背景(考古学)
组学
代谢组学
数据科学
数据集成
计算生物学
人口
计算机科学
选择(遗传算法)
生物
生物信息学
医学
数据挖掘
环境卫生
人工智能
古生物学
作者
Su H. Chu,Mengna Huang,Rachel S. Kelly,Elisa Benedetti,Jalal Siddiqui,Oana A. Zeleznik,Alexandre Pereira,David M. Herrington,Craig E. Wheelock,Jan Krumsiek,Michael J. McGeachie,Steven C. Moore,Peter Kraft,Ewy A. Mathé,Jessica Lasky‐Su
出处
期刊:Metabolites
[Multidisciplinary Digital Publishing Institute]
日期:2019-06-18
卷期号:9 (6): 117-117
被引量:62
标识
DOI:10.3390/metabo9060117
摘要
It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.
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