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Int. J. Mol. Sci. 2017, 18(2), 412; doi:10.3390/ijms18020412

Big Data Analytics for Genomic Medicine

1
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA
2
BioSciKin Co., Ltd., Nanjing 210042, China
3
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
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)
View Full-Text   |   Download PDF [1212 KB, uploaded 15 February 2017]   |  

Abstract

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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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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