Advancing method benchmarking and data sharing through crowd-sourced competitions in cancer research

通过癌症研究中的众包竞赛推进方法基准测试和数据共享

• 批准号: 10188471
• 负责人: Jacob Albrecht
• 金额: $ 81.82万
• 依托单位: SAGE BIONETWORKS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10188471
• 关键词: Address; Adoption; Algorithms; Architecture; Area; Assessment tool; Benchmarking; Biological; Biological Markers; Biological Models; Biology; Biomedical Research; Clinical; Clinical Trials; Collaborations; Communities; Community Developments; Complex; Computer software; Crowding; Data; Data Set; Development; Diagnosis; Diagnostic; Disease; Education and Outreach; Educational workshop; Engineering; Ensure; Environment; Evaluation; Face; Fostering; Funding; Genomics; Human; Image; Improve Access; Incentives; Individual; Infrastructure; Instruction; Libraries; Longevity; Malignant Neoplasms; Medicine; Methods; Modeling; Modernization; Oncology; Output; Participant; Patient Care; Patients; Performance; Plug-in; Practice Guidelines; Privatization; Process; Prognosis; Registries; Reproducibility; Research; Research Personnel; Running; Science; Services; Structure; Text; Time; Vision; Work; algorithm development; anticancer research; base; community building; community engagement; crowdsourcing; data ecosystem; data reuse; data sharing; data tools; design; drug response prediction; empowered; genomic tools; hackathon; improved; innovation; insight; neoplasm resource; next generation; open data; portability; prognostic; programs; symposium; tool; tool development; treatment response; usability; virtual technology

Project Summary/Abstract Crowd-sourced competitions have transformed biomedical research by incentivizing the coalescence of communities around timely and difficult problems. These communities have created new standards and benchmarks, and developed innovative solutions to dozens of pressing research problems. The Dialogue on Reverse Engineering Assessment and Methods (DREAM) Challenges is a leader in generating communities through these crowd-sourced competitions. DREAM is an open science, collaborative competition framework that allows participants from around the world to work together to solve fundamental biomedical questions. Its vision is to allow open collaboration amongst individuals and groups, maximizing societal impact through the “wisdom of the crowd”. Over the past 13 years DREAM Challenges have developed key biomarkers and benchmarks for cancer research, fostering the development of innovative methods, the objective assessment of tools and algorithms, the development of community standards, and greater access to critical data sets. To enable the next generation of Challenges, we propose to expand our existing infrastructure into an innovative platform for rapid, rigorous and scientifically-valid Challenge-based assessments. We propose to create a Challenge framework that (1) provides a discovery engine for biomedical Challenges and their outputs, (2) contains innovative tools that enhance and streamline Challenges, and (3) is scaleable and supports distributed assessments using private and sensitive data. These will be married to a suite of educational tools, instructions, best-practices guidelines, and workshops and conferences to expand the solver community and support the organization of independent challenges to maximize impact on biomedical research. Our proposal will utilize several existing and funded driver Challenges in high-impact cancer domains, encompassing key biomedical data in imaging, genomics, EHR (structured and unstructured text), and clinical trial data.

项目摘要/摘要 众包竞争通过激励 及时解决社区周围的困难问题。这些社区创建了新的标准和 基准，并为数十个紧迫的研究问题开发了创新的解决方案。对话内容： 逆向工程评估和方法(DREAM)挑战赛是生成社区的领导者 通过这些众包比赛。梦想是一个开放的科学、协作的竞争框架 这使得来自世界各地的参与者能够共同努力解决基本的生物医学问题。它的 愿景是允许个人和团体之间的开放协作，通过 《群众的智慧》。在过去的13年里，梦想挑战开发了关键的生物标记物和 癌症研究的基准，促进创新方法的开发，客观评估 工具和算法，开发社区标准，以及更多地获取关键数据集。至 应对下一代挑战，我们建议将现有基础设施扩展为创新的 基于挑战的快速、严谨和科学有效的评估平台。我们建议创建一个 挑战框架，(1)为生物医学挑战及其成果提供发现引擎，(2) 包含可增强和简化挑战的创新工具，以及(3)可扩展并支持 使用私有和敏感数据的分布式评估。这些人将与一套教育工具相结合， 说明、最佳实践指南以及研讨会和会议，以扩大求解器社区和 支持组织独立的挑战，以最大限度地提高对生物医学研究的影响。我们的建议 将利用高影响癌症领域的几个现有和资助的驱动因素挑战，包括关键 成像、基因组学、EHR(结构化和非结构化文本)和临床试验数据中的生物医学数据。