The Center for Predictive Computational Phenotyping-1 Overall

预测计算表型中心-1 总体

• 批准号: 9266344
• 负责人: Mark W. Craven
• 金额: $ 269.23万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 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/9266344
• 关键词: Address; Biological; Blood coagulation; Clinical; Complex; Computational algorithm; Computer software; Computing Methodologies; Data; Data Collection; Data Science; Data Set; Data Sources; Diagnosis; Disease; Electronic Health Record; Environment; Exhibits; General Population; Generations; Genomics; Genotype; Greek; Health; Human; Human Biology; Individual; Knowledge; Learning; Machine Learning; Measures; Medical Imaging; Methods; Modeling; Molecular Profiling; Monitor; Myocardial Infarction; Organism; Output; Patients; Phenotype; Population; Postdoctoral Fellow; Property; Regulatory Element; Resources; Risk; Risk Assessment; Sampling; Science; Statistical Algorithm; Statistical Models; Time; Training Activity; biomedical scientist; clinical investigation; clinical predictors; education research; graduate student; high dimensionality; improved; innovation; interest; malignant breast neoplasm; novel strategies; outcome forecast; predictive modeling; public health relevance; success; tool; treatment planning; undergraduate student

DESCRIPTION (provided by applicant): The biomedical sciences are being radically transformed by advances in our ability to monitor, record, store and integrate information characterizing human biology and health at scales that range from individual molecules to large populations of subjects. This wealth of information has the potential to substantially advance both our understanding of human biology and our ability to improve human health. Perhaps the most central and general approach for exploiting biomedical data is to use methods from machine learning and statistical modeling to infer predictive models. Such models take as input observable data representing some object of interest, and produce as output a prediction about a particular, unobservable property of the object. This approach has proven to be of high value for a wide range of biomedical tasks, but numerous significant challenges remain to be solved in order for the full potential of predictive modeling to be realized. To address these challenges, we propose to establish The Center for Predictive Computational Phenotyping (CPCP). Our proposed center will focus on a broad range of problems that can be cast as computational phenotyping. Although some phenotypes are easily measured and interpreted, and are available in an accessible format, a wide range of scientifically and clinically important phenotypes do not satisfy these criteria. In such cases, computational phenotyping methods are required either to (i) extract a relevant phenotype from a complex data source or collection of heterogeneous data sources, (ii) predict clinically important phenotypes before they are exhibited, or (iii) do both in the same application.

描述（由申请人提供）：生物医学科学正在从根本上改变我们的能力的进步，以监测，记录，存储和整合信息表征人类生物学和健康的规模，从单个分子到大群体的主题。这些丰富的信息有可能大大提高我们对人类生物学的理解和改善人类健康的能力。也许利用生物医学数据的最核心和最通用的方法是使用机器学习和统计建模的方法来推断预测模型。这样的模型将表示感兴趣的某个对象的可观察数据作为输入，并产生关于对象的特定不可观察属性的预测作为输出。这种方法已被证明对广泛的生物医学任务具有很高的价值，但为了实现预测建模的全部潜力，仍有许多重大挑战有待解决。 为了应对这些挑战，我们建议建立预测计算表型分析中心（CPCP）。我们建议的中心将集中在一个广泛的问题，可以作为计算表型铸造。尽管一些表型很容易测量和解释，并且可以以可访问的格式获得，但广泛的科学和临床重要表型不满足这些标准。在这种情况下，需要计算表型分析方法（i）从复杂的数据源或异质数据源的集合中提取相关表型，（ii）在表现出临床重要的表型之前预测它们，或（iii）在同一应用中进行两者。