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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 healthcare. METHODS: In this paper, we present -omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, 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 toward precision medicine. SIGNIFICANCE: Big data analytics makes sense of -omic and EHR data to improve healthcare outcome. It has long lasting societal impact.
Wu et al. (Mon,) studied this question.