Biomedical Data Commons Workbench (BDCW)

生物医学数据共享工作台 (BDCW)

• 批准号: 10683515
• 负责人: Shankar Subramaniam
• 金额: $ 80.72万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-23 至 2023-09-22
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10683515
• 关键词: Data Commons; Text

The future of biomedicine will rely on the ability to integrate genotype and phenotype data contextually to identify biomarkers, decipher mechanisms, reconstruct networks, and develop quantitative models by biomedical and clinical researchers in a seamless manner. What would be extremely valuable is a webaccessible one-stop shop providing these capabilities. Creating such seamless infrastructure is the vision of our initiative. We propose the development of the Biomedical Data Commons Workbench (BDCW), which will help overcome the fundamental barriers to biomedical data integration. BDCW will address two fundamental barriers faced by biomedical researchers – the first deals with the question, are there data available that can answer a biomedical question or questions (also referred to from the research point of view as a Use Case) and second, how can such data be integratively analyzed without having to go through the tedious process of either developing or using a variety of tools (which in turn requires working knowledge of computational methods). Several Common Fund data sources attempt to help the end-user vis-à-vis the data they provide, but not the ability to seamlessly interoperate with another Common Fund data source. Even if one were to identify the appropriate data sources to address a given research question, the challenge of integration of these diverse data is non-trivial. Data integration refers to the task of combining information about the same entities managed in different information systems to present a unified data view across different systems. It also refers to integrative analyses of multiple types of data, including data collected at different biological scales, to discover new knowledge as to how biological systems function (“mechanisms”).

生物医学的未来将依赖于整合基因型和表型数据的能力