Center for causal Modeling and discovery of Biomedical Knowledge from Big Data

大数据因果建模和生物医学知识发现中心

• 批准号: 8935874
• 负责人: Ivet Bahar
• 金额: $ 273万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-29 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8935874
• 关键词: Academia; Address; Advanced Development; Algorithmic Software; Algorithms; Automobile Driving; Big Data; Biology; Biomedical Research; Code; Collaborations; Complex; Computer software; Computing Methodologies; Data; Data Set; Databases; Development; Disease; Education and Outreach; Educational process of instructing; Feedback; Goals; Government; Health; Human; Industry; Knowledge; Lead; Location; Logic; Medicine; Methods; Mission; Modeling; Monitor; Nature; Policies; Qualitative Evaluations; Quantitative Evaluations; Research; Research Infrastructure; Research Personnel; Science; Scientist; Site; Software Tools; Staging; TimeLine; Training; Work; biomedical scientist; career; causal model; computer based statistical methods; design; graduate student; improved; innovation; meetings; method development; programs; public health relevance; symposium; tool

DESCRIPTION (provided by applicant): Much of science consists of discovering and modeling causal relationships that occur in nature. Increasingly big data are being used to drive such discoveries. There is a pressing need for methods that can efficiently infer causal networks from large and diverse types of biomedical data and background knowledge. This center of excellence will develop, implement, and evaluate an integrated set of tools that support causal modeling and discovery (CMD) of biomedical knowledge from very large and complex biomedical data. We also plan to actively share our knowledge, methods, and tools with others, through an innovative set of training and consortium activities. In the past 25 years, there has been tremendous progress in developing general computational methods for representing and discovering causal knowledge from data, based on a representation called causal Bayesian networks (CBNs). These methods have been applied successfully in a wide range of fields, including medicine and biology. While much progress has been made in the development of these computational methods, they are not readily available, sufficiently efficient, nor easy to use by biomedical scientists, and they have not been reconfigured to exploit the increasingly Big Data available for analysis. This Center will make these methods widely available, highly efficient when applied to big datasets, and easy to use. The proposed Center will provide a powerful set of concepts and tools that accelerate the discovery and sharing of causal knowledge derived from very large and complex biomedical datasets. The approaches and products emanating from this center of excellence are likely to have a significant positive impact on our understanding of health and disease, and thereby on the improvement of human health.

描述（由申请人提供）：大部分科学都包括发现和建模自然界中发生的因果关系。越来越多的大数据被用来推动这些发现。迫切需要能够从大量不同类型的生物医学数据和背景知识中有效推断因果网络的方法。该卓越中心将开发，实施和评估一套集成的工具，以支持从非常庞大和复杂的生物医学数据中进行生物医学知识的因果建模和发现（CMD）。我们还计划通过一系列创新的培训和联盟活动，积极与他人分享我们的知识、方法和工具。在过去的25年里，基于一种称为因果贝叶斯网络（CBN）的表示方法，开发用于从数据中表示和发现因果知识的通用计算方法取得了巨大进展。这些方法已成功地应用于广泛的领域，包括医学和生物学。虽然这些计算方法的发展取得了很大进展，但它们并不容易获得，效率不够高，也不容易被生物医学科学家使用，而且它们还没有被重新配置以利用越来越多的大数据进行分析。该中心将使这些方法广泛可用，在应用于大数据集时非常有效，并且易于使用。拟议中的中心将提供一套强大的概念和工具，加速发现和共享从非常庞大和复杂的生物医学数据集得出的因果知识。从这个卓越中心产生的方法和产品可能会对我们对健康和疾病的理解产生重大的积极影响，从而改善人类健康。