Biomedical Data Commons Workbench (BDCW)

生物医学数据共享工作台 (BDCW)

• 批准号: 10907953
• 负责人: Shankar Subramaniam
• 金额: $ 82.55万
• 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 2024-09-22
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10907953
• 关键词: Address; Biological; Clinical; Computing Methodologies; Data; Data Commons; Data Sources; Development; Funding; Future; Genotype; Information Systems; Infrastructure; Knowledge; Modeling; Process; Research; Research Personnel; System; Vision; biological systems; biomarker identification; data integration; diverse data; multiple data types; phenotypic data; tool

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， 识别生物标志物，破译机制，重建网络，并开发定量模型， 生物医学和临床研究人员以无缝的方式。一个非常有价值的东西是一个可以通过网络访问的 一站式商店提供这些功能。创建此类无缝基础设施是 我们倡议的愿景。我们建议开发生物医学数据共享空间， （BDCW），这将有助于克服生物医学数据集成的根本障碍。 BDCW将解决生物医学研究人员面临的两个基本障碍-第一个涉及 问题是，是否有可用的数据可以回答一个或多个生物医学问题（也 从研究的角度来看，这被称为用例），其次，这些数据如何被 集成分析，而不必经历繁琐的过程，无论是开发或 使用各种工具（这反过来又需要计算方法的工作知识）。 若干共同基金数据来源试图帮助最终用户维斯其提供的数据，但 与另一个共同基金数据源无缝互操作的能力。即使一个人要识别 适当的数据来源，以解决一个给定的研究问题，整合这些挑战 多样的数据是不平凡的。数据集成指的是将有关相同数据的信息 在不同信息系统中管理的实体，以跨不同系统呈现统一的数据视图。 它还指对多种类型数据的综合分析，包括在不同生物学领域收集的数据， 规模，发现新的知识，如何生物系统的功能（“机制”）。