大数据
数据科学
分析
精密医学
计算机科学
个性化医疗
数据分析
软件分析
数据质量
组学
数据挖掘
生物信息学
医学
软件
工程类
软件系统
病理
公制(单位)
软件建设
程序设计语言
生物
运营管理
作者
Po-Yen Wu,Chih‐Wen Cheng,Chanchala Kaddi,Janani Venugopalan,Ryan Hoffman,May D. Wang
标识
DOI:10.1109/tbme.2016.2573285
摘要
Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of healthcare.In this paper, we present -omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, mining, and modeling.To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR.Big data analytics is able to address -omic and EHR data challenges for paradigm shift toward precision medicine.Big data analytics makes sense of -omic and EHR data to improve healthcare outcome. It has long lasting societal impact.
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