From f350e4330269e8af70fd0ea6c9993b9db457c686 Mon Sep 17 00:00:00 2001
From: Verena Chung <9377970+vpchung@users.noreply.github.com>
Date: Mon, 6 Nov 2023 10:54:07 -0800
Subject: [PATCH] update challenge status (#2326)

---
 .../challenge-service/src/main/resources/db/challenges.csv    | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv b/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv
index 9113d79eb9..49ee418ded 100644
--- a/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv
+++ b/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv
@@ -276,7 +276,7 @@
 "275","cagi6-invitae","CAGI6: Sherloc clinical classification","Over 122,000 coding (missense, silent, frameshift, stop gained, in-frame cod...","Invitae is a genetic testing company that publishes their variant interpretations to ClinVar. In this challenge, over 122,000 previously uncharacterized variants are provided, spanning the range of effects seen in the clinic. Following the close of this challenge, Invitae will submit their interpretations for these variants to ClinVar. Predictors are asked to interpret the pathogenicity of these variants, and the clinical utility of predictions will be assessed across multiple categories by Invitae.","","https://genomeinterpretation.org/cagi6-invitae.html","completed","intermediate","1","","2021-07-08","2021-12-01","2023-06-23 00:00:00","2023-10-12 18:12:31"
 "276","cagi6-splicing-vus","CAGI6: Splicing VUS","Predict whether the experimentally validated variants of unknown significanc...","Variants causing aberrant splicing have been implicated in a range of common and rare disorders, including retinitis pigmentosa, autism spectrum disorder, amyotrophic lateral sclerosis, and a variety of cancers. However, such variants are frequently overlooked by diagnostic sequencing pipelines, leading to missed diagnoses for patients. Clinically ascertained variants of unknown significance underwent whole-blood based RT-PCR to test for impact on splicing. The challenge is to predict which of the tested variants disrupt splicing.","","https://genomeinterpretation.org/cagi6-splicing-vus.html","completed","intermediate","1","","2021-06-08","2021-10-11","2023-06-23 00:00:00","2023-10-12 18:12:34"
 "277","cagi6-stk11","CAGI6: STK11","Participants are asked to submit predictions on the impact of the variants l...","Serine/Threonine Kinase 11 (STK11) is considered a master kinase that functions as a tumor suppressor and nutrient sensor within a heterotrimeric complex with pseudo-kinase STRAD-alpha and structural protein MO25. Germline variants resulting in loss of STK11 define Peutz-Jaghers Syndrome, an autosomal dominant cancer predisposition syndrome marked by gastrointestinal hamartomas and freckling of the oral mucosa. Somatic loss of function variants, both nonsense and missense, occur in 15-30% of non-small cell lung adenocarcinomas, where they correlate clinically with insensitivity to anti-PD1 monoclonal antibody therapy. The challenge is to predict the impact on STK11 function for each missense variant in relation to wildtype STK11.","","https://genomeinterpretation.org/cagi6-stk11.html","completed","intermediate","1","","2021-06-08","2021-09-01","2023-06-23 00:00:00","2023-10-12 18:12:38"
-"278","qbi-hackathon","QBI hackathon","A 48-hour event connecting the Bay Area developer community with scientists ...","The QBI hackathon is a 48-hour event connecting the vibrant Bay Area developer community with the scientists from UCSF, UCB and UCSC, during which we work together on the cutting edge biomedical problems. Advances in computer vision, AI, and machine learning have enabled computers to pick out cat videos, recognize people’s faces from photos, play video games and drive cars. More recently, application of deep neural nets to protein structure prediction completely revolutionized the field. We look forward to seeing how far we can push science ahead when we apply these latest algorithms to biomedically relevant light microscopy, electron microscopy, and proteomics data. If you love FFTs, transformers, language models, topological data processing, or simply writing code, this is your chance to apply your skills to make an impact on global healthcare. Beyond the actual event, we hope to establish a better connection between talented developers and scientists in the Bay Area, so that we...","","https://www.eventbrite.com/e/qbi-hackathon-2023-tickets-633794304827?aff=oddtdtcreator","upcoming","intermediate","14","","2023-11-04","2023-11-05","2023-10-06 21:22:51","2023-10-26 23:23:36"
-"279","niddk-central-repository-data-centric-challenge","NIDDK Central Repository Data-Centric Challenge","Enhancing NIDDK datasets for future Artificial Intelligence (AI) application...","The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repository (https://repository.niddk.nih.gov/home/) is conducting a Data Centric Challenge aimed at augmenting existing Repository data for future secondary research including data-driven discovery by artificial intelligence (AI) researchers. The NIDDK Central Repository (NIDDK-CR) program strives to increase the utilization and impact of the resources under its guardianship. However, lack of standardization and consistent metadata within and across studies limit the ability of secondary researchers to easily combine datasets from related studies to generate new insights using data science methods. In the fall of 2021, the NIDDK-CR began implementing approaches to augment data quality to improve AI-readiness by making research data FAIR (findable, accessible, interoperable, and reusable) via a small pilot project utilizing Natural Language Processing (NLP) to tag study variables. In 2022, the NIDD...","","https://www.challenge.gov/?challenge=niddk-central-repository-data-centric-challenge","active","intermediate","14","","2023-09-20","2023-11-03","2023-10-18 16:58:17","2023-10-18 20:52:49"
+"278","qbi-hackathon","QBI hackathon","A 48-hour event connecting the Bay Area developer community with scientists ...","The QBI hackathon is a 48-hour event connecting the vibrant Bay Area developer community with the scientists from UCSF, UCB and UCSC, during which we work together on the cutting edge biomedical problems. Advances in computer vision, AI, and machine learning have enabled computers to pick out cat videos, recognize people’s faces from photos, play video games and drive cars. More recently, application of deep neural nets to protein structure prediction completely revolutionized the field. We look forward to seeing how far we can push science ahead when we apply these latest algorithms to biomedically relevant light microscopy, electron microscopy, and proteomics data. If you love FFTs, transformers, language models, topological data processing, or simply writing code, this is your chance to apply your skills to make an impact on global healthcare. Beyond the actual event, we hope to establish a better connection between talented developers and scientists in the Bay Area, so that we...","","https://www.eventbrite.com/e/qbi-hackathon-2023-tickets-633794304827?aff=oddtdtcreator","completed","intermediate","14","","2023-11-04","2023-11-05","2023-10-06 21:22:51","2023-10-26 23:23:36"
+"279","niddk-central-repository-data-centric-challenge","NIDDK Central Repository Data-Centric Challenge","Enhancing NIDDK datasets for future Artificial Intelligence (AI) application...","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","intermediate","14","","2023-09-20","2023-11-03","2023-10-18 16:58:17","2023-10-18 20:52:49"
 "280","stanford-ribonanza-rna-folding","Stanford Ribonanza RNA Folding","Pioneering RNA Science: A Path to Programmable Medicine and Scientific Break...","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","active","intermediate","8","","2023-08-23","2023-11-24","2023-10-23 20:58:06","2023-11-02 18:01:38"
 "281","uls23","Universal Lesion Segmentation '23 Challenge","Revolutionizing Lesion Segmentation: Advancements, Challenges, and a Univers...","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.  For these reasons we are excited to host the ULS23 Challenge...","","https://uls23.grand-challenge.org/","active","intermediate","5","","2023-10-29","2024-03-17","2023-11-02 15:35:22","2023-11-02 18:02:48"