Statistical Inference for Biomedical Big Data: Theory, Methods, and Tools

生物医学大数据的统计推断：理论、方法和工具

• 批准号: 1703077
• 负责人: Faming Liang
• 金额: $ 2万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-15 至 2018-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1703077&HistoricalAwards=false
• 关键词: Statistical; Inference; Biomedical; Big; Data

This award supports participation in the workshop "Statistical Inference for Biomedical Big Data: Theory, Methods, and Tools" to be held on the Gainesville campus of the University of Florida, Gainesville, Florida on April 7-8, 2017. Dramatic improvements in data collection and acquisition technologies in the past two decades have enabled scientists to collect vast amounts of health-related data in biomedical studies. If analyzed properly, these data can expand our knowledge and improve contemporary healthcare services from diagnosis to prevention to personalized treatment, and can also provide insights into reducing healthcare costs. However, biomedical data can be rather big and complex, and are often characterized by some mixture of high dimensionality, heterogeneity, high volume, high velocity, and high variety. Advances in biomedical big data analysis can greatly impact the development of biomedical sciences.This workshop will bring together some of the most prominent statisticians in biomedical big data and a selected group of local biomedical researchers in a collaborative setting to discuss and foster cutting-edge developments of statistical theory, methods, and tools for biomedical big data analysis. The format of this workshop is designed to foster interactions between statisticians and biomedical researchers. To facilitate communications between graduate students, junior researchers, and senior researchers, a poster session is included in the program. To disseminate the results of the meeting, the program and abstracts will be widely circulated, and modest travel funds for a large geographically diverse group of graduate students, postdocs, and junior faculty are planned. More information about the workshop can be found at http://biostat.ufl.edu/seminars/biostatistics-workshop/.

该奖项支持参加将于 2017 年 4 月 7 日至 8 日在佛罗里达州盖恩斯维尔佛罗里达大学盖恩斯维尔校区举办的研讨会“生物医学大数据的统计推断：理论、方法和工具”。数据的显着改进过去二十年的收集和获取技术使科学家能够在生物医学研究中收集大量与健康相关的数据。如果分析得当，这些数据可以扩展我们的知识并改善当代医疗保健服务，从诊断到预防再到个性化治疗，还可以提供降低医疗保健成本的见解。然而，生物医学数据可能相当大和复杂，并且通常具有高维度、异质性、高容量、高速度和高多样性的特征。生物医学大数据分析的进步可以极大地影响生物医学科学的发展。本次研讨会将汇集生物医学大数据领域最杰出的统计学家和选定的本地生物医学研究人员，在协作环境中讨论和促进前沿发展生物医学大数据分析的统计理论、方法和工具。本次研讨会的形式旨在促进统计学家和生物医学研究人员之间的互动。为了促进研究生、初级研究人员和高级研究人员之间的交流，该计划中包括了海报会议。为了传播会议结果，会议议程和摘要将广泛分发，并计划为来自不同地区的研究生、博士后和初级教师提供适度的旅行资金。有关研讨会的更多信息，请访问 http://biostat.ufl.edu/seminars/biostatistics-workshop/。