chore(openchallenges): 2024-03-26 DB update (#2598)

Co-authored-by: vpchung <9377970+vpchung@users.noreply.github.com>

apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv

@@ -23,7 +23,7 @@
 "22","idea","Idea","Fostering collaborative solutions in health: the DREAM IDEA challenge","The DREAM Idea Challenge is designed to collaboratively shape and enable the solution of a question fundamental to improving human health. In the process, all proposals and their evaluation will be made publicly available for the explicit purpose of connecting modelers and experimentalists who want to address the same question. This Wall of Models will enable new collaborations, and help turn every good modeling idea into a success story. It will further serve as a basis for new DREAM challenges.","","https://www.synapse.org/#!Synapse:syn5659209","completed","1","","2016-06-15","2017-04-30","\N","2023-06-23 00:00:00","2023-11-20 20:18:36"
 "23","smc-rna","SMC-RNA","Crowdsourcing challenge to improve cancer mutation detection from rna data","The ICGC-TCGA DREAM Somatic Mutation Calling-RNA Challenge (SMC-RNA) is an international effort to improve standard methods for identifying cancer-associated rearrangements in RNA sequencing (RNA-seq) data. Leaders of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cancer genomics projects are joining with Sage Bionetworks and IBM-DREAM to initiate this innovative open crowd-sourced Challenge [1-3].","","https://www.synapse.org/#!Synapse:syn2813589","completed","1","","2016-06-29","2017-05-02","\N","2023-06-23 00:00:00","2023-10-14 05:38:29"
 "24","digital-mammography-dream-challenge","Digital Mammography DREAM Challenge","Improve mammography prediction to detect breast cancer early","The Digital Mammography DREAM Challenge will attempt to improve the predictive accuracy of digital mammography for the early detection of breast cancer. The primary benefit of this Challenge will be to establish new quantitative tools-machine learning, deep learning or other-that can help decrease the recall rate of screening mammography, with a potential impact on shifting the balance of routine breast cancer screening towards more benefit and less harm. Participating teams will be asked to submit predictive models based on over 640,000 de-identified digital mammography images from over 86000 subjects, with corresponding clinical variables.","","https://www.synapse.org/#!Synapse:syn4224222","completed","1","https://doi.org/10.1001/jamanetworkopen.2020.0265","2016-11-18","2017-05-16","\N","2023-06-23 00:00:00","2023-10-14 05:38:29"
-"25","multiple-myeloma","Multiple Myeloma","Develop precise risk model for myeloma patients","Multiple myeloma (MM) is a cancer of the plasma cells in the bone marrow, with about 25,000 newly diagnosed patients per year in the United States alone. The disease's clinical course depends on a complex interplay of clinical traits and molecular characteristics of the plasma cells.1 Since risk-adapted therapy is becoming standard of care, there is an urgent need for a precise risk stratification model to assist in therapeutic decision-making and research. While progress has been made, there remains a significant opportunity to improve patient stratification to optimize treatment and to develop new therapies for high-risk patients. A DREAM Challenge represents a chance not only to integrate available data and analytical approaches to tackle this important problem, but also provides the ability to benchmark potential methods to identify those with the greatest potential to yield patient care benefits in the future.","","https://www.synapse.org/#!Synapse:syn6187098","completed","1","","2017-06-30","2017-11-08","\N","2023-06-23 00:00:00","2023-10-14 05:38:31"
+"25","multiple-myeloma","Multiple Myeloma","Develop precise risk model for myeloma patients","Multiple myeloma (MM) is a cancer of the plasma cells in the bone marrow, with about 25,000 newly diagnosed patients per year in the United States alone. The disease's clinical course depends on a complex interplay of clinical traits and molecular characteristics of the plasma cells.1 Since risk-adapted therapy is becoming standard of care, there is an urgent need for a precise risk stratification model to assist in therapeutic decision-making and research. While progress has been made, there remains a significant opportunity to improve patient stratification to optimize treatment and to develop new therapies for high-risk patients. A DREAM Challenge represents a chance not only to integrate available data and analytical approaches to tackle this important problem, but also provides the ability to benchmark potential methods to identify those with the greatest potential to yield patient care benefits in the future.","","https://www.synapse.org/#!Synapse:syn6187098","completed","1","","2017-06-30","2017-11-08","\N","2023-06-23 00:00:00","2024-03-26 01:26:13"
 "26","ga4gh-dream-workflow-execution","GA4GH-DREAM Workflow Execution","Develop technologies to enable distributed genomic data analysis","The highly distributed and disparate nature of genomic and clinical data generated around the world presents an enormous challenge for those scientists who wish to integrate and analyze these data. The sheer volume of data often exceeds the capacity for storage at any one site and prohibits the efficient transfer between sites. To address this challenge, researchers must bring their computation to the data. Numerous groups are now developing technologies and best practice methodologies for running portable and reproducible genomic analysis pipelines as well as tools and APIs for discovering genomic analysis resources. Software development, deployment, and sharing efforts in these groups commonly rely on the use of modular workflow pipelines and virtualization based on Docker containers and related tools.","","https://www.synapse.org/#!Synapse:syn8507133","completed","1","","2017-07-21","2017-12-31","\N","2023-06-23 00:00:00","2023-10-14 05:38:31"
 "27","parkinsons-disease-digital-biomarker","Parkinson's Disease Digital Biomarker","Develop Parkinson's digital signatures from sensor data for Parkinson's disease","The Parkinson's Disease Digital Biomarker DREAM Challenge is a first of it's kind challenge, designed to benchmark methods for the processing of sensor data for development of digital signatures reflective of Parkinson's Disease. Participants will be provided with raw sensor (accelerometer, gyroscope, and magnetometer) time series data recorded during the performance of pre-specified motor tasks, and will be asked to extract data features which are predictive of PD pathology. In contrast to traditional DREAM challenges, this one will focus on feature extraction rather than predictive modeling, and submissions will be evaluated based on their ability to predict disease phenotype using an array of standard machine learning algorithms.","","https://www.synapse.org/#!Synapse:syn8717496","completed","1","","2017-07-06","2017-11-10","\N","2023-06-23 00:00:00","2023-11-14 19:10:32"
 "28","nci-cptac-proteogenomics","NCI-CPTAC Proteogenomics","Develop tools to extract insights from cancer proteomics data","Cancer is driven by aberrations in the genome [1,2], and these alterations manifest themselves largely in the changes in the structure and abundance of proteins, the main functional gene products. Hence, characterization and analyses of alterations in the proteome has the promise to shed light into cancer development and may improve development of both biomarkers and therapeutics. Measuring the proteome is very challenging, but recent rapid technology developments in mass spectrometry are enabling deep proteomics analysis [3]. Multiple initiatives have been launched to take advantage of this development to characterize the proteome of tumours, such as the Clinical Proteomic Tumor Analysis Consortium (CPTAC). These efforts hold the promise to revolutionize cancer research, but this will only be possible if the community develops computational tools powerful enough to extract the most information from the proteome, and to understand the association between genome, transcriptome and ...","","https://www.synapse.org/#!Synapse:syn8228304","completed","1","","2017-06-26","2017-11-20","\N","2023-11-01 22:21:37","2023-10-14 05:38:33"
@@ -498,6 +498,6 @@
 "497","hack-rare-disease","Harvard Rare Disease Hackathon 2024","Are you a student interested in using AI/ML to tackle rare diseases? Join us!","This March 2-3, join us for the 2024 Harvard Rare Disease Hackathon, where students will gather on Harvard''s campus to set forth their own data-driven solutions for rare diseases.  Participants will have the chance to analyze public and patient-sourced genomic and clinical datasets, and will be challenged to produce deliverables for participating patient organizations. These deliverables may take the form of a data report, computational tool, or web/mobile application that improves the lives of patients or furthers research progress.   Participation is free and open to all undergraduate and graduate students who register with their .edu email address.","","https://www.harvard-rarediseases.org/","completed","\N","","2024-03-02","2024-03-03","\N","2024-02-06 00:12:34","2024-02-06 0:41:24"
 "498","dreaming","Diminished Reality for Emerging Applications in Medicine through Inpainting","Dataset of Synthetic Surgery Scenes: Photorealistic Operating Room Simulations","The Diminished Reality for Emerging Applications in Medicine through Inpainting (DREAMING) challenge seeks to pioneer the integration of Diminished Reality (DR) into oral and maxillofacial surgery. While Augmented Reality (AR) has been extensively explored in medicine, DR remains largely uncharted territory.  DR involves virtually removing real objects from the environment by replacing them with their background. Recent inpainting methods present an opportunity for real-time DR applications without scene knowledge. DREAMING focuses on implementing such methods to fill obscured regions in surgery scenes with realistic backgrounds, emphasizing the complex facial anatomy and patient diversity.  The challenge provides a dataset of synthetic yet photorealistic surgery scenes featuring humans, simulating an operating room setting. Participants are tasked with developing algorithms that seamlessly remove disruptions caused by medical instruments and hands, offering surgeons an unimpeded ...","https://rumc-gcorg-p-public.s3.amazonaws.com/b/752/isbi_dreaming_banner_gc_297CU3H.x10.jpeg","https://dreaming.grand-challenge.org/","active","5","","2024-01-08","2024-04-27","\N","2024-02-12 21:56:27","2024-02-20 6:38:09"
 "499","brats-goat","BraTS-ISBI 2024 - Generalizability Across Tumors Challenge","BraTS-GoAT Challenge: Generalizability Across Brain Tumor Segmentation Tasks","The International Brain Tumor Segmentation (BraTS) challenge has been focusing, since its inception in 2012, on generating a benchmarking environment and a dataset for delineating adult brain gliomas. The focus of the BraTS 2023 challenge remained the same: generating a standard benchmark environment. At the same time, the dataset expanded into explicitly addressing 1) the same adult glioma population, as well as 2) the underserved sub-Saharan African brain glioma patient population, 3) brain/intracranial meningioma, 4) brain metastasis, and 5) pediatric brain tumor patients.  Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. Each segmentation method was evaluated exclusively on the patient population it was trained on in each sub-challenge.  In this challenge, we aim to organize the BraTS Generalizability Across Tumors (BraTS-GoAT) Challenge. The hypothesis is t...","","https://www.synapse.org/brats_goat","active","1","","2024-01-09","2024-03-29","\N","2024-02-19 18:20:32","2024-03-06 18:57:49"
-"500","ctc2024","Cell Tracking Challenge 2024","Develop novel, robust cell segmentation and tracking algorithms","Segmenting and tracking moving cells in time-lapse sequences is a challenging task, required for many applications in both scientific and industrial settings. Properly characterizing how cells change their shapes and move as they interact with their surrounding environment is key to understanding the mechanobiology of cell migration and its multiple implications in both normal tissue development and many diseases.  In this challenge, we objectively compare and evaluate state-of-the-art whole-cell and nucleus segmentation and tracking methods using both real and computer-generated (2D and 3D) time-lapse microscopy videos of cells and nuclei. With over a decade-long history and three detailed analyses of its results published in  Bioinformatics 2014, Nature Methods 2017, and Nature Methods 2023, the Cell Tracking Challenge has become a reference in cell segmentation and tracking algorithm development.  This ongoing benchmarking initiative calls for segmentation-and-tracking and segm...","http://celltrackingchallenge.net/files/extras/tracking-result.gif","http://celltrackingchallenge.net/ctc-vii/","active","\N","","2023-12-22","2024-03-25","\N","2024-03-06 18:57:14","2024-03-06 19:30:53"
+"500","ctc2024","Cell Tracking Challenge 2024","Develop novel, robust cell segmentation and tracking algorithms","Segmenting and tracking moving cells in time-lapse sequences is a challenging task, required for many applications in both scientific and industrial settings. Properly characterizing how cells change their shapes and move as they interact with their surrounding environment is key to understanding the mechanobiology of cell migration and its multiple implications in both normal tissue development and many diseases.  In this challenge, we objectively compare and evaluate state-of-the-art whole-cell and nucleus segmentation and tracking methods using both real and computer-generated (2D and 3D) time-lapse microscopy videos of cells and nuclei. With over a decade-long history and three detailed analyses of its results published in  Bioinformatics 2014, Nature Methods 2017, and Nature Methods 2023, the Cell Tracking Challenge has become a reference in cell segmentation and tracking algorithm development.  This ongoing benchmarking initiative calls for segmentation-and-tracking and segm...","http://celltrackingchallenge.net/files/extras/tracking-result.gif","http://celltrackingchallenge.net/ctc-vii/","active","\N","","2023-12-22","2024-04-05","\N","2024-03-06 18:57:14","2024-03-26 1:26:38"
 "501","isbi-bodymaps24-3d-atlas-of-human-body","ISBI BodyMaps24: 3D Atlas of Human Body","","Variations in organ sizes and shapes can indicate a range of medical conditions, from benign anomalies to life-threatening diseases. Precise organ volume measurement is fundamental for effective patient care, but manual organ contouring is extremely time-consuming and exhibits considerable variability among expert radiologists. Artificial Intelligence (AI) holds the promise of improving volume measurement accuracy and reducing manual contouring efforts. We formulate our challenge as a semantic segmentation task, which automatically identifies and delineates the boundary of various anatomical structures essential for numerous downstream applications such as disease diagnosis and treatment planning. Our primary goal is to promote the development of advanced AI algorithms and to benchmark the state of the art in this field.  The BodyMaps challenge particularly focuses on assessing and improving the generalizability and efficiency of AI algorithms in medical segmentation across divers...","","https://codalab.lisn.upsaclay.fr/competitions/16919","active","9","","2024-01-10","2024-04-15","\N","2024-03-06 20:12:50","2024-03-06 20:16:23"
 "502","precisionfda-automated-machine-learning-automl-app-a-thon","precisionFDA Automated Machine Learning (AutoML) App-a-thon","Unlock new insights into its potential applications in healthcare and medicine","Say goodbye to the days when machine learning (ML) access was the exclusive purview of data scientists and hello to automated ML (AutoML), a low-code ML technique designed to empower professionals without a data science background and enable their access to ML. Although ML and artificial intelligence (AI) have been highly discussed topics in healthcare and medicine, only 15% of hospitals are routinely using ML due to lack of ML expertise and a lengthy data provisioning process. Can AutoML help bridge this gap and expand ML throughout healthcare?  The goal of this app-a-thon is to evaluate the effectiveness of AutoML when applied to biomedical datasets. This app-a-thon aligns with the new Executive Order on Safe, Secure, and Trustworthy Development and Use of AI, which calls for agencies to promote competition in AI. The results of this app-a-thon will be used to help inform regulatory science by evaluating whether AutoML can match or improve the performance of traditional, human-c...","","https://precision.fda.gov/challenges/32","active","6","","2024-02-26","2024-04-26","\N","2024-03-11 22:58:43","2024-03-11 23:02:12"