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Big Data Analytics for Genomic Medicine

by 1, 2,* and 2,3,*
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA
BioSciKin Co., Ltd., Nanjing 210042, China
Computation and Informatics in Biology and Medicine, University of Wisconsin-Madison, Madison, WI 53706, USA
Authors to whom correspondence should be addressed.
Academic Editor: William Chi-shing Cho
Int. J. Mol. Sci. 2017, 18(2), 412;
Received: 24 October 2016 / Revised: 8 February 2017 / Accepted: 9 February 2017 / Published: 15 February 2017
(This article belongs to the Special Issue Precision Medicine—From Bench to Bedside)
Genomic medicine attempts to build individualized strategies for diagnostic or therapeutic decision-making by utilizing patients’ genomic information. Big Data analytics uncovers hidden patterns, unknown correlations, and other insights through examining large-scale various data sets. While integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a Big Data infrastructure exhibit challenges, they also provide a feasible opportunity to develop an efficient and effective approach to identify clinically actionable genetic variants for individualized diagnosis and therapy. In this paper, we review the challenges of manipulating large-scale next-generation sequencing (NGS) data and diverse clinical data derived from the EHRs for genomic medicine. We introduce possible solutions for different challenges in manipulating, managing, and analyzing genomic and clinical data to implement genomic medicine. Additionally, we also present a practical Big Data toolset for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs. View Full-Text
Keywords: Big Data analytics; clinically actionable genetic variants; electronic health records; healthcare; next-generation sequencing Big Data analytics; clinically actionable genetic variants; electronic health records; healthcare; next-generation sequencing
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MDPI and ACS Style

He, K.Y.; Ge, D.; He, M.M. Big Data Analytics for Genomic Medicine. Int. J. Mol. Sci. 2017, 18, 412.

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