From 18527ae419519e76851d106810b39bbc94c621c8 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Mon, 18 Mar 2024 09:33:33 -0700 Subject: [PATCH] chore(openchallenges): 2024-03-18 DB update (#2571) Co-authored-by: vpchung <9377970+vpchung@users.noreply.github.com> --- .../src/main/resources/db/challenges.csv | 10 +- .../src/main/resources/db/input_data_type.csv | 306 +++++++++++++++++- 2 files changed, 309 insertions(+), 7 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 f2eb462ad5..9faf6b4c06 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv +++ b/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv @@ -2,7 +2,7 @@ "1","network-topology-and-parameter-inference","Network Topology and Parameter Inference","Optimize methods to estimate biology model parameters","Participants are asked to develop and/or apply optimization methods, including the selection of the most informative experiments, to accurately estimate parameters and predict outcomes of perturbations in Systems Biology models.","","https://www.synapse.org/#!Synapse:syn2821735","completed","1","","2012-06-01","2012-10-01","\N","2023-11-15 22:40:15","2024-03-04 18:31:19" "2","breast-cancer-prognosis","Breast Cancer Prognosis","Predict breast cancer survival from clinical and genomic data","The goal of the breast cancer prognosis Challenge is to assess the accuracy of computational models designed to predict breast cancer survival, based on clinical information about the patient's tumor as well as genome-wide molecular profiling data including gene expression and copy number profiles.","","https://www.synapse.org/#!Synapse:syn2813426","completed","1","","2012-07-12","2012-10-15","\N","2023-11-14 20:36:32","2024-02-19 18:17:47" "3","phil-bowen-als-prediction-prize4life","Phil Bowen ALS Prediction Prize4Life","Seeking treatment to halt ALS's fatal loss of motor function","Amyotrophic Lateral Sclerosis (ALS), or Lou Gehrig's disease, is a fatal neurological condition causing the death of nerve cells in the brain and spinal cord, resulting in a progressive loss of motor function while cognitive functions persist. Typically emerging around age 50, it affects about five in 100,000 people worldwide, with familial hereditary forms as the only known risk factors (5-10% of cases). There is currently no cure for ALS. The FDA-approved drug Riluzole extends life by a few months. ALS patients, on average, have a life expectancy of 2-5 years, with 10% experiencing slower disease progression. Astrophysicist Stephen Hawking, living with ALS for 49 years, is an exceptional case. The DREAM-Phil Bowen ALS Prediction Prize4Life, or ""ALS Prediction Prize,"" utilizes the PRO-ACT database with clinical data from over 7,500 ALS patients. This collaboration with DREAM aims to expedite ALS treatment discovery. Prize4Life, a non-profit, collaborates with NEALS and ALS Ther...","","https://www.synapse.org/#!Synapse:syn2826267","completed","1","","2012-06-01","2012-10-01","\N","2023-11-01 22:09:02","2024-02-19 18:18:00" -"4","drug-sensitivity-and-drug-synergy-prediction","Drug Sensitivity and Drug Synergy Prediction","Predicting drug sensitivity in human cell lines","Development of new cancer therapeutics currently requires a long and protracted process of experimentation and testing. Human cancer cell lines represent a good model to help identify associations between molecular subtypes, pathways, and drug response. In recent years there have been several efforts to generate genomic profiles of collections of cell lines and to determine their response to panels of candidate therapeutic compounds. These data provide the basis for the development of in silico models of sensitivity based either on the unperturbed genetic potential of a cancer cell, or by using perturbation data to incorporate knowledge of actual cell response. Making predictions from either of these data profiles will be beneficial in identifying single and combinatorial chemotherapeutic response in patients. To that end, the present challenge seeks computational methods, derived from the molecular profiling of cell lines both in a static state and in response to perturbation of ...","","https://www.synapse.org/#!Synapse:syn2785778","completed","1","","2012-06-01","2012-10-01","2812","2023-11-01 22:08:36","2024-03-04 18:31:14" +"4","drug-sensitivity-and-drug-synergy-prediction","Drug Sensitivity and Drug Synergy Prediction","Predicting drug sensitivity in human cell lines","Development of new cancer therapeutics currently requires a long and protracted process of experimentation and testing. Human cancer cell lines represent a good model to help identify associations between molecular subtypes, pathways, and drug response. In recent years there have been several efforts to generate genomic profiles of collections of cell lines and to determine their response to panels of candidate therapeutic compounds. These data provide the basis for the development of in silico models of sensitivity based either on the unperturbed genetic potential of a cancer cell, or by using perturbation data to incorporate knowledge of actual cell response. Making predictions from either of these data profiles will be beneficial in identifying single and combinatorial chemotherapeutic response in patients. To that end, the present challenge seeks computational methods, derived from the molecular profiling of cell lines both in a static state and in response to perturbation of ...","","https://www.synapse.org/#!Synapse:syn2785778","completed","1","","2012-06-01","2012-10-01","2813","2023-11-01 22:08:36","2024-03-04 18:31:14" "5","niehs-ncats-unc-toxicogenetics","NIEHS-NCATS-UNC Toxicogenetics","Predicting cytotoxicity from genomic and chemical data","This challenge is designed to build predictive models of cytotoxicity as mediated by exposure to environmental toxicants and drugs. To approach this question, we will provide a dataset containing cytotoxicity estimates as measured in lymphoblastoid cell lines derived from 884 individuals following in vitro exposure to 156 chemical compounds. In subchallenge 1, participants will be asked to model interindividual variability in cytotoxicity based on genomic profiles in order to predict cytotoxicity in unknown individuals. In subchallenge 2, participants will be asked to predict population-level parameters of cytotoxicity across chemicals based on structural attributes of compounds in order to predict median cytotoxicity and mean variance in toxicity for unknown compounds.","","https://www.synapse.org/#!Synapse:syn1761567","completed","1","","2013-06-10","2013-09-15","\N","2023-11-01 22:08:45","2023-11-01 22:06:01" "6","whole-cell-parameter-estimation","Whole-Cell Parameter Estimation","Seeking innovative parameter estimation methods for large models","The goal of this challenge is to explore and compare innovative approaches to parameter estimation of large, heterogeneous computational models. Participants are encouraged to develop and/or apply optimization methods, including the selection of the most informative experiments. The organizers encourage participants to form teams to collaboratively solve the challenge.","","https://www.synapse.org/#!Synapse:syn1876068","completed","1","","2013-06-10","2013-09-23","\N","2023-06-23 00:00:00","2023-11-01 22:06:23" "7","hpn-dream-breast-cancer-network-inference","HPN-DREAM Breast Cancer Network Inference","Inferring causal signaling networks in breast cancer","The overall goal of the Heritage-DREAM breast cancer network inference challenge is to quickly and effectively advance our ability to infer causal signaling networks and predict protein phosphorylation dynamics in cancer. We provide extensive training data from experiments on four breast cancer cell lines stimulated with various ligands. The data comprise protein abundance time-courses under inhibitor perturbations.","","https://www.synapse.org/#!Synapse:syn1720047","completed","1","","2013-06-10","2013-09-16","\N","2023-06-23 00:00:00","2023-11-13 17:15:59" @@ -33,8 +33,8 @@ "32","malaria","Malaria","Predict malaria drug resistance from parasite gene expression for malaria","The Malaria DREAM Challenge is open to anyone interested in contributing to the development of computational models that address important problems in advancing the fight against malaria. The overall goal of the first Malaria DREAM Challenge is to predict Artemisinin (Art) drug resistance level of a test set of malaria parasites using their in vitro transcription data and a training set consisting of published in vivo and unpublished in vitrotranscriptomes. The in vivodataset consists of ~1000 transcription samples from various geographic locations covering a wide range of life cycles and resistance levels, with other accompanying data such as patient age, geographic location, Art combination therapy used, etc [Mok et al (2015) Science]. The in vitro transcription dataset consists of 55 isolates, with transcription collected at two timepoints (6 and 24 hours post-invasion), in the absence or presence of an Art perturbation, for two biological replicates using a custom microarray a...","","https://www.synapse.org/#!Synapse:syn16924919","completed","1","","2019-04-30","2019-08-15","\N","2023-06-23 00:00:00","2023-10-14 05:38:35" "33","preterm-birth-prediction-transcriptomics","Preterm Birth Prediction - Transcriptomics","Determine gestational age for preterm birth prediction","A basic need in pregnancy care is to establish gestational age, and inaccurate estimates may lead to unnecessary interventions and sub-optimal patient management. Current approaches to establish gestational age rely on patient's recollection of her last menstrual period and/or ultrasound, with the latter being not only costly but also less accurate if not performed during the first trimester of pregnancy. Therefore development of an inexpensive and accurate molecular clock of pregnancy would be of benefit to patients and health care systems. Participants in sub-challenge 1 (Prediction of gestational age) will be given whole blood gene topic_3170 collected from pregnant women to develop prediction models for the gestational age at blood draw. Another challenge in obstetrics, in both low and high-income countries, is identification and treatment of women at risk of developing the ‘great obstetrical syndromes‘. Of these, preterm birth (PTB), defined as giving birth prior to completio...","","https://www.synapse.org/#!Synapse:syn18380862","completed","1","","2019-05-04","2019-12-05","\N","2023-06-23 00:00:00","2023-11-14 19:07:28" "34","single-cell-signaling-in-breast-cancer","Single-Cell Signaling in Breast Cancer","Exploring heterogeneous signaling in single cancer cells","Signaling underlines nearly every cellular event. Individual cells, even if genetically identical, respond to perturbation in different ways. This underscores the relevance of cellular heterogeneity, in particular in how cells respond to drugs. This is of high relevance since the fact that a subset of cells do not respond (or only weakly) to drugs can render this drug an ineffective treatment. In spite of its relevance to many diseases, comprehensive studies on the heterogeneous signaling in single cells are still lacking. We have generated the, to our knowledge, currently largest single cell signaling dataset on a panel of 67 well-characterized breast cancer cell lines by mass cytometry (3'015 conditions, ~80 mio single cells, 38 markers; Bandura et al. 2009; Bendall et al., 2011; Bodenmiller et al., 2012; Lun et al., 2017; Lun et al., 2019). These cell lines are, among others, also characterized at the genomic, transcriptomic, and proteomic level (Marcotte et al., 2016). We ask ...","","https://www.synapse.org/#!Synapse:syn20366914","completed","1","","2018-08-20","2019-11-15","\N","2023-06-23 00:00:00","2023-10-14 05:38:37" -"35","ehr-dream-challenge-patient-mortality-prediction","EHR DREAM Challenge: Patient Mortality Prediction","New tools to reconstruct cell lineages from CRISPR mutations","The recent advent of new CRISPR-based molecular tools allows the reconstruction of cell lineages based on the phylogenetical analysis of DNA mutations induced by CRISPR during development and promises to solve the lineage of complex model organisms at single-cell resolution (see image from McKenna et al Science 2016). To date, however, no lineage reconstruction algorithms have been rigorously examined for their performance/robustness across diverse molecular tools, datasets, and number of cells/size of lineage trees. It also remains unclear whether new Machine-Learning algorithms that go beyond the classical ones developed for reconstructing phylogenetic trees, could consistently reconstruct cell lineages to a high degree of accuracy. The challenge-a partnership between The Allen Institute and DREAM-will comprise 3 subchallenges that consist of reconstructing cell lineage trees of different sizes and nature. In subchallenge 1, participants will be given experimental molecular data...","","https://www.synapse.org/#!Synapse:syn18405991","completed","1","https://doi.org/10.1093/jamia/ocad159","2019-09-09","2020-01-23","2229","2023-06-23 00:00:00","2024-03-04 18:29:53" -"36","allen-institute-cell-lineage-reconstruction","Allen Institute Cell Lineage Reconstruction","New tools enable reconstructing complex cell lineages at single-cell resolution","The recent advent of new CRISPR-based molecular tools allows the reconstruction of cell lineages based on the phylogenetical analysis of DNA mutations induced by CRISPR during development and promises to solve the lineage of complex model organisms at single-cell resolution. To date, however, no lineage reconstruction algorithms have been rigorously examined for their performance/robustness across diverse molecular tools, datasets, and number of cells/size of lineage trees. It also remains unclear whether new Machine-Learning algorithms that go beyond the classical ones developed for reconstructing phylogenetic trees, could consistently reconstruct cell lineages to a high degree of accuracy. The challenge-a partnership between The Allen Institute and DREAM-will comprise 3 subchallenges that consist of reconstructing cell lineage trees of different sizes and nature. In subchallenge 1, participants will be given experimental molecular data to reconstruct in vitro cell lineages of l...","","https://www.synapse.org/#!Synapse:syn20692755","completed","1","","2019-10-15","2020-02-06","2229","2023-06-23 00:00:00","2024-03-04 18:29:55" +"35","ehr-dream-challenge-patient-mortality-prediction","EHR DREAM Challenge: Patient Mortality Prediction","New tools to reconstruct cell lineages from CRISPR mutations","The recent advent of new CRISPR-based molecular tools allows the reconstruction of cell lineages based on the phylogenetical analysis of DNA mutations induced by CRISPR during development and promises to solve the lineage of complex model organisms at single-cell resolution (see image from McKenna et al Science 2016). To date, however, no lineage reconstruction algorithms have been rigorously examined for their performance/robustness across diverse molecular tools, datasets, and number of cells/size of lineage trees. It also remains unclear whether new Machine-Learning algorithms that go beyond the classical ones developed for reconstructing phylogenetic trees, could consistently reconstruct cell lineages to a high degree of accuracy. The challenge-a partnership between The Allen Institute and DREAM-will comprise 3 subchallenges that consist of reconstructing cell lineage trees of different sizes and nature. In subchallenge 1, participants will be given experimental molecular data...","","https://www.synapse.org/#!Synapse:syn18405991","completed","1","https://doi.org/10.1093/jamia/ocad159","2019-09-09","2020-01-23","2230","2023-06-23 00:00:00","2024-03-15 16:52:54" +"36","allen-institute-cell-lineage-reconstruction","Allen Institute Cell Lineage Reconstruction","New tools enable reconstructing complex cell lineages at single-cell resolution","The recent advent of new CRISPR-based molecular tools allows the reconstruction of cell lineages based on the phylogenetical analysis of DNA mutations induced by CRISPR during development and promises to solve the lineage of complex model organisms at single-cell resolution. To date, however, no lineage reconstruction algorithms have been rigorously examined for their performance/robustness across diverse molecular tools, datasets, and number of cells/size of lineage trees. It also remains unclear whether new Machine-Learning algorithms that go beyond the classical ones developed for reconstructing phylogenetic trees, could consistently reconstruct cell lineages to a high degree of accuracy. The challenge-a partnership between The Allen Institute and DREAM-will comprise 3 subchallenges that consist of reconstructing cell lineage trees of different sizes and nature. In subchallenge 1, participants will be given experimental molecular data to reconstruct in vitro cell lineages of l...","","https://www.synapse.org/#!Synapse:syn20692755","completed","1","","2019-10-15","2020-02-06","2230","2023-06-23 00:00:00","2024-03-04 18:29:55" "37","tumor-deconvolution","Tumor Deconvolution","Deconvolve bulk tumor data into immune components","The extent of stromal and immune cell infiltration within solid tumors has prognostic and predictive significance. Unfortunately, expression profiling of tumors has, until very recently, largely been undertaken using bulk techniques (e.g., microarray and RNA-seq). Unlike single-cell methods (e.g., single-cell RNA-seq, FACS, mass cytometry, or immunohistochemistry), bulk approaches average expression across all cells (cancer, stromal, and immune) within the sample and, hence, do not directly quantitate tumor infiltration. This information can be recovered by computational tumor deconvolution methods, which would thus allow interrogation of immune subpopulations across the large collection of public bulk topic_3170sets. The goal of this Challenge is to evaluate the ability of computational methods to deconvolve bulk topic_3170, reflecting a mixture of cell types, into individual immune components. Methods will be assessed based on in vitro and in silico admixtures specifically gener...","","https://www.synapse.org/#!Synapse:syn15589870","completed","1","","2019-06-26","2020-04-30","\N","2023-06-23 00:00:00","2023-11-14 19:07:39" "38","ctd2-pancancer-drug-activity","CTD2 Pancancer Drug Activity","Benchmark algorithms predicting drug targets from gene data","Over the last two years, the Columbia CTD2 Center developed PANACEA (Pancancer Analysis of Chemical Entity Activity), a comprehensive repertoire of dose response curves and molecular profiles representative of cellular responses to drug perturbations. PANACEA covers a broad spectrum of cellular contexts representative of poor outcome malignancies, including rare ones such as GIST sarcoma and gastroenteropancreatic neuroendocrine tumors (GEP-NETs). PANACEA is uniquely suited to support DREAM Challenges related to the elucidation of drug mechanism of action (MOA), drug sensitivity, and drug synergy. The goal of the CTD2 Pancancer Drug Activity DREAM Challenge is to foster the development and benchmarking of algorithms to predict targets of chemotherapeutic compounds from post-treatment transcriptional data.","","https://www.synapse.org/#!Synapse:syn20968331","completed","1","","2019-12-02","2020-02-13","\N","2023-06-23 00:00:00","2023-10-20 23:11:10" "39","ctd2-beataml","CTD2 BeatAML","Seeking new drug targets for precision AML treatment","In the era of precision medicine, AML patients have few therapeutic options, with “7 + 3” induction chemotherapy having been the standard for decades (Bertoli et al. 2017). While several agents targeting the myeloid marker CD33 or alterations in FLT3 or IDH2 have demonstrated efficacy in patients (Wei and Tiong 2017), responses are uncertain in some populations (Castaigne et al. 2012) and relapse remains prevalent (Stone et al. 2017). These drugs highlight both the promise of targeted therapies in AML and the urgent need for additional treatment options that are tailored to more refined patient subpopulations in order to achieve durable responses. The BeatAML initiative was launched as a comprehensive study of the relationship between molecular alterations and ex-vivo drug sensitivity in patients with AML. One of the primary goals of this multi-center study was to develop a discovery cohort that could yield new drug target hypotheses and predictive biomarkers of therapeutic respon...","","https://www.synapse.org/#!Synapse:syn20940518","completed","1","","2019-12-19","2020-04-28","\N","2023-06-23 00:00:00","2023-10-14 05:38:42" @@ -162,7 +162,7 @@ "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","\N","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","\N","2023-08-05 0-13-23","2023-11-14 19:27:00" "163","icr-identify-age-related-conditions","ICR - Identifying Age-Related Conditions","Detect conditions with measurements of anonymous characteristics of a subject","The goal of this competition is to predict if a person has any of three medical conditions. You are being asked to predict if the person has one or more of any of the three medical conditions (Class 1), or none of the three medical conditions (Class 0). You will create a model trained on measurements of health characteristics. To determine if someone has these medical conditions requires a long and intrusive process to collect information from patients. With predictive models, we can shorten this process and keep patient details private by collecting key characteristics relative to the conditions, then encoding these characteristics.","","https://www.kaggle.com/competitions/icr-identify-age-related-conditions","completed","8","","2023-05-11","2023-08-10","\N","2023-08-05 0-32-01","2023-11-14 19:25:37" -"164","cafa-5-protein-function-prediction","CAFA 5: Protein Function Prediction","Predict the biological function of a protein","The goal of this competition is to predict the function of a set of proteins. You will develop a model trained on the amino-acid sequences of the proteins and on other data. Your work will help researchers better understand the function of proteins, which is important for discovering how cells, tissues, and organs work. This may also aid in the development of new drugs and therapies for various diseases.","","https://www.kaggle.com/competitions/cafa-5-protein-function-prediction","completed","8","","2023-04-18","2023-08-21","2569","2023-08-05 5-18-40","2024-03-04 18:30:13" +"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","2570","2023-08-05 5-18-40","2024-03-04 18:30:13" "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","\N","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","\N","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","\N","2023-08-05 5-37-12","2023-12-06 22:44:19" @@ -279,7 +279,7 @@ "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 w...","","https://www.eventbrite.com/e/qbi-hackathon-2023-tickets-633794304827?aff=oddtdtcreator","completed","\N","","2023-11-04","2023-11-05","\N","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","\N","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","\N","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","\N","2023-11-02 15:35:22","2023-11-17 21:29:35" +"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","2024-04-09","\N","2023-11-02 15:35:22","2024-03-18 16:30:06" "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","\N","2023-11-08 00:42:00","2023-11-17 21:30:05" "283","crass","CRASS","Invites participants to submit clavicle segmentation results","Crass stands for chest radiograph anatomical structure segmentation. The challenge currently invites participants to send in results for clavicle segmentation algorithms.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/5/logo.png","https://crass.grand-challenge.org/","completed","5","","\N","\N","\N","2023-11-08 00:42:00","2023-11-15 22:09:56" "284","anode09","ANODE09","Automatic pulmonary nodule detection systems in chest CT scans","ANODE09 is an initiative to compare systems that perform automatic detection of pulmonary nodules in chest CT scans on a single common database, with a single evaluation protocol.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/7/logo.png","https://anode09.grand-challenge.org/","completed","5","https://doi.org/10.1016/j.media.2010.05.005","\N","\N","\N","2023-11-08 00:42:00","2023-11-17 23:17:55" diff --git a/apps/openchallenges/challenge-service/src/main/resources/db/input_data_type.csv b/apps/openchallenges/challenge-service/src/main/resources/db/input_data_type.csv index 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