Advanced Big Data Analytics for -Omic Data and Electronic Health Records: Toward Precision Medicine. - PubMed - NCBI
Advanced Big Data Analytics for -Omic Data and Electronic Health Records: Toward Precision Medicine.
Abstract
OBJECTIVE:
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 health care. METHODS:
In this article, we present -omic and EHR data characteristics, associated challenges, and data analytics including data pre-processing, mining, and modeling. RESULTS:
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. CONCLUSION:
Big data analytics is able to address -omic and EHR data challenges for paradigm shift towards precision medicine. SIGNIFICANCE:
Big data analytics makes sense of -omic and EHR data to improve healthcare outcome. It has long lasting societal impact.
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