From b1e42cde8b730f2b3fa3ab4cba695e498af9c711 Mon Sep 17 00:00:00 2001 From: vpchung <9377970+vpchung@users.noreply.github.com> Date: Thu, 6 Feb 2025 00:01:56 +0000 Subject: [PATCH] 2025-02-06: add latest CSV dump files --- .../challenge-service/src/main/resources/db/challenges.csv | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 b008ca1ac..77b49ceb4 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv +++ b/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv @@ -522,7 +522,7 @@ "521","placental-clock-dream-challenge","Placental Clock DREAM Challenge","Develop a new clock to achieve greater accuracy in predicting gestational age!","Since 2011, three generations of epigenetic clocks have been developed to estimate biological age[1-3]. The first-generation of models were generated by using DNA methylation data in various tissues to predict chronological age (outcome)[1, 4-6]. Second-generation models, such as PhenoAge and GrimAge used health outcomes, including all-cause mortality, for a more accurate determination of the latent biological age[7-9]. The latest, third-generation clocks like DunedinPoAm use longitudinal data to estimate the rate of aging[10]. This generation also includes universal clocks applicable to multiple species, such as the universal pan-mammalian epigenetic clock[11]. Biological age, as captured by these DNA methylation clocks, can be influenced by environmental factors, including smoking, obesity, sleep patterns, diet and exercise, stress, as well as diseases like cancer, diabetes, and Down syndrome[12-18]. The role of epigenetic programming in fetal development is crucial [19-21]. Th...","","http://synapse.org/placentalclock","completed","1","","2024-06-03","2024-08-27","\N","2024-06-03 16:59:54","2024-06-03 17:03:06" "522","detecting-active-tuberculosis-bacilli-2024","Detecting Active Tuberculosis Bacilli - 2024","","Tuberculosis is one of the leading infectious causes of death worldwide 1. Each year, millions of individuals contract and develop active TB without knowing 2. Case identification and treatment are the primary methods for controlling spread as there is no effective TB vaccine for adults. Unfortunately, delays in diagnosis are common, especially in resource-limited settings, and can worsen individual outcomes and perpetuate transmission of the disease 3,4. Without a timely diagnosis, patients needing treatment would head home from a clinic without knowing they were positive. If they miss their follow up, they would not learn about their diagnosis and nor would they start their treatment. Automated TB diagnosis could play a role reducing the loss of follow up and get patients to be treated more timely. Automated digital microscopy has been proposed as a cost-effective solution 6,7. An automated algorithm that could reliably detect mycobacterium on samples from patients with suspecte...","","https://app.nightingalescience.org/contests/m3rl61qq21wo","completed","15","","2024-03-01","2024-04-01","\N","2024-07-02 22:45:34","2024-11-19 22:17:31" "523","isic-2024-challenge","ISIC 2024 - Skin Cancer Detection with 3D-TBP","Identify cancers among skin lesions cropped from 3D total body photographs","Skin cancer can be deadly if not caught early, but many populations lack specialized dermatologic care. Over the past several years, dermoscopy-based AI algorithms have been shown to benefit clinicians in diagnosing melanoma, basal cell, and squamous cell carcinoma. However, determining which individuals should see a clinician in the first place has great potential impact. Triaging applications have a significant potential to benefit underserved populations and improve early skin cancer detection, the key factor in long-term patient outcomes. Dermatoscope images reveal morphologic features not visible to the naked eye, but these images are typically only captured in dermatology clinics. Algorithms that benefit people in primary care or non-clinical settings must be adept to evaluating lower quality images. This competition leverages 3D TBP to present a novel dataset of every single lesion from thousands of patients across three continents with images resembling cell phone photos....","","https://www.kaggle.com/competitions/isic-2024-challenge","completed","8","","2024-06-27","2024-09-06","2869","2024-11-19 22:20:29","2024-11-19 22:20:34" -"524","czii-cryo-et-object-identification","CZII - CryoET Object Identification","Find small biological structures in large 3D volumes","Protein complexes (such as oxygen-carrying hemoglobin, or keratin in hair, and thousands of others) are essential for cell function, and understanding their interactions is essential for our health and finding new disease treatments. Cryo-electron tomography (cryoET) creates 3D images—called tomograms—at near-atomic detail, showing proteins in their very complex and crowded natural environment. Therefore, cryoET has immense potential to unlock the mysteries of the cell. There is a wealth of cryoET tomograms that is yet to be fully mined. A large and growing portion of this published corpus exists in a standardized format in the cryoET data portal (cryoetdataportal.czscience.com). Mining this data requires automatic identification of each protein molecule within these images. This problem has not been solved even for proteins that are identifiable by the human eye. A generalizable solution will reveal the “dark matter” of the cell, and will enable thousands of discoveries contribu...","","https://www.kaggle.com/competitions/czii-cryo-et-object-identification","active","8","","2024-11-06","2025-02-05","2869","2024-11-19 22:20:36","2024-12-03 1:32:11" +"524","czii-cryo-et-object-identification","CZII - CryoET Object Identification","Find small biological structures in large 3D volumes","Protein complexes (such as oxygen-carrying hemoglobin, or keratin in hair, and thousands of others) are essential for cell function, and understanding their interactions is essential for our health and finding new disease treatments. Cryo-electron tomography (cryoET) creates 3D images—called tomograms—at near-atomic detail, showing proteins in their very complex and crowded natural environment. Therefore, cryoET has immense potential to unlock the mysteries of the cell. There is a wealth of cryoET tomograms that is yet to be fully mined. A large and growing portion of this published corpus exists in a standardized format in the cryoET data portal (cryoetdataportal.czscience.com). Mining this data requires automatic identification of each protein molecule within these images. This problem has not been solved even for proteins that are identifiable by the human eye. A generalizable solution will reveal the “dark matter” of the cell, and will enable thousands of discoveries contribu...","","https://www.kaggle.com/competitions/czii-cryo-et-object-identification","completed","8","","2024-11-06","2025-02-05","2869","2024-11-19 22:20:36","2024-12-03 1:32:11" "525","1000-genomes-ancestry","1000 Genomes Ancestry","Predict the ancestry of individuals from the 1000 Genomes Project.","Welcome to the 1000 Genomes Project ancestry prediction competition. Geneticists identified locations in the human genome that can assess an individual's ancestry, called ancestry-informative single nucleotide polymorphisms (AISNPs). These locations, denoted by rsid numbers, display ancestry-specific variation. For example, at location rs3737576, 32% of individuals from the 1000 Genomes Project who were assigned American ancestry have a C allele, while the study-wide frequency of the C allele was only 8%. There are many locations like this across the human genome. AISNPs from two peer-reviewed publications are available in this competition. Kidd et al. identified 55 AISNPs, and Seldin et al. identified 128 AISNPs with varying degrees of discriminatory power. You can learn more about original ancestry classifications from the 1000 Genomes Project here and here. The dataset in this competition contains alleles from the 1000 Genomes Project for 2,504 individuals across 183 AISNPs....","","https://www.kaggle.com/competitions/1000-genomes-ancestry","completed","8","","2024-04-18","2024-06-24","2416","2024-11-19 22:20:44","2024-12-03 1:31:49" "526","tcr-specificity-prediction-challenge","IMMREP23: TCR Specificity Prediction Challenge","Predictions on unpublished TCR-epitope binding to benchmark prediction methods.","IMMREP23, the second annual IMMREP benchmark on TCR-epitope specificity prediction will run from November 1, 2023 to December 11, 2023. Together with several experimental groups, we have compiled a data set of paired TCR data with annotated specificity to 21 pHLA (covering 6 distinct HLA molecules). This challenge models TCR epitope recognition as a binary classification task. For a given test set of TCR-epitope pairs, the task of the model is to identify which pairs will bind and which will not bind.","","https://www.kaggle.com/competitions/tcr-specificity-prediction-challenge","completed","8","","2023-10-31","2023-12-11","2242","2024-11-20 16:02:13","2024-12-03 1:32:04" "527","ibiohash-2024-fgvc11","iBioHash 2024-FGVC11","A task of large-scale zero-shot fine-grained image hashing.","Fine-Grained Image Analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class and large intra-class variations inherent to fine-grained image analysis make it a challenging problem. Fine-grained image retrieval, as a crucial research area of FGIA, aims to retrieve images belonging to multiple subordinate categories of a super-category (aka a meta-category). Its key challenge therefore lies in understanding fine-grained visual differences that sufficiently distinguish objects that are highly similar in overall appearance, but differ in fine-grained features. Also, fine-grained retrieval still demands ranking all the instances so that images depicting the concept of interest are ranked highest based on the fine-grained details in the query. In...","","https://www.kaggle.com/competitions/ibiohash-2024-fgvc11","completed","8","","2024-03-08","2024-05-17","2869","2024-11-25 22:36:58","2024-11-25 23:42:11"