMETH Benchmark","Quantify tumor heterogeneity","Successful treatment of cancer is still a challenge and this is partly due to a wide heterogeneity of cancer composition across patient population. Unfortunately, accounting for such heterogeneity is very difficult. Clinical evaluation of tumor heterogeneity often requires the expertise of anatomical pathologists and radiologists.This benchmark is dedicated to the quantification of intra-tumor heterogeneity using appropriate statistical methods on cancer omics data.In particular, it focuses on estimating cell types and proportion in biological samples based on methylation and methylome data sets. The goal is to explore various statistical methods for source separation/deconvolution analysis (Non-negative Matrix Factorization, Surrogate Variable Analysis, Principal component Analysis, Latent Factor Models, ...) using both RNA-seq and methylome data.","","https://www.codabench.org/competitions/218/","completed","10","","2020-06-14","2020-12-29","2023-09-28 23:25:52","2023-11-14 19:41:05"
 "266","the-miccai-2014-machine-learning-challenge","The MICCAI 2014 Machine Learning Challenge","Predict binary and continuous phenotypes from Structural Brain MRI","Machine learning tools are increasingly applied to brain MRI scans for predicting individual-level clinical phenotypes. Despite methodological advancements, benchmark studies with standardized datasets are scarce, hindering tool validation and comparison. The MICCAI 2014 Machine Learning Challenge (MLC) addresses this gap, utilizing four large-scale (N > 70) brain MRI datasets with clinically relevant phenotypes. The aim is to showcase the current state of neuroimage-based prediction, drawing machine-learning practitioners to MICCAI and medical image computing. MICCAI 2014 MLC complements the main conference, the Machine Learning in Medical Imaging Workshop, and the CADDementia challenge focused on Alzheimer's diagnosis from brain MR scans.","","https://competitions.codalab.org/competitions/1471","completed","9","","2014-04-16","2014-06-14","2023-09-28 23:36:12","2023-11-14 19:41:17"
 "267","cagi6-annotate-all-missense","CAGI6: Annotate All Missense","Predict the functional effect of every coding SNV in the human genome","dbNSFP currently describes 81,782,923 possible protein-altering variants in the human genome. The challenge is to predict the functional effect of every such variant. For the vast majority of these missense and nonsense variants, the functional impact is not currently known, but experimental and clinical evidence is accruing rapidly. Rather than drawing upon a single discrete dataset as typical with CAGI, predictions will be assessed by comparing with experimental or clinical annotations made available after the prediction submission date, on an ongoing basis. If predictors assent, predictions will also be incorporated into dbNSFP.","","https://genomeinterpretation.org/cagi6-annotate-all-missense.html","completed","1","","2021-06-01","2021-10-11","2023-06-23 00:00:00","2023-11-15 22:48:16"
@@ -276,8 +276,8 @@
 "275","cagi6-invitae","CAGI6: Sherloc clinical classification","122,000 coding variants predicted for ClinVar","Invitae is a genetic testing company that publishes their variant interpretations to ClinVar. In this challenge, over 122,000 previously uncharacterized variants are provided, spanning the range of effects seen in the clinic. Following the close of this challenge, Invitae will submit their interpretations for these variants to ClinVar. Predictors are asked to interpret the pathogenicity of these variants, and the clinical utility of predictions will be assessed across multiple categories by Invitae.","","https://genomeinterpretation.org/cagi6-invitae.html","completed","1","","2021-07-08","2021-12-01","2023-06-23 00:00:00","2023-11-16 17:44:21"
 "276","cagi6-splicing-vus","CAGI6: Splicing VUS","Predict whether vus disrupt splicing and contribute to genetic disorders","Variants causing aberrant splicing have been implicated in a range of common and rare disorders, including retinitis pigmentosa, autism spectrum disorder, amyotrophic lateral sclerosis, and a variety of cancers. However, such variants are frequently overlooked by diagnostic sequencing pipelines, leading to missed diagnoses for patients. Clinically ascertained variants of unknown significance underwent whole-blood based RT-PCR to test for impact on splicing. The challenge is to predict which of the tested variants disrupt splicing.","","https://genomeinterpretation.org/cagi6-splicing-vus.html","completed","1","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-11-14 19:44:47"
 "277","cagi6-stk11","CAGI6: STK11","Impact of variants in STK11 gene for Peutz-Jeghers syndrome","Serine/Threonine Kinase 11 (STK11) is considered a master kinase that functions as a tumor suppressor and nutrient sensor within a heterotrimeric complex with pseudo-kinase STRAD-alpha and structural protein MO25. Germline variants resulting in loss of STK11 define Peutz-Jaghers Syndrome, an autosomal dominant cancer predisposition syndrome marked by gastrointestinal hamartomas and freckling of the oral mucosa. Somatic loss of function variants, both nonsense and missense, occur in 15-30% of non-small cell lung adenocarcinomas, where they correlate clinically with insensitivity to anti-PD1 monoclonal antibody therapy. The challenge is to predict the impact on STK11 function for each missense variant in relation to wildtype STK11.","","https://genomeinterpretation.org/cagi6-stk11.html","completed","1","","2021-06-08","2021-09-01","2023-06-23 00:00:00","2023-11-16 17:44:00"
-"278","qbi-hackathon","QBI hackathon","The QBI hackathon","The QBI hackathon is a 48-hour event connecting the vibrant Bay Area developer community with the scientists from UCSF, UCB and UCSC, during which we work together on the cutting edge biomedical problems. Advances in computer vision, AI, and machine learning have enabled computers to pick out cat videos, recognize people’s faces from photos, play video games and drive cars. More recently, application of deep neural nets to protein structure prediction completely revolutionized the field. We look forward to seeing how far we can push science ahead when we apply these latest algorithms to biomedically relevant light microscopy, electron microscopy, and proteomics data. If you love FFTs, transformers, language models, topological data processing, or simply writing code, this is your chance to apply your skills to make an impact on global healthcare. Beyond the actual event, we hope to establish a better connection between talented developers and scientists in the Bay Area, so that we...","","https://www.eventbrite.com/e/qbi-hackathon-2023-tickets-633794304827?aff=oddtdtcreator","completed","14","","2023-11-04","2023-11-05","2023-10-06 21:22:51","2023-11-15 22:49:20"
-"279","niddk-central-repository-data-centric-challenge","NIDDK Central Repository Data-Centric Challenge","Enhance NIDDK datasets for future Artificial Intelligence (AI) applications","The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repository (https://repository.niddk.nih.gov/home/) is conducting a Data Centric Challenge aimed at augmenting existing Repository data for future secondary research including data-driven discovery by artificial intelligence (AI) researchers. The NIDDK Central Repository (NIDDK-CR) program strives to increase the utilization and impact of the resources under its guardianship. However, lack of standardization and consistent metadata within and across studies limit the ability of secondary researchers to easily combine datasets from related studies to generate new insights using data science methods. In the fall of 2021, the NIDDK-CR began implementing approaches to augment data quality to improve AI-readiness by making research data FAIR (findable, accessible, interoperable, and reusable) via a small pilot project utilizing Natural Language Processing (NLP) to tag study variables. In 2022, the NIDD...","","https://www.challenge.gov/?challenge=niddk-central-repository-data-centric-challenge","completed","14","","2023-09-20","2023-11-03","2023-10-18 16:58:17","2023-11-15 22:49:26"
+"278","qbi-hackathon","QBI hackathon","The QBI hackathon","The QBI hackathon is a 48-hour event connecting the vibrant Bay Area developer community with the scientists from UCSF, UCB and UCSC, during which we work together on the cutting edge biomedical problems. Advances in computer vision, AI, and machine learning have enabled computers to pick out cat videos, recognize people’s faces from photos, play video games and drive cars. More recently, application of deep neural nets to protein structure prediction completely revolutionized the field. We look forward to seeing how far we can push science ahead when we apply these latest algorithms to biomedically relevant light microscopy, electron microscopy, and proteomics data. If you love FFTs, transformers, language models, topological data processing, or simply writing code, this is your chance to apply your skills to make an impact on global healthcare. Beyond the actual event, we hope to establish a better connection between talented developers and scientists in the Bay Area, so that we...","","https://www.eventbrite.com/e/qbi-hackathon-2023-tickets-633794304827?aff=oddtdtcreator","completed","\N","","2023-11-04","2023-11-05","2023-10-06 21:22:51","2023-11-15 22:49:20"
+"279","niddk-central-repository-data-centric-challenge","NIDDK Central Repository Data-Centric Challenge","Enhance NIDDK datasets for future Artificial Intelligence (AI) applications","The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repository (https://repository.niddk.nih.gov/home/) is conducting a Data Centric Challenge aimed at augmenting existing Repository data for future secondary research including data-driven discovery by artificial intelligence (AI) researchers. The NIDDK Central Repository (NIDDK-CR) program strives to increase the utilization and impact of the resources under its guardianship. However, lack of standardization and consistent metadata within and across studies limit the ability of secondary researchers to easily combine datasets from related studies to generate new insights using data science methods. In the fall of 2021, the NIDDK-CR began implementing approaches to augment data quality to improve AI-readiness by making research data FAIR (findable, accessible, interoperable, and reusable) via a small pilot project utilizing Natural Language Processing (NLP) to tag study variables. In 2022, the NIDD...","","https://www.challenge.gov/?challenge=niddk-central-repository-data-centric-challenge","completed","\N","","2023-09-20","2023-11-03","2023-10-18 16:58:17","2023-11-15 22:49:26"
 "280","stanford-ribonanza-rna-folding","Stanford Ribonanza RNA Folding","A path to programmable medicine and scientific breakthroughs","Ribonucleic acid (RNA) is essential for most biological functions. A better understanding of how to manipulate RNA could help usher in an age of programmable medicine, including first cures for pancreatic cancer and Alzheimer’s disease as well as much-needed antibiotics and new biotechnology approaches for climate change. But first, researchers must better understand each RNA molecule's structure, an ideal problem for data science.","","https://www.kaggle.com/competitions/stanford-ribonanza-rna-folding","completed","8","","2023-08-23","2023-11-24","2023-10-23 20:58:06","2023-11-15 22:49:31"
 "281","uls23","Universal Lesion Segmentation Challenge '23","Advancements, challenges, and a universal solution emerges","Significant advancements have been made in AI-based automatic segmentation models for tumours. Medical challenges focusing on e.g. Liver, kidney, or lung tumours have resulted in large performance improvements for segmenting these types of lesions. However, in clinical practice there is a need for versatile and robust models capable of quickly segmenting the many possible lesions types in the thorax-abdomen area. Developing a universal lesion segmentation (uls) model that can handle this diversity of lesions types requires a well-curated and varied dataset. Whilst there has been previous work on uls [6-8], most research in this field has made extensive use of a single partially annotated dataset [9], containing only the long- and short-axis diameters on a single axial slice. Furthermore, a test set containing 3d segmentation masks used during evaluation on this dataset by previous publications is not publicly available.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/747/ULS23_logo_aoB8tlx.png","https://uls23.grand-challenge.org/","active","5","","2023-10-29","2024-03-17","2023-11-02 15:35:22","2023-11-17 21:29:35"
 "282","vessel12","VESSEL12","Assess methods for blood vessels in lung CT images","The VESSEL12 challenge compares methods for automatic (and semi-automatic) segmentation of blood vessels in the lungs from CT images.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/1/logo.png","https://vessel12.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.media.2014.07.003","2011-11-25","2012-04-01","2023-11-08 00:42:00","2023-11-17 21:30:05"
@@ -377,7 +377,7 @@
 "376","flare","FLARE21","Abdominal organ segmentation challenge","Abdominal organ segmentation plays an important role in clinical practice, and to some extent, it seems to be a solved problem because the state-of-the-art methods have achieved inter-observer performance in several benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can be generalized on more diverse datasets. Moreover, many SOTA methods use model ensembles to boost performance, but these solutions usually have a large model size and cost extensive computational resources, which are impractical to be deployed in clinical practice.  To address these limitations, we organize the Fast and Low GPU Memory Abdominal Organ Segmentation challenge that has two main features:  (1) the dataset is large and diverse, includes 511 cases from 11 medical centers.  (2) we not only focus on segmentation accuracy but also segmentation efficiency, whi...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/599/logo_hDqJ8uG.gif","https://flare.grand-challenge.org/","active","5","https://doi.org/10.1016/j.media.2022.102616","\N","\N","2023-11-08 00:42:00","2023-11-15 22:36:39"
 "377","nucls","NuCLS","Triple-negative breast cancer nuclei challenge","Classification, Localization and Segmentation of nuclei in scanned FFPE H&E stained slides of triple-negative breast cancer from The Cancer Genome Atlas. See: Amgad et al. 2021. arXiv:2102.09099 [cs.CV].","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/601/TCGA-AR-A0U4-DX1_id-5ea40a88ddda5f8398990ccf_left-42405_top-70784_bo_PgpXdUu.png","https://nucls.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:29:28"
 "378","bcsegmentation","Breast Cancer Segmentation","Triple-negative breast cancer segmentation","Semantic segmentation of histologic regions in scanned FFPE H&E stained slides of triple-negative breast cancer from The Cancer Genome Atlas. See: Amgad M, Elfandy H, ..., Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/602/BCSegmentationLogo.png","https://bcsegmentation.grand-challenge.org/","completed","5","","\N","\N","2023-11-08 00:42:00","2023-11-17 23:29:37"
-"379","feta","FeTA - Fetal Tissue Annotation Challenge","Fetal tissue annotation challenge","The Fetal Tissue Annotation and Segmentation Challenge (FeTA) is a multi-class, multi-institution image segmentation challenge part of MICCAI 2022. The goal of FeTA is to develop generalizable automatic multi-class segmentation methods for the segmentation of developing human brain tissues that will work with data acquired at different hospitals.  The challenge provides manually annotated, super-resolution reconstructed MRI data of human fetal brains which will be used for training and testing automated multi-class image segmentation algorithms. In FeTA 2021, we used the first publicly available dataset of fetal brain MRI to encourage teams to develop automatic brain tissue segmentation algorithms. This year, FeTA 2022 takes it to the next level by launching a multi-center challenge for the development of image segmentation algorithms that will be generalizable to different hospitals with unseen data. We will include data from two institutions in the training dataset, and there wi...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/604/FeTA_logo_640.png","https://feta.grand-challenge.org/","upcoming","5","","2024-03-21","2024-04-26","2023-11-08 00:42:00","2023-11-15 22:36:15"
+"379","feta","FeTA - Fetal Tissue Annotation Challenge","Fetal tissue annotation challenge","The Fetal Tissue Annotation and Segmentation Challenge (FeTA) is a multi-class, multi-institution image segmentation challenge part of MICCAI 2022. The goal of FeTA is to develop generalizable automatic multi-class segmentation methods for the segmentation of developing human brain tissues that will work with data acquired at different hospitals.  The challenge provides manually annotated, super-resolution reconstructed MRI data of human fetal brains which will be used for training and testing automated multi-class image segmentation algorithms. In FeTA 2021, we used the first publicly available dataset of fetal brain MRI to encourage teams to develop automatic brain tissue segmentation algorithms. This year, FeTA 2022 takes it to the next level by launching a multi-center challenge for the development of image segmentation algorithms that will be generalizable to different hospitals with unseen data. We will include data from two institutions in the training dataset, and there wi...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/604/FeTA_logo_640.png","https://feta.grand-challenge.org/","upcoming","5","","2024-03-21","2024-04-26","2023-11-08 00:42:00","2023-12-12 19:00:18"
 "380","fastpet-ld","fastPET-LD","PET scan ""hot spots"" detection challenge","In this challenge, we provide 2 training datasets of 68 cases each: the first one was acquired at Sheba medical center (Israel) nuclear medicine department with a very-short exposure of 30s pbp, while the second is the same data followed by a denoising step implemented by a fully convolutional Dnn architecture trained under perceptual loss [1,2]. The purpose of this challenge is the detection of “hot spots”, that is locations that have an elevated standard uptake value (SUV) and potential clinical significance. Corresponding CT scans are also provided. The ground truth, common to both datasets, was generated by Dr. Liran Domachevsky, chair of  nuclear medicine  at Sheba medical center. It consists of a 3-D segmentation map of the hot spots as well as an Excel file containing the position and size of a 3D cuboid bounding box for each hot spot.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/605/IMG_19052021_144815_600_x_600_pixel.jpg","https://fastpet-ld.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2023-11-15 22:35:52"
 "381","autoimplant2021","AutoImplant 2021","Automatic cranial implant design challenge","Please see our AutoImplant 2020 website for an overview of the cranial implant design topic.  Our 2nd AutoImplant Challenge (referred to as AutoImplant 2021) sees the (not limited to) following  three major improvements  compared to the prior edition, besides a stronger team: Real craniotomy defective skulls will be provided in the evaluation phase. Task specific metrics (e.g., boundary Dice Score) that are optimally in agreement with the clinical criteria of cranial implant design will be implemented and used. Besides a metric-based scoring and ranking system, neurosurgeons will be invited to verify, score and rank the participants-submitted cranial implants based their clinical usability (for the real cases in Task 2).","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/607/AutoImplant_2021_Logo.png","https://autoimplant2021.grand-challenge.org/","completed","5","https://doi.org/10.1109/tmi.2021.3077047","\N","\N","2023-11-08 00:42:00","2023-11-16 17:41:01"
 "382","dfu-2021","DFUC2021","Diabetic foot ulcer challenge 2021","We  have  received  approval  from  the  UK  National  Health  Service  (NHS)  Re-search Ethics Committee (REC) to use these images for the purpose of research. The NHS REC reference number is 15/NW/0539. Foot images with DFU were collected from the Lancashire Teaching Hospital over the past few years. Three cameras were used for capturing the foot images, Kodak DX4530, Nikon D3300and Nikon COOLPIX P100. The images were acquired with close-ups of the full foot at a distance of around 30–40 cm with the parallel orientation to the plane of an ulcer. The use of flash as the primary light source was avoided, and instead, adequate room lights were used to get the consistent colours in images. Images were acquired by a podiatrist and a consultant physician with specialization in the diabetic foot, both with more than 5 years professional experience. As a pre-processing stage, we have discarded photographs with out of focus and blurry artefacts. The DFUC2021 consists of 15,683 DFU patche...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/608/footsnap_logo.png","https://dfu-2021.grand-challenge.org/","active","5","https://doi.org/10.1007/978-3-030-94907-5_7","\N","\N","2023-11-08 00:42:00","2023-11-16 17:41:08"
@@ -459,19 +459,24 @@
 "458","cameo-qe-model-quality-estimation","CAMEO-QE: Model Quality Estimation","Protein structure prediction quality assessment","Cameo Continuously Applies Quality Assessment Criteria Established By The Protein Structure Prediction Community. Since The Accuracy Requirements For Different Scientific Applications Vary, There Is No ""One Fits All"" Score. Cameo Therefore Offers A Variety Of Scores - Assessing Different Aspects Of A Prediction (Coverage, Local Accuracy, Completeness, Etc.) To Reflect These Requirements.","","https://www.cameo3d.org/quality-estimation/","active","18","","2023-11-04","\N","2023-11-11 01:29:20","2023-11-16 22:41:57"
 "459","cameo-3d-modeling-of-structures-and-complexes-beta","CAMEO-3D: Modeling of Structures & Complexes - BETA","Protein structure prediction quality assessment","Cameo Continuously Applies Quality Assessment Criteria Established By The Protein Structure Prediction Community. Since The Accuracy Requirements For Different Scientific Applications Vary, There Is No ""One Fits All"" Score. Cameo Therefore Offers A Variety Of Scores - Assessing Different Aspects Of A Prediction (Coverage, Local Accuracy, Completeness, Etc.) To Reflect These Requirements.","","https://beta.cameo3d.org/complete-modeling/","active","18","","2023-11-04","\N","2023-11-11 01:29:20","2023-11-16 22:41:58"
 "460","pegs-dream-challenge","PEGS DREAM Challenge","","","","https://www.synapse.org/pegs","upcoming","1","","\N","\N","2023-11-13 22:48:02","2023-11-16 16:20:18"
-"461","fda-data-centric-challenge","FDA Data-Centric Challenge","","","","https://www.synapse.org/fda_data_centric","upcoming","1","","\N","\N","2023-11-13 22:49:41","2023-11-16 16:18:38"
+"461","fda-data-centric-challenge","FDA Data-Centric Challenge","","The Food and Drug Administration (FDA) - Center for Devices and Radiological Health (CDRH), Sage Bionetworks, and precisionFDA call on the scientific, industry, and data science communities to develop methods to augment the training data and improve the robustness of a baseline artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD).","","https://www.synapse.org/fda_data_centric","upcoming","1","","\N","\N","2023-11-13 22:49:41","2023-12-12 19:02:40"
 "462","ai-institute-for-dynamic-systems","AI Institute for Dynamic Systems","","","","https://www.synapse.org/#!Synapse:syn52052735","upcoming","1","","\N","\N","2023-11-13 22:51:53","2023-11-17 0:13:33"
-"463","competition-nih-alzheimers-adrd-1","PREPARE Phase 1 - Find IT!","Help NIH break new ground in early Alzheimer's prediction and related dementias","The goal of the PREPARE Challenge (Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA Challenge) is to inform novel approaches to early detection that might ultimately lead to more accurate tests, tools, and methodologies for clinical and research purposes. Advances in artificial intelligence (AI), machine learning (ML), and computing ecosystems increase possibilities of intelligent data collection and analysis, including better algorithms and methods that could be leveraged for the prediction of biological, psychological (cognitive), socio-behavioral, functional, and clinical changes related to AD/ADRD.  This first phase, Find IT!: Data for Early Prediction, is focused on finding, curating, or contributing data to create representative and open datasets that can be used for the early prediction of AD/ADRD.","","https://www.drivendata.org/competitions/253/competition-nih-alzheimers-adrd-1/","active","18","","2023-09-01","2024-01-31","2023-11-16 21:57:03","2023-12-06 7:15:18"
-"464","prepare-phase-2-build-it","PREPARE Phase 2 - Build IT!","Help NIH break new ground in early Alzheimer's prediction and related dementias","The goal of the PREPARE Challenge (Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA Challenge) is to inform novel approaches to early detection that might ultimately lead to more accurate tests, tools, and methodologies for clinical and research purposes. Advances in artificial intelligence (AI), machine learning (ML), and computing ecosystems increase possibilities of intelligent data collection and analysis, including better algorithms and methods that could be leveraged for the prediction of biological, psychological (cognitive), socio-behavioral, functional, and clinical changes related to AD/ADRD.  This second phase, Build IT!: Algorithms and Approaches, is focused on advancing algorithms and analytic approaches for early prediction of AD/ADRD, with an emphasis on explainability of predictions.","","","upcoming","18","","2024-09-01","\N","2023-11-17 00:09:25","2023-12-06 7:18:18"
-"465","prepare-phase-3-put-it-all-together","PREPARE Phase 3 - Put IT All Together!","Help NIH break new ground in early Alzheimer's prediction and related dementias","The goal of the PREPARE Challenge (Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA Challenge) is to inform novel approaches to early detection that might ultimately lead to more accurate tests, tools, and methodologies for clinical and research purposes. Advances in artificial intelligence (AI), machine learning (ML), and computing ecosystems increase possibilities of intelligent data collection and analysis, including better algorithms and methods that could be leveraged for the prediction of biological, psychological (cognitive), socio-behavioral, functional, and clinical changes related to AD/ADRD.  This third phase, Put IT All Together!: Proof of Principle Demonstration, is for the top solvers from Phase 2 demonstrate algorithmic approaches on diverse datasets and share their results at an innovation event.","","","upcoming","18","","2025-03-01","\N","2023-11-17 00:09:26","2023-12-06 7:18:08"
-"466","cdc-fall-narratives","Unsupervised Wisdom: Explore Medical Narratives on Older Adult Falls","Extract insights about older adult falls from emergency department narratives","Falls among adults 65 and older is the leading cause of injury-related deaths. Falls can also result in serious injuries to the head and/or broken bones. Some risk factors associated with falls can be reduced through appropriate interventions like treating vision problems, exercising for strength and balance, and removing tripping hazards in your home.  Medical record narratives are a rich yet under-explored source of potential insights about how, when, and why people fall. However, narrative data sources can be difficult to work with, often requiring carefully designed, time-intensive manual coding procedures. Modern machine learning approaches to working with narrative data have the potential to effectively extract insights about older adult falls from narrative medical record data at scale.  The goal in this challenge is to identify effective methods of using unsupervised machine learning to extract insights about older adult falls from emergency department narratives. Insights...","https://drivendata-public-assets.s3.amazonaws.com/cdc-banner-hands.png","https://www.drivendata.org/competitions/217/cdc-fall-narratives/","completed","18","","\N","2023-10-06","2023-12-06 06:56:06","2023-12-06 7:21:14"
-"467","visiomel-melanoma","VisioMel Challenge: Predicting Melanoma Relapse","Use digitized microscopic slides to predict the likelihood of melanoma relapse","Melanoma is a cancer of the skin which develops from cells responsible for skin pigmentation. In 2020, over 325,000 people were diagnosed with skin melanoma, with 57,000 deaths in the same year.1 Melanomas represent 10% of all skin cancers and are the most dangerous due to high likelihood of metastasizing (spreading).  Patients are initially diagnosed with melanoma after a pathologist examines a portion of the cancerous tissue under a microscope. At this stage, the pathologist assesses the risk of relapse—a return of cancerous cells after the melanoma has been treated—based on information such as the thickness of the tumor and the presence of an ulceration. Combined with factors such as age, sex, and medical history of the patient, these microscopic observations can help a dermatologist assess the severity of the disease and determine appropriate surgical and medical treatment. Preventative treatments can be administered to patients with high likelihood for relapse. However, these...","https://drivendata-public-assets.s3.amazonaws.com/visiomel_banner_img.jpeg","https://www.drivendata.org/competitions/1481/visiomel-melanoma/","completed","18","","\N","2023-05-11","2023-12-06 07:35:00","2023-12-06 7:52:55"
-"468","competition-cervical-biopsy","TissueNet: Detect Lesions in Cervical Biopsies","Detect the most severe epithelial lesions of the uterine cervix","A biopsy is a sample of tissue examined at a microscopic level to diagnose cancer or signs of pre-cancer. While most diagnoses are still made with photonic microscopes, digital pathology has developed considerably over the past decade as it has become possible to digitize slides into ""virtual slides"" or ""whole slide images"" (WSIs). These heavy image files contain all the information required to diagnose lesions as malignant or benign.  Making this diagnosis is no easy task. It requires specialized training and careful examination of microscopic tissue. Approaches in machine learning are already able to help analyze WSIs by measuring or counting areas of the image under a pathologist's supervision. In addition, computer vision has shown some potential to classify tumor subtypes, and in time may offer a powerful tool to aid pathologists in making the most accurate diagnoses.  This challenge focuses on epithelial lesions of the uterine cervix, and features a unique collection of thou...","https://s3.amazonaws.com/drivendata-public-assets/sfp_comp_image.jpg","https://www.drivendata.org/competitions/67/competition-cervical-biopsy/","completed","18","","\N","2020-10-29","2023-12-06 07:52:34","2023-12-06 7:58:21"
-"469","clog-loss-alzheimers-research","Clog Loss: Advance Alzheimer’s Research with Stall Catchers","Automatically classify which blood vessels are flowing and which are stalled","5.8 million Americans live with Alzheimer’s dementia, including 10% of all seniors 65 and older. Scientists at Cornell have discovered links between “stalls,” or clogged blood vessels in the brain, and Alzheimer’s. Stalls can reduce overall blood flow in the brain by 30%. The ability to prevent or remove stalls may transform how Alzheimer’s disease is treated.  Stall Catchers is a citizen science project that crowdsources the analysis of Alzheimer’s disease research data provided by Cornell University’s Department of Biomedical Engineering. It resolves a pressing analytic bottleneck: for each hour of data collection it would take an entire week to analyze the results in the lab, which means an entire experimental dataset would take 6-12 months to analyze. Today, the Stall Catchers players are collectively analyzing data 5x faster than the lab while exceeding data quality requirements.  The research team has realized there are aspects of this task that are best suited to uniquely h...","","https://www.drivendata.org/competitions/65/clog-loss-alzheimers-research/","completed","18","","\N","2020-08-03","2023-12-06 08:04:52","2023-12-06 8:07:15"
-"470","flu-shot-learning","Flu Shot Learning: Predict H1N1 and Seasonal Flu Vaccines","Predict whether people got H1N1 and flu vaccines using information they shared","In this challenge, we will take a look at vaccination, a key public health measure used to fight infectious diseases. Vaccines provide immunization for individuals, and enough immunization in a community can further reduce the spread of diseases through ""herd immunity.""  As of the launch of this competition, vaccines for the COVID-19 virus are still under development and not yet available. The competition will instead revisit the public health response to a different recent major respiratory disease pandemic. Beginning in spring 2009, a pandemic caused by the H1N1 influenza virus, colloquially named ""swine flu,"" swept across the world. Researchers estimate that in the first year, it was responsible for between 151,000 to 575,000 deaths globally.  A vaccine for the H1N1 flu virus became publicly available in October 2009. In late 2009 and early 2010, the United States conducted the National 2009 H1N1 Flu Survey. This phone survey asked respondents whether they had received the H1N1...","https://drivendata-public-assets.s3.amazonaws.com/flu-vaccine.jpg","https://www.drivendata.org/competitions/66/flu-shot-learning/","active","18","","\N","2024-07-30","2023-12-06 08:10:49","2023-12-06 8:14:49"
-"471","machine-learning-with-a-heart","Warm Up: Machine Learning with a Heart","Predict the presence or absence of heart disease in patients","We've all got to start somewhere. This is one of the smallest datasets on DrivenData. That makes it a great place to dive into the world of data science competitions. Get your heart thumping and try your hand at predicting heart disease.","","https://www.drivendata.org/competitions/54/machine-learning-with-a-heart/","completed","18","","\N","2019-10-30","2023-12-06 08:19:47","2023-12-06 8:21:53"
-"472","dengai-predicting-disease-spread","DengAI: Predicting Disease Spread","Predict the number of dengue fever cases reported each week in 2 regions","Using environmental data collected by various U.S. Federal Government agencies—from the Centers for Disease Control and Prevention to the National Oceanic and Atmospheric Administration in the U.S. Department of Commerce—can you predict the number of dengue fever cases reported each week in San Juan, Puerto Rico and Iquitos, Peru?  This is an intermediate-level practice competition. Your task is to predict the number of dengue cases each week (in each location) based on environmental variables describing changes in temperature, precipitation, vegetation, and more.  An understanding of the relationship between climate and dengue dynamics can improve research initiatives and resource allocation to help fight life-threatening pandemics.","","https://www.drivendata.org/competitions/44/dengai-predicting-disease-spread/","active","18","","\N","2024-10-05","2023-12-06 08:28:42","2023-12-06 8:30:39"
-"473","senior-data-science-safe-aging-with-sphere","Senior Data Science: Safe Aging with SPHERE","Predict actual activity from sensor data in seniors","This challenge is part of a large research project which centers around using sensors and algorithms to help older people live safely at home while maintaining their privacy and independence. Using passive, automated monitoring, the ultimate goal is to look out for a person's well-being without being burdensome or intrusive.  To gather data, researchers in the SPHERE Inter-disciplinary Research Collaboration (IRC) equipped volunteers with accelerometers similar to those found in cell phones or fitness wearables, and then had the subjects go about normal activities of daily living in a home-like environment that was also equipped with motion detectors. After gathering a robust set of sensor data, they had multiple annotators use camera footage to establish the ground truth, labeling chunks of sensor data as one of twenty specifically chosen activities (e.g. walk, sit, stand-to-bend, ascend stairs, descend stairs, etc).  Your challenge: help push forward the state of the art by pred...","","https://www.drivendata.org/competitions/42/senior-data-science-safe-aging-with-sphere/","completed","18","","\N","2016-07-31","2023-12-06 08:35:31","2023-12-06 8:44:36"
-"474","countable-care-modeling-womens-health-care-decisions","Countable Care: Modeling Women's Health Care Decisions","Predict what drives women’s health care decisions in America","Recent literature suggests that the demand for women’s health care will grow over 6% by 2020. Given how rapidly the health landscape has been changing over the last 15 years, it’s increasingly important that we understand how these changes affect what care people receive, where they go for it, and how they pay. Through the National Survey of Family Growth, the CDC provides one of the few nationally representative datasets that dives deep into the questions that women face when thinking about their health.  Can you predict what drives women’s health care decisions in America?","","https://www.drivendata.org/competitions/6/countable-care-modeling-womens-health-care-decisions/","completed","18","","\N","2015-04-14","2023-12-06 08:45:12","2023-12-06 8:46:00"
-"475","warm-up-predict-blood-donations","Warm Up: Predict Blood Donations","Predict whether a donor will return to donate blood given their donation history","We've all got to start somewhere. This is the smallest, least complex dataset on DrivenData. That makes it a great place to dive into the world of data science competitions. Get your blood pumping and try your hand at predicting donations.","","https://www.drivendata.org/competitions/2/warm-up-predict-blood-donations/","completed","18","","\N","2019-03-21","2023-12-06 08:52:21","2023-12-06 8:53:13"
-"476","genetic-engineering-attribution","Genetic Engineering Attribution Challenge","Identify the lab-of-origin for genetically engineered DNA","our goal is to create an algorithm that identifies the most likely lab-of-origin for genetically engineered DNA.  Applications for genetic engineering are rapidly diversifying. Researchers across the world are using powerful new techniques in synthetic biology to solve some of the world’s most pressing challenges in medicine, agriculture, manufacturing and more. At the same time, increasingly powerful genetically engineered systems could yield unintended consequences for people, food crops, livestock, and industry. These incredible advances in capability demand tools that support accountable innovation.  Genetic engineering attribution is the process of identifying the source of a genetically engineered piece of DNA. This ability ensures that scientists who have spent countless hours developing breakthrough technology get their due credit, intellectual property is protected, and responsible innovation is promoted. By connecting a genetically engineered system with its designers, s...","https://s3.amazonaws.com/drivendata-public-assets/al-green-homepage.jpg","https://www.drivendata.org/competitions/63/genetic-engineering-attribution/","completed","18","","\N","2020-10-19","2023-12-06 08:54:24","2023-12-06 8:56:29"
+"463","competition-nih-alzheimers-adrd-1","PREPARE Phase 1 - Find IT!","Help NIH break new ground in early Alzheimer's prediction and related dementias","The goal of the PREPARE Challenge (Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA Challenge) is to inform novel approaches to early detection that might ultimately lead to more accurate tests, tools, and methodologies for clinical and research purposes. Advances in artificial intelligence (AI), machine learning (ML), and computing ecosystems increase possibilities of intelligent data collection and analysis, including better algorithms and methods that could be leveraged for the prediction of biological, psychological (cognitive), socio-behavioral, functional, and clinical changes related to AD/ADRD.  This first phase, Find IT!: Data for Early Prediction, is focused on finding, curating, or contributing data to create representative and open datasets that can be used for the early prediction of AD/ADRD.","","https://www.drivendata.org/competitions/253/competition-nih-alzheimers-adrd-1/","active","19","","2023-09-01","2024-01-31","2023-11-16 21:57:03","2023-12-06 7:15:18"
+"464","prepare-phase-2-build-it","PREPARE Phase 2 - Build IT!","Help NIH break new ground in early Alzheimer's prediction and related dementias","The goal of the PREPARE Challenge (Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA Challenge) is to inform novel approaches to early detection that might ultimately lead to more accurate tests, tools, and methodologies for clinical and research purposes. Advances in artificial intelligence (AI), machine learning (ML), and computing ecosystems increase possibilities of intelligent data collection and analysis, including better algorithms and methods that could be leveraged for the prediction of biological, psychological (cognitive), socio-behavioral, functional, and clinical changes related to AD/ADRD.  This second phase, Build IT!: Algorithms and Approaches, is focused on advancing algorithms and analytic approaches for early prediction of AD/ADRD, with an emphasis on explainability of predictions.","","","upcoming","19","","2024-09-01","\N","2023-11-17 00:09:25","2023-12-06 7:18:18"
+"465","prepare-phase-3-put-it-all-together","PREPARE Phase 3 - Put IT All Together!","Help NIH break new ground in early Alzheimer's prediction and related dementias","The goal of the PREPARE Challenge (Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA Challenge) is to inform novel approaches to early detection that might ultimately lead to more accurate tests, tools, and methodologies for clinical and research purposes. Advances in artificial intelligence (AI), machine learning (ML), and computing ecosystems increase possibilities of intelligent data collection and analysis, including better algorithms and methods that could be leveraged for the prediction of biological, psychological (cognitive), socio-behavioral, functional, and clinical changes related to AD/ADRD.  This third phase, Put IT All Together!: Proof of Principle Demonstration, is for the top solvers from Phase 2 demonstrate algorithmic approaches on diverse datasets and share their results at an innovation event.","","","upcoming","19","","2025-03-01","\N","2023-11-17 00:09:26","2023-12-06 7:18:08"
+"466","cdc-fall-narratives","Unsupervised Wisdom: Explore Medical Narratives on Older Adult Falls","Extract insights about older adult falls from emergency department narratives","Falls among adults 65 and older is the leading cause of injury-related deaths. Falls can also result in serious injuries to the head and/or broken bones. Some risk factors associated with falls can be reduced through appropriate interventions like treating vision problems, exercising for strength and balance, and removing tripping hazards in your home.  Medical record narratives are a rich yet under-explored source of potential insights about how, when, and why people fall. However, narrative data sources can be difficult to work with, often requiring carefully designed, time-intensive manual coding procedures. Modern machine learning approaches to working with narrative data have the potential to effectively extract insights about older adult falls from narrative medical record data at scale.  The goal in this challenge is to identify effective methods of using unsupervised machine learning to extract insights about older adult falls from emergency department narratives. Insights...","https://drivendata-public-assets.s3.amazonaws.com/cdc-banner-hands.png","https://www.drivendata.org/competitions/217/cdc-fall-narratives/","completed","19","","\N","2023-10-06","2023-12-06 06:56:06","2023-12-06 7:21:14"
+"467","visiomel-melanoma","VisioMel Challenge: Predicting Melanoma Relapse","Use digitized microscopic slides to predict the likelihood of melanoma relapse","Melanoma is a cancer of the skin which develops from cells responsible for skin pigmentation. In 2020, over 325,000 people were diagnosed with skin melanoma, with 57,000 deaths in the same year.1 Melanomas represent 10% of all skin cancers and are the most dangerous due to high likelihood of metastasizing (spreading).  Patients are initially diagnosed with melanoma after a pathologist examines a portion of the cancerous tissue under a microscope. At this stage, the pathologist assesses the risk of relapse—a return of cancerous cells after the melanoma has been treated—based on information such as the thickness of the tumor and the presence of an ulceration. Combined with factors such as age, sex, and medical history of the patient, these microscopic observations can help a dermatologist assess the severity of the disease and determine appropriate surgical and medical treatment. Preventative treatments can be administered to patients with high likelihood for relapse. However, these...","https://drivendata-public-assets.s3.amazonaws.com/visiomel_banner_img.jpeg","https://www.drivendata.org/competitions/1481/visiomel-melanoma/","completed","19","","\N","2023-05-11","2023-12-06 07:35:00","2023-12-06 7:52:55"
+"468","competition-cervical-biopsy","TissueNet: Detect Lesions in Cervical Biopsies","Detect the most severe epithelial lesions of the uterine cervix","A biopsy is a sample of tissue examined at a microscopic level to diagnose cancer or signs of pre-cancer. While most diagnoses are still made with photonic microscopes, digital pathology has developed considerably over the past decade as it has become possible to digitize slides into ""virtual slides"" or ""whole slide images"" (WSIs). These heavy image files contain all the information required to diagnose lesions as malignant or benign.  Making this diagnosis is no easy task. It requires specialized training and careful examination of microscopic tissue. Approaches in machine learning are already able to help analyze WSIs by measuring or counting areas of the image under a pathologist's supervision. In addition, computer vision has shown some potential to classify tumor subtypes, and in time may offer a powerful tool to aid pathologists in making the most accurate diagnoses.  This challenge focuses on epithelial lesions of the uterine cervix, and features a unique collection of thou...","https://s3.amazonaws.com/drivendata-public-assets/sfp_comp_image.jpg","https://www.drivendata.org/competitions/67/competition-cervical-biopsy/","completed","19","","\N","2020-10-29","2023-12-06 07:52:34","2023-12-06 7:58:21"
+"469","clog-loss-alzheimers-research","Clog Loss: Advance Alzheimer’s Research with Stall Catchers","Automatically classify which blood vessels are flowing and which are stalled","5.8 million Americans live with Alzheimer’s dementia, including 10% of all seniors 65 and older. Scientists at Cornell have discovered links between “stalls,” or clogged blood vessels in the brain, and Alzheimer’s. Stalls can reduce overall blood flow in the brain by 30%. The ability to prevent or remove stalls may transform how Alzheimer’s disease is treated.  Stall Catchers is a citizen science project that crowdsources the analysis of Alzheimer’s disease research data provided by Cornell University’s Department of Biomedical Engineering. It resolves a pressing analytic bottleneck: for each hour of data collection it would take an entire week to analyze the results in the lab, which means an entire experimental dataset would take 6-12 months to analyze. Today, the Stall Catchers players are collectively analyzing data 5x faster than the lab while exceeding data quality requirements.  The research team has realized there are aspects of this task that are best suited to uniquely h...","","https://www.drivendata.org/competitions/65/clog-loss-alzheimers-research/","completed","19","","\N","2020-08-03","2023-12-06 08:04:52","2023-12-06 8:07:15"
+"470","flu-shot-learning","Flu Shot Learning: Predict H1N1 and Seasonal Flu Vaccines","Predict whether people got H1N1 and flu vaccines using information they shared","In this challenge, we will take a look at vaccination, a key public health measure used to fight infectious diseases. Vaccines provide immunization for individuals, and enough immunization in a community can further reduce the spread of diseases through ""herd immunity.""  As of the launch of this competition, vaccines for the COVID-19 virus are still under development and not yet available. The competition will instead revisit the public health response to a different recent major respiratory disease pandemic. Beginning in spring 2009, a pandemic caused by the H1N1 influenza virus, colloquially named ""swine flu,"" swept across the world. Researchers estimate that in the first year, it was responsible for between 151,000 to 575,000 deaths globally.  A vaccine for the H1N1 flu virus became publicly available in October 2009. In late 2009 and early 2010, the United States conducted the National 2009 H1N1 Flu Survey. This phone survey asked respondents whether they had received the H1N1...","https://drivendata-public-assets.s3.amazonaws.com/flu-vaccine.jpg","https://www.drivendata.org/competitions/66/flu-shot-learning/","active","19","","\N","2024-07-30","2023-12-06 08:10:49","2023-12-06 8:14:49"
+"471","machine-learning-with-a-heart","Warm Up: Machine Learning with a Heart","Predict the presence or absence of heart disease in patients","We've all got to start somewhere. This is one of the smallest datasets on DrivenData. That makes it a great place to dive into the world of data science competitions. Get your heart thumping and try your hand at predicting heart disease.","","https://www.drivendata.org/competitions/54/machine-learning-with-a-heart/","completed","19","","\N","2019-10-30","2023-12-06 08:19:47","2023-12-06 8:21:53"
+"472","dengai-predicting-disease-spread","DengAI: Predicting Disease Spread","Predict the number of dengue fever cases reported each week in 2 regions","Using environmental data collected by various U.S. Federal Government agencies—from the Centers for Disease Control and Prevention to the National Oceanic and Atmospheric Administration in the U.S. Department of Commerce—can you predict the number of dengue fever cases reported each week in San Juan, Puerto Rico and Iquitos, Peru?  This is an intermediate-level practice competition. Your task is to predict the number of dengue cases each week (in each location) based on environmental variables describing changes in temperature, precipitation, vegetation, and more.  An understanding of the relationship between climate and dengue dynamics can improve research initiatives and resource allocation to help fight life-threatening pandemics.","","https://www.drivendata.org/competitions/44/dengai-predicting-disease-spread/","active","19","","\N","2024-10-05","2023-12-06 08:28:42","2023-12-06 8:30:39"
+"473","senior-data-science-safe-aging-with-sphere","Senior Data Science: Safe Aging with SPHERE","Predict actual activity from sensor data in seniors","This challenge is part of a large research project which centers around using sensors and algorithms to help older people live safely at home while maintaining their privacy and independence. Using passive, automated monitoring, the ultimate goal is to look out for a person's well-being without being burdensome or intrusive.  To gather data, researchers in the SPHERE Inter-disciplinary Research Collaboration (IRC) equipped volunteers with accelerometers similar to those found in cell phones or fitness wearables, and then had the subjects go about normal activities of daily living in a home-like environment that was also equipped with motion detectors. After gathering a robust set of sensor data, they had multiple annotators use camera footage to establish the ground truth, labeling chunks of sensor data as one of twenty specifically chosen activities (e.g. walk, sit, stand-to-bend, ascend stairs, descend stairs, etc).  Your challenge: help push forward the state of the art by pred...","","https://www.drivendata.org/competitions/42/senior-data-science-safe-aging-with-sphere/","completed","19","","\N","2016-07-31","2023-12-06 08:35:31","2023-12-06 8:44:36"
+"474","countable-care-modeling-womens-health-care-decisions","Countable Care: Modeling Women's Health Care Decisions","Predict what drives women’s health care decisions in America","Recent literature suggests that the demand for women’s health care will grow over 6% by 2020. Given how rapidly the health landscape has been changing over the last 15 years, it’s increasingly important that we understand how these changes affect what care people receive, where they go for it, and how they pay. Through the National Survey of Family Growth, the CDC provides one of the few nationally representative datasets that dives deep into the questions that women face when thinking about their health.  Can you predict what drives women’s health care decisions in America?","","https://www.drivendata.org/competitions/6/countable-care-modeling-womens-health-care-decisions/","completed","19","","\N","2015-04-14","2023-12-06 08:45:12","2023-12-06 8:46:00"
+"475","warm-up-predict-blood-donations","Warm Up: Predict Blood Donations","Predict whether a donor will return to donate blood given their donation history","We've all got to start somewhere. This is the smallest, least complex dataset on DrivenData. That makes it a great place to dive into the world of data science competitions. Get your blood pumping and try your hand at predicting donations.","","https://www.drivendata.org/competitions/2/warm-up-predict-blood-donations/","completed","19","","\N","2019-03-21","2023-12-06 08:52:21","2023-12-06 8:53:13"
+"476","genetic-engineering-attribution","Genetic Engineering Attribution Challenge","Identify the lab-of-origin for genetically engineered DNA","our goal is to create an algorithm that identifies the most likely lab-of-origin for genetically engineered DNA.  Applications for genetic engineering are rapidly diversifying. Researchers across the world are using powerful new techniques in synthetic biology to solve some of the world’s most pressing challenges in medicine, agriculture, manufacturing and more. At the same time, increasingly powerful genetically engineered systems could yield unintended consequences for people, food crops, livestock, and industry. These incredible advances in capability demand tools that support accountable innovation.  Genetic engineering attribution is the process of identifying the source of a genetically engineered piece of DNA. This ability ensures that scientists who have spent countless hours developing breakthrough technology get their due credit, intellectual property is protected, and responsible innovation is promoted. By connecting a genetically engineered system with its designers, s...","https://s3.amazonaws.com/drivendata-public-assets/al-green-homepage.jpg","https://www.drivendata.org/competitions/63/genetic-engineering-attribution/","completed","19","","\N","2020-10-19","2023-12-06 08:54:24","2023-12-06 8:56:29"
+"477","neural-latents-benchmark-21","Neural Latents Benchmark '21","A benchmark on co-smoothing or inference of firing rates of unseen neurons","Advances in neural recording present increasing opportunities to study neural activity in unprecedented detail. Latent variable models (LVMs) are promising tools for analyzing this rich activity across diverse neural systems and behaviors, as LVMs do not depend on known relationships between the activity and external experimental variables. To coordinate LVM modeling efforts, we introduce the Neural Latents Benchmark (NLB). The first benchmark suite, NLB 2021, evaluates models on 7 datasets of neural spiking activity spanning 4 tasks and brain areas.","https://neurallatents.github.io/logo.svg","https://eval.ai/web/challenges/challenge-page/1256/overview","completed","16","","\N","2022-04-03","2023-12-12 18:31:00","2023-12-12 22:39:42"
+"478","brain-to-text-benchmark-24","Brain-to-Text Benchmark '24","Develop new and improved algorithms for decoding speech from the brain","People with ALS or brainstem stroke can lose the ability to move, rendering them “locked-in” their own bodies and unable to communicate. Speech brain-computer interfaces (BCIs) can restore communication by decoding what someone is trying to say directly from their brain activity. Once deciphered, the person’s intended message can be spoken for them or typed as text on a computer.  We recently showed that a speech BCI can decode speech at 62 words per minute with a 23% word error rate, demonstrating the potential of a high-performance speech BCI. Nevertheless, word error rates are not yet low enough for fluent communication.  The goal of this competition is to foster the development of new and improved algorithms for decoding speech from the brain. Improved accuracies will make it more likely that a speech BCI can be clinically translated, improving the lives of those with paralysis. We hope that this baseline can also serve as an indicator of progress in the field and provide a st...","https://evalai.s3.amazonaws.com/media/logos/35b2c474-c1be-41ae-97a4-49446766f9b1.png","https://eval.ai/web/challenges/challenge-page/2099/overview","active","16","","2023-06-01","2024-06-01","2023-12-12 21:54:25","2023-12-12 22:38:33"
+"479","vqa-answertherapy-2024","VQA-AnswerTherapy 2024","Grounding all answers for each visual question","Visual Question Answering (VQA) is a task of predicting the answer to a question about an image. Given that different people can provide different answers to a visual question, we aim to better understand why with answer groundings. To achieve this goal, we introduce the VQA-AnswerTherapy dataset, the first dataset that visually grounds each unique answer to each visual question. We offer this work as a valuable foundation for improving our understanding and handling of annotator differences. This work can inform how to account for annotator differences for other related tasks such as image captioning, visual dialog, and open-domain VQA (e.g., VQAs found on Yahoo!Answers and Stack Exchange). This work also contributes to ethical AI by enabling revisiting how VQA models are developed and evaluated to consider the diversity of plausible answer groundings rather than a single (typically majority) one.","https://evalai.s3.amazonaws.com/media/logos/e63bc0a0-cd35-4418-b32b-4ef2b9c61ce2.png","https://eval.ai/web/challenges/challenge-page/1910/overview","upcoming","16","","2024-01-30","\N","2023-12-12 22:41:48","2023-12-12 23:20:41"
+"480","vqa-challenge-2021","VQA Challenge 2021","Answer open-ended, free-form natural language questions about images","Recent progress in computer vision and natural language processing has demonstrated that lower-level tasks are much closer to being solved. We believe that the time is ripe to pursue higher-level tasks, one of which is Visual Question Answering (VQA), where the goal is to be able to understand the semantics of scenes well enough to be able to answer open-ended, free-form natural language questions (asked by humans) about images.  VQA Challenge 2021 is the 6th edition of the VQA Challenge on the VQA v2.0 dataset introduced in Goyal et al., CVPR 2017. The 2nd, 3rd, 4th and 5th editions of the VQA Challenge were organized in CVPR 2017, CVPR 2018, CVPR 2019 and CVPR 2020 on the VQA v2.0 dataset. The 1st edition of the VQA Challenge was organized in CVPR 2016 on the 1st edition (v1.0) of the VQA dataset introduced in Antol et al., ICCV 2015.","https://evalai.s3.amazonaws.com/media/logos/85d3b99e-b3a7-498a-a142-3325eab17138.png","https://eval.ai/web/challenges/challenge-page/830/overview","completed","16","","2021-02-24","2021-05-07","2023-12-12 22:42:59","2023-12-12 23:00:07"
+"481","ntx-hackathon-2023-sleep-states","NTX Hackathon 2023 - Sleep States","Speculate on possible use-cases of Neurotechnology and BCI","This competition is dedicated to advancing the use of machine learning and deep learning techniques in the realm of Brain-Computer Interface (BCI). It focuses on analyzing EEG data obtained from IDUN Guardian Earbuds.  Electroencephalography (EEG) is a non-invasive method of recording electrical activity in the brain. Its high-resolution, real-time data is crucial in various clinical and consumer applications. In clinical environments, EEG is instrumental in diagnosing and monitoring neurological disorders like epilepsy, sleep disorders, and brain injuries. It's also used for assessing brain function in patients under anesthesia or in comas. The real-time aspect of EEG data is vital for clinicians to make informed decisions about diagnosis and treatment, such as pinpointing the onset and location of a seizure.  Beyond clinical use, EEG has significant applications in understanding human cognition. Researchers utilize EEG to explore cognitive processes including attention, percepti...","https://miniodis-rproxy.lisn.upsaclay.fr/coda-v2-prod-public/logos/2023-12-02-1701542051/06a6dc054e4b/NTXHackathon23-Logo-Black-Blue-2048.png","https://www.codabench.org/competitions/1777/","active","10","","2023-12-01","2023-12-15","2023-12-12 23:22:24","2023-12-12 23:30:24"
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 9d30f496aa..a6e301666c 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
@@ -1117,3 +1117,13 @@
 "1116","476","429","sponsor"
 "1117","476","339","sponsor"
 "1118","476","430","sponsor"
+"1119","477","431","challenge_organizer"
+"1120","478","432","challenge_organizer"
+"1121","480","434","challenge_organizer"
+"1122","480","435","challenge_organizer"
+"1123","480","436","challenge_organizer"
+"1124","480","437","challenge_organizer"
+"1125","480","411","challenge_organizer"
+"1126","480","438","challenge_organizer"
+"1127","481","439","challenge_organizer"
+"1128","481","439","sponsor"
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 eb5ba14845..6ebdbd2a49 100644
--- a/apps/openchallenges/challenge-service/src/main/resources/db/incentives.csv
+++ b/apps/openchallenges/challenge-service/src/main/resources/db/incentives.csv
@@ -607,3 +607,6 @@
 "606","publication","473","2023-12-06 08:35:31"
 "607","monetary","474","2023-12-06 08:45:12"
 "608","monetary","476","2023-12-06 08:54:24"
+"609","monetary","477","2023-12-12 18:31:00"
+"610","other","480","2023-12-12 22:42:59"
+"611","other","481","2023-12-12 23:22:24"
diff --git a/apps/openchallenges/challenge-service/src/main/resources/db/platforms.csv b/apps/openchallenges/challenge-service/src/main/resources/db/platforms.csv
index 602d2efab4..526e8a7f35 100644
--- a/apps/openchallenges/challenge-service/src/main/resources/db/platforms.csv
+++ b/apps/openchallenges/challenge-service/src/main/resources/db/platforms.csv
@@ -7,8 +7,8 @@
 "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","nightingale-os","Nightingale OS","logo/nightingale-os.jpeg","https://app.nightingalescience.org/","2023-08-22 15:58:49","2023-12-06 01:10:23"
-"15","evalai","EvalAI","logo/evalai.png","https://eval.ai/","2023-09-15 16:00:34","2023-12-06 01:09:55"
-"16","cache","CACHE","logo/cache.png","https://cache-challenge.org/","2023-10-16 18:43:36","2023-12-06 01:09:56"
-"17","cameo","CAMEO","logo/cameo.png","https://www.cameo3d.org","2023-11-13 22:47:03","2023-12-06 01:09:56"
-"18","drivendata","DrivenData","logo/drivendata.jpg","https://www.drivendata.org/","2023-11-16 21:57:43","2023-12-06 01:09:57"
+"15","nightingale-os","Nightingale OS","logo/nightingale-os.jpeg","https://app.nightingalescience.org/contests","2023-08-22 15:58:49","2023-12-12 19:05:13"
+"16","evalai","EvalAI","logo/evalai.png","https://eval.ai/","2023-09-15 16:00:34","2023-12-12 18:18:37"
+"17","cache","CACHE","logo/cache.png","https://cache-challenge.org/","2023-10-16 18:43:36","2023-12-06 01:09:56"
+"18","cameo","CAMEO","logo/cameo.png","https://www.cameo3d.org","2023-11-13 22:47:03","2023-12-12 18:18:38"
+"19","drivendata","DrivenData","logo/drivendata.jpg","https://www.drivendata.org/","2023-11-16 21:57:43","2023-12-12 18:18:39"
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 3e0532bc3c..4a7987fc0f 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
@@ -467,3 +467,8 @@
 "466","prediction_file","474","2023-12-06 08:45:12"
 "467","prediction_file","475","2023-12-06 08:52:21"
 "468","prediction_file","476","2023-12-06 08:54:24"
+"469","prediction_file","477","2023-12-12 18:31:00"
+"470","prediction_file","478","2023-12-12 21:54:25"
+"471","prediction_file","479","2023-12-12 22:41:48"
+"472","prediction_file","480","2023-12-12 22:42:59"
+"473","container_image","481","2023-12-12 23:22:24"
diff --git a/apps/openchallenges/challenge-service/src/main/resources/openapi.yaml b/apps/openchallenges/challenge-service/src/main/resources/openapi.yaml
index e89a3690f3..854cce92cd 100644
--- a/apps/openchallenges/challenge-service/src/main/resources/openapi.yaml
+++ b/apps/openchallenges/challenge-service/src/main/resources/openapi.yaml
@@ -679,13 +679,13 @@ components:
       format: date
       nullable: true
       type: string
-    CreatedDatetime:
-      description: Datetime when metadata was added to the OC database.
+    CreatedDateTime:
+      description: Datetime when the object was added to the database.
       example: 2022-07-04T22:19:11Z
       format: date-time
       type: string
-    ModifiedDatetime:
-      description: Datetime when metadata was last modified in the OC database.
+    UpdatedDateTime:
+      description: Datetime when the object was last modified in the database.
       example: 2022-07-04T22:19:11Z
       format: date-time
       type: string
@@ -810,12 +810,12 @@ components:
           minimum: 0
           type: integer
         createdAt:
-          description: Datetime when metadata was added to the OC database.
+          description: Datetime when the object was added to the database.
           example: 2022-07-04T22:19:11Z
           format: date-time
           type: string
         updatedAt:
-          description: Datetime when metadata was last modified in the OC database.
+          description: Datetime when the object was last modified in the database.
           example: 2022-07-04T22:19:11Z
           format: date-time
           type: string
diff --git a/apps/openchallenges/organization-service/src/main/java/org/sagebionetworks/openchallenges/organization/service/model/dto/OrganizationDto.java b/apps/openchallenges/organization-service/src/main/java/org/sagebionetworks/openchallenges/organization/service/model/dto/OrganizationDto.java
index 48b31f2e27..6347cf9572 100644
--- a/apps/openchallenges/organization-service/src/main/java/org/sagebionetworks/openchallenges/organization/service/model/dto/OrganizationDto.java
+++ b/apps/openchallenges/organization-service/src/main/java/org/sagebionetworks/openchallenges/organization/service/model/dto/OrganizationDto.java
@@ -220,7 +220,7 @@ public OrganizationDto createdAt(OffsetDateTime createdAt) {
   }
 
   /**
-   * Datetime when metadata was added to the OC database.
+   * Datetime when the object was added to the database.
    *
    * @return createdAt
    */
@@ -229,7 +229,7 @@ public OrganizationDto createdAt(OffsetDateTime createdAt) {
   @Schema(
       name = "createdAt",
       example = "2022-07-04T22:19:11Z",
-      description = "Datetime when metadata was added to the OC database.",
+      description = "Datetime when the object was added to the database.",
       required = true)
   public OffsetDateTime getCreatedAt() {
     return createdAt;
@@ -245,7 +245,7 @@ public OrganizationDto updatedAt(OffsetDateTime updatedAt) {
   }
 
   /**
-   * Datetime when metadata was last modified in the OC database.
+   * Datetime when the object was last modified in the database.
    *
    * @return updatedAt
    */
@@ -254,7 +254,7 @@ public OrganizationDto updatedAt(OffsetDateTime updatedAt) {
   @Schema(
       name = "updatedAt",
       example = "2022-07-04T22:19:11Z",
-      description = "Datetime when metadata was last modified in the OC database.",
+      description = "Datetime when the object was last modified in the database.",
       required = true)
   public OffsetDateTime getUpdatedAt() {
     return updatedAt;
diff --git a/apps/openchallenges/organization-service/src/main/resources/db/contribution_roles.csv b/apps/openchallenges/organization-service/src/main/resources/db/contribution_roles.csv
index 9d30f496aa..a6e301666c 100644
--- a/apps/openchallenges/organization-service/src/main/resources/db/contribution_roles.csv
+++ b/apps/openchallenges/organization-service/src/main/resources/db/contribution_roles.csv
@@ -1117,3 +1117,13 @@
 "1116","476","429","sponsor"
 "1117","476","339","sponsor"
 "1118","476","430","sponsor"
+"1119","477","431","challenge_organizer"
+"1120","478","432","challenge_organizer"
+"1121","480","434","challenge_organizer"
+"1122","480","435","challenge_organizer"
+"1123","480","436","challenge_organizer"
+"1124","480","437","challenge_organizer"
+"1125","480","411","challenge_organizer"
+"1126","480","438","challenge_organizer"
+"1127","481","439","challenge_organizer"
+"1128","481","439","sponsor"
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 994866db84..c83b8a0f67 100644
--- a/apps/openchallenges/organization-service/src/main/resources/db/organizations.csv
+++ b/apps/openchallenges/organization-service/src/main/resources/db/organizations.csv
@@ -387,6 +387,7 @@
 "406","Biozentrum of the University of Basel","biozentrum","logo/biozentrum.jpg","http://www.biozentrum.unibas.ch/","The Biozentrum of the University of Basel is one of the leading life sciences institutes in the world. The primary focus is basic molecular and biomedical research and teaching.  It’s international and interdisciplinary research groups explore how molecules and cells create life, spanning the scale from atom to organism. Founded in 1971, the Biozentrum has been the birth place of many fundamental discoveries in biology and medicine, spawning several Nobel Laureates.","3","2023-11-13 22:24:00","2023-11-21 00:17:11",""
 "407","National Institute of Aging","nia","logo/nih.png","https://www.nia.nih.gov/","NIA leads a broad scientific effort to understand the nature of aging and to extend the healthy, active years of life. NIA is the primary federal agency supporting and conducting Alzheimer's disease research.","3","2023-11-16 22:06:31","2023-11-21 00:13:03","NIA"
 "408","National Aeronautics and Space Administration","nasa","logo/nasa.jpg","https://www.nasa.gov/","NASA explores the unknown in air and space, innovates for the benefit of humanity, and inspires the world through discovery.","1","2023-11-16 22:08:34","2023-11-21 00:17:31","NASA"
+"411","DeepMind Technologies Limited","deepmind","logo/deepmind.png","","This organization may no longer exist, has rebranded, or has been merged under another organization.","1","2023-11-20 18:58:02","2023-11-21 00:19:11",""
 "414","CDC National Center for Injury Prevention and Control","ncipc","logo/cdc-ncipc.jpg","https://www.cdc.gov/injury/","For 30 years, CDC’s National Center for Injury Prevention and Control (Injury Center) has been a leader in protecting Americans from injury and violence so that individuals, families, and communities can be safe, healthy, and thriving. We work proactively with our partners to track trends, conduct research, raise awareness, and implement prevention programs.","1","2023-12-06 07:22:56","2023-12-06 08:50:23","NCIPC"
 "415","Centers for Disease Control and Prevention","cdc","logo/cdc.jpg","https://www.cdc.gov/injury/","CDC is the nation’s leading science-based, data-driven, service organization that protects the public’s health. For more than 70 years, we’ve put science into action to help children stay healthy so they can grow and learn; to help families, businesses, and communities fight disease and stay strong; and to protect the public’s health.","2","2023-12-06 07:26:44","2023-12-06 08:15:44","CDC"
 "416","United States Consumer Product Safety Commission","cpsc","logo/cpsc.jpg","https://www.cpsc.gov/","CPSC works to save lives and keep families safe by reducing the unreasonable risk of injuries and deaths associated with consumer products and fulfilling its vision to be the recognized global leader in consumer product safety. CPSC does this by: 1) Issuing and enforcing mandatory standards or banning consumer products if no feasible standard would adequately protect the public; 2) Obtaining the recall of products and arranging for a repair, replacement or refund for recalled products; 3) Researching potential product hazards; 4) Developing voluntary standards with standards organizations, manufacturers and businesses; 5) Informing and educating consumers directly and through traditional, online, and social media and by working with foreign, state and local governments and private organizations; and 6) Educating manufacturers worldwide about our regulations, supply chain integrity and development of safe products.","1","2023-12-06 07:29:21","2023-12-06 07:33:21","CPSC"
@@ -404,3 +405,11 @@
 "428","CDC National Survey of Family Growth","nsfg","logo/cdc-nchs.jpg","https://www.cdc.gov/nchs/nsfg/index.htm?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fnchs%2Fnsfg.htm","The National Survey of Family Growth (NSFG) gathers information on pregnancy and births, marriage and cohabitation, infertility, use of contraception, family life, and general and reproductive health. The survey results are used by the U.S. Department of Health and Human Services and others to plan health services and health education programs, and to do statistical studies of families, fertility, and health.","1","2023-12-06 08:49:39","2023-12-06 08:51:01","NSFG"
 "429","altLabs","altlabs","logo/altlabs.jpg","https://altlabs.tech/","altLabs is a research non-profit institution focused on the development and advancement of safety-promoting technologies. We collaborate with world-class researchers across diverse scientific fields to prioritize thoughtful solutions with long-term positive impact on a global scale. In a nutshell, we’re working to make the world a safer place.","1","2023-12-06 08:56:57","2023-12-06 08:59:34",""
 "430","iGEM","igem","logo/igem.jpg","https://igem.org/","iGEM educates the workforce and the leaders of the Synthetic Biology industry. The iGEM Foundation is an independent, non-profit organization dedicated to the advancement of synthetic biology, education and competition, and the development of an open, collaborative, and cooperative community.","1","2023-12-06 08:58:34","2023-12-06 08:59:36",""
+"431","Systems Neural Engineering Lab","snel","logo/snel.png","https://snel.ai/","Our lab at Emory University and the Georgia Institute of Technology sits at the intersection of systems neuroscience, neural engineering, and artificial intelligence, with dual goals of better understanding the nervous system and developing high-performing and robust brain-computer interfaces for people with paralysis.","1","2023-12-12 18:54:15","2023-12-12 18:56:43","SNEL"
+"432","Neural Prosthetics Translational Lab","nptl","logo/stanford.jpg","https://nptl.stanford.edu/","We conduct neuroscience, neuroengineering and translational research to better understand how the brain controls movement and to then design medical systems to assist people with paralysis. These medical systems are referred to as brain-computer interfaces (BCIs), brain-machine interfaces (BMIs) and intra-cortical neural prostheses. We conduct this research in our Neural Prosthetics Translational Lab (NPTL) which focuses on fundamental human neuroscience and translational research with people with paralysis. Prof. Jaimie Henderson and Prof. Krishna Shenoy are the PIs of the NPTL.","1","2023-12-12 21:54:51","2023-12-12 21:56:13","NPTL"
+"434","Georgia Institute of Technology","ga-tech","logo/gatech.jpg","https://www.gatech.edu/","The Georgia Institute of Technology is a leading research university committed to improving the human condition through advanced science and technology.","1","2023-12-12 23:01:56","2023-12-12 23:08:44",""
+"435","Samsung Advanced Institute of Technology AI Lab Montreal","sail-montreal","logo/samsung.jpg","https://www.sait.samsung.co.kr/saithome/main/main.do","SAIL Montreal (SAIT AI Lab Montreal) is a recently established academic-style research lab (in close collaboration with Mila) whose mission is to advance our fundamental understanding of deep learning technology and its applications. Headed by Simon Lacoste-Julien, professor in computer science at Université de Montréal and co-founding member of Mila, SAIL is located in Mila's corporate space at the heart of the Montreal AI ecosystem nearby Element AI, Borealis AI, FAIR, Microsoft Research and others where an open collaborative environment is encouraged.","1","2023-12-12 23:06:45","2023-12-12 23:11:43",""
+"436","Facebook AI Research","meta-ai","logo/meta.png","https://ai.meta.com/research/","(Renamed from Facebook AI Research). The Fundamental AI Research (FAIR) team at Meta seeks to further our fundamental understanding in both new and existing domains, covering the full spectrum of topics related to AI, with the mission of advancing the state-of-the-art of AI through open research for the benefit of all.  AI at Meta engages in cutting-edge applied research that can improve and power new product experiences at huge scale for our community. Building on AI at Meta's key principles of openness, collaboration, excellence, and scale, we make big, bold research investments focused on pushing the boundaries of AI to create a more connected world.","1","2023-12-12 23:11:50","2023-12-12 23:16:14",""
+"437","Université de Montréal","udem","logo/udem.png","https://www.umontreal.ca/en/","With its roots in Montreal and its eyes on the international horizon, the Université de Montréal is one of the world’s leading research universities.","1","2023-12-12 23:16:51","2023-12-12 23:18:13","UdeM"
+"438","Mila","mila","logo/mila.jpg","https://mila.quebec/en/","Located in the heart of Quebec’s AI ecosystem, Mila is a community of more than 1,200 researchers specializing in machine learning and dedicated to scientific excellence and innovation.","1","2023-12-12 23:19:27","2023-12-12 23:20:00",""
+"439","NeuroTechX","ntx","logo/ntx.jpg","https://neurotechx.com/","NeuroTechX is a non-profit organization whose mission is to facilitate the advancement of neurotechnology by providing key resources and learning opportunities, and by being leaders in local and worldwide technological initiatives. Our 3 pillars are “Community”, “Education”, and “Professional Development”.","1","2023-12-12 23:27:39","2023-12-12 23:28:16","NTX"
diff --git a/apps/openchallenges/organization-service/src/main/resources/openapi.yaml b/apps/openchallenges/organization-service/src/main/resources/openapi.yaml
index 93fb244921..97911d0168 100644
--- a/apps/openchallenges/organization-service/src/main/resources/openapi.yaml
+++ b/apps/openchallenges/organization-service/src/main/resources/openapi.yaml
@@ -271,13 +271,13 @@ components:
       maxLength: 500
       nullable: true
       type: string
-    CreatedDatetime:
-      description: Datetime when metadata was added to the OC database.
+    CreatedDateTime:
+      description: Datetime when the object was added to the database.
       example: 2022-07-04T22:19:11Z
       format: date-time
       type: string
-    ModifiedDatetime:
-      description: Datetime when metadata was last modified in the OC database.
+    UpdatedDateTime:
+      description: Datetime when the object was last modified in the database.
       example: 2022-07-04T22:19:11Z
       format: date-time
       type: string
@@ -333,12 +333,12 @@ components:
           minimum: 0
           type: integer
         createdAt:
-          description: Datetime when metadata was added to the OC database.
+          description: Datetime when the object was added to the database.
           example: 2022-07-04T22:19:11Z
           format: date-time
           type: string
         updatedAt:
-          description: Datetime when metadata was last modified in the OC database.
+          description: Datetime when the object was last modified in the database.
           example: 2022-07-04T22:19:11Z
           format: date-time
           type: string
diff --git a/libs/openchallenges/api-client-angular/src/lib/model/challenge.ts b/libs/openchallenges/api-client-angular/src/lib/model/challenge.ts
index 31a0980c80..de083683cf 100644
--- a/libs/openchallenges/api-client-angular/src/lib/model/challenge.ts
+++ b/libs/openchallenges/api-client-angular/src/lib/model/challenge.ts
@@ -70,11 +70,11 @@ export interface Challenge {
      */
     starredCount: number;
     /**
-     * Datetime when metadata was added to the OC database.
+     * Datetime when the object was added to the database.
      */
     createdAt: string;
     /**
-     * Datetime when metadata was last modified in the OC database.
+     * Datetime when the object was last modified in the database.
      */
     updatedAt: string;
 }
diff --git a/libs/openchallenges/api-client-angular/src/lib/model/organization.ts b/libs/openchallenges/api-client-angular/src/lib/model/organization.ts
index 97e3e51578..10ce28c364 100644
--- a/libs/openchallenges/api-client-angular/src/lib/model/organization.ts
+++ b/libs/openchallenges/api-client-angular/src/lib/model/organization.ts
@@ -41,11 +41,11 @@ export interface Organization {
      */
     challengeCount?: number;
     /**
-     * Datetime when metadata was added to the OC database.
+     * Datetime when the object was added to the database.
      */
     createdAt: string;
     /**
-     * Datetime when metadata was last modified in the OC database.
+     * Datetime when the object was last modified in the database.
      */
     updatedAt: string;
     /**
diff --git a/libs/openchallenges/api-description/build/challenge.openapi.yaml b/libs/openchallenges/api-description/build/challenge.openapi.yaml
index 253f2b28d3..3e0f2c336a 100644
--- a/libs/openchallenges/api-description/build/challenge.openapi.yaml
+++ b/libs/openchallenges/api-description/build/challenge.openapi.yaml
@@ -460,13 +460,13 @@ components:
       format: date
       nullable: true
       example: '2017-07-21'
-    CreatedDatetime:
-      description: Datetime when metadata was added to the OC database.
+    CreatedDateTime:
+      description: Datetime when the object was added to the database.
       type: string
       format: date-time
       example: '2022-07-04T22:19:11Z'
-    ModifiedDatetime:
-      description: Datetime when metadata was last modified in the OC database.
+    UpdatedDateTime:
+      description: Datetime when the object was last modified in the database.
       type: string
       format: date-time
       example: '2022-07-04T22:19:11Z'
@@ -517,9 +517,9 @@ components:
           minimum: 0
           example: 100
         createdAt:
-          $ref: '#/components/schemas/CreatedDatetime'
+          $ref: '#/components/schemas/CreatedDateTime'
         updatedAt:
-          $ref: '#/components/schemas/ModifiedDatetime'
+          $ref: '#/components/schemas/UpdatedDateTime'
       required:
         - id
         - slug
diff --git a/libs/openchallenges/api-description/build/openapi.yaml b/libs/openchallenges/api-description/build/openapi.yaml
index df6b59252f..46edf97e21 100644
--- a/libs/openchallenges/api-description/build/openapi.yaml
+++ b/libs/openchallenges/api-description/build/openapi.yaml
@@ -612,13 +612,13 @@ components:
       format: date
       nullable: true
       example: '2017-07-21'
-    CreatedDatetime:
-      description: Datetime when metadata was added to the OC database.
+    CreatedDateTime:
+      description: Datetime when the object was added to the database.
       type: string
       format: date-time
       example: '2022-07-04T22:19:11Z'
-    ModifiedDatetime:
-      description: Datetime when metadata was last modified in the OC database.
+    UpdatedDateTime:
+      description: Datetime when the object was last modified in the database.
       type: string
       format: date-time
       example: '2022-07-04T22:19:11Z'
@@ -669,9 +669,9 @@ components:
           minimum: 0
           example: 100
         createdAt:
-          $ref: '#/components/schemas/CreatedDatetime'
+          $ref: '#/components/schemas/CreatedDateTime'
         updatedAt:
-          $ref: '#/components/schemas/ModifiedDatetime'
+          $ref: '#/components/schemas/UpdatedDateTime'
       required:
         - id
         - slug
@@ -1093,9 +1093,9 @@ components:
           default: 0
           example: 10
         createdAt:
-          $ref: '#/components/schemas/CreatedDatetime'
+          $ref: '#/components/schemas/CreatedDateTime'
         updatedAt:
-          $ref: '#/components/schemas/ModifiedDatetime'
+          $ref: '#/components/schemas/UpdatedDateTime'
         acronym:
           description: An acronym of the organization.
           type: string
diff --git a/libs/openchallenges/api-description/build/organization.openapi.yaml b/libs/openchallenges/api-description/build/organization.openapi.yaml
index c4199afc42..3f25b7a4cb 100644
--- a/libs/openchallenges/api-description/build/organization.openapi.yaml
+++ b/libs/openchallenges/api-description/build/organization.openapi.yaml
@@ -180,13 +180,13 @@ components:
       maxLength: 500
       nullable: true
       example: https://openchallenges.io
-    CreatedDatetime:
-      description: Datetime when metadata was added to the OC database.
+    CreatedDateTime:
+      description: Datetime when the object was added to the database.
       type: string
       format: date-time
       example: '2022-07-04T22:19:11Z'
-    ModifiedDatetime:
-      description: Datetime when metadata was last modified in the OC database.
+    UpdatedDateTime:
+      description: Datetime when the object was last modified in the database.
       type: string
       format: date-time
       example: '2022-07-04T22:19:11Z'
@@ -219,9 +219,9 @@ components:
           default: 0
           example: 10
         createdAt:
-          $ref: '#/components/schemas/CreatedDatetime'
+          $ref: '#/components/schemas/CreatedDateTime'
         updatedAt:
-          $ref: '#/components/schemas/ModifiedDatetime'
+          $ref: '#/components/schemas/UpdatedDateTime'
         acronym:
           description: An acronym of the organization.
           type: string
diff --git a/libs/openchallenges/api-description/src/components/schemas/Challenge.yaml b/libs/openchallenges/api-description/src/components/schemas/Challenge.yaml
index 604c1db5b5..9aa5b8e0ba 100644
--- a/libs/openchallenges/api-description/src/components/schemas/Challenge.yaml
+++ b/libs/openchallenges/api-description/src/components/schemas/Challenge.yaml
@@ -60,9 +60,9 @@ properties:
     minimum: 0
     example: 100
   createdAt:
-    $ref: CreatedDatetime.yaml
+    $ref: CreatedDateTime.yaml
   updatedAt:
-    $ref: ModifiedDatetime.yaml
+    $ref: UpdatedDateTime.yaml
 required:
   - id
   - slug
diff --git a/libs/openchallenges/api-description/src/components/schemas/CreatedDateTime.yaml b/libs/openchallenges/api-description/src/components/schemas/CreatedDateTime.yaml
new file mode 100644
index 0000000000..09217eca80
--- /dev/null
+++ b/libs/openchallenges/api-description/src/components/schemas/CreatedDateTime.yaml
@@ -0,0 +1,4 @@
+description: Datetime when the object was added to the database.
+type: string
+format: date-time
+example: '2022-07-04T22:19:11Z'
diff --git a/libs/openchallenges/api-description/src/components/schemas/CreatedDatetime.yaml b/libs/openchallenges/api-description/src/components/schemas/CreatedDatetime.yaml
deleted file mode 100644
index bf8f871cd7..0000000000
--- a/libs/openchallenges/api-description/src/components/schemas/CreatedDatetime.yaml
+++ /dev/null
@@ -1,4 +0,0 @@
-description: Datetime when metadata was added to the OC database.
-type: string
-format: date-time
-example: '2022-07-04T22:19:11Z'
diff --git a/libs/openchallenges/api-description/src/components/schemas/ModifiedDatetime.yaml b/libs/openchallenges/api-description/src/components/schemas/ModifiedDatetime.yaml
deleted file mode 100644
index 698d40703a..0000000000
--- a/libs/openchallenges/api-description/src/components/schemas/ModifiedDatetime.yaml
+++ /dev/null
@@ -1,4 +0,0 @@
-description: Datetime when metadata was last modified in the OC database.
-type: string
-format: date-time
-example: '2022-07-04T22:19:11Z'
diff --git a/libs/openchallenges/api-description/src/components/schemas/Organization.yaml b/libs/openchallenges/api-description/src/components/schemas/Organization.yaml
index 041bfcabbe..91c50bfe47 100644
--- a/libs/openchallenges/api-description/src/components/schemas/Organization.yaml
+++ b/libs/openchallenges/api-description/src/components/schemas/Organization.yaml
@@ -26,9 +26,9 @@ properties:
     default: 0
     example: 10
   createdAt:
-    $ref: CreatedDatetime.yaml
+    $ref: CreatedDateTime.yaml
   updatedAt:
-    $ref: ModifiedDatetime.yaml
+    $ref: UpdatedDateTime.yaml
   acronym:
     description: An acronym of the organization.
     type: string
diff --git a/libs/openchallenges/api-description/src/components/schemas/UpdatedDateTime.yaml b/libs/openchallenges/api-description/src/components/schemas/UpdatedDateTime.yaml
new file mode 100644
index 0000000000..a8895865a1
--- /dev/null
+++ b/libs/openchallenges/api-description/src/components/schemas/UpdatedDateTime.yaml
@@ -0,0 +1,4 @@
+description: Datetime when the object was last modified in the database.
+type: string
+format: date-time
+example: '2022-07-04T22:19:11Z'