Integrative methods for analyzing big data in precision medicine

被引:134
|
作者
Gligorijevic, Vladimir [1 ]
Malod-Dognin, Noel [1 ]
Przulj, Natasa [1 ]
机构
[1] Univ London Imperial Coll Sci Technol & Med, Dept Comp, London SW7 2AZ, England
基金
欧洲研究理事会; 美国国家科学基金会;
关键词
Big data; Bioinformatics; Integration methods; Personalized medicine; GENE-EXPRESSION; SYSTEMATIC IDENTIFICATION; PATTERN DISCOVERY; CANCER; GENOMICS; INFORMATION; MICROARRAY; DISEASE; PROTEOMICS; RESOURCE;
D O I
10.1002/pmic.201500396
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of Big Data in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face.
引用
收藏
页码:741 / 758
页数:18
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