Biomedical Data Commons Workbench (BDCW)

生物医学的未来将依赖于根据上下文整合基因和表型数据的能力 通过以下方式识别生物标志物、破译机制、重建网络和开发定量模型 生物医学和临床研究人员之间的无缝连接。最有价值的是一个可以通过网络访问的 提供这些功能的一站式商店。创建这样的无缝基础设施是 我们倡议的愿景。我们建议开发生物医学数据共享工作台 (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，是否有数据可以回答一个或多个生物医学问题(还 从研究的角度称为用例)和第二，这样的数据怎么可能 综合分析，无需经历开发或开发的繁琐过程 使用各种工具(这反过来需要计算方法的实用知识)。 几个共同基金数据源试图帮助终端用户了解他们提供的数据，但没有 能够与另一个共同基金数据源无缝互操作。即使有人认出 解决给定研究问题的适当数据来源，即整合这些数据来源的挑战 多样化的数据不是无关紧要的。数据集成指的是将关于同一数据的信息组合在一起的任务 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。 它还指对多种类型的数据进行综合分析，包括在不同生物领域收集的数据 规模，以发现关于生物系统如何运作的新知识(“机制”)。

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”).