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支持基于暴露组的公共卫生发现的信息学与数据分析

Informatics and Data Analytics to Support Exposome-Based Discovery for Public Health.

作者信息

Manrai Arjun K, Cui Yuxia, Bushel Pierre R, Hall Molly, Karakitsios Spyros, Mattingly Carolyn J, Ritchie Marylyn, Schmitt Charles, Sarigiannis Denis A, Thomas Duncan C, Wishart David, Balshaw David M, Patel Chirag J

机构信息

Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115; email:

National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709; email:

出版信息

Annu Rev Public Health. 2017 Mar 20;38:279-294. doi: 10.1146/annurev-publhealth-082516-012737. Epub 2016 Dec 23.

Abstract

The complexity of the human exposome-the totality of environmental exposures encountered from birth to death-motivates systematic, high-throughput approaches to discover new environmental determinants of disease. In this review, we describe the state of science in analyzing the human exposome and provide recommendations for the public health community to consider in dealing with analytic challenges of exposome-based biomedical research. We describe extant and novel analytic methods needed to associate the exposome with critical health outcomes and contextualize the data-centered challenges by drawing parallels to other research endeavors such as human genomics research. We discuss efforts for training scientists who can bridge public health, genomics, and biomedicine in informatics and statistics. If an exposome data ecosystem is brought to fruition, it will likely play a role as central as genomic science has had in molding the current and new generations of biomedical researchers, computational scientists, and public health research programs.

摘要

人类暴露组(从出生到死亡所接触的所有环境暴露的总和)的复杂性促使人们采用系统的、高通量的方法来发现疾病新的环境决定因素。在本综述中,我们描述了分析人类暴露组的科学现状,并为公共卫生界应对基于暴露组的生物医学研究的分析挑战提供建议。我们描述了将暴露组与关键健康结果相关联所需的现有和新颖分析方法,并通过与人类基因组学研究等其他研究工作进行类比,将以数据为中心的挑战置于具体情境中。我们讨论了培训能够在信息学和统计学方面将公共卫生、基因组学和生物医学联系起来的科学家的努力。如果一个暴露组数据生态系统得以实现,它可能会像基因组科学在塑造当代和新一代生物医学研究人员、计算科学家以及公共卫生研究项目方面所起的作用一样核心。

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