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Int J Mol Sci. 2017 Feb 15;18(2). pii: E412. doi: 10.3390/ijms18020412.

Big Data Analytics for Genomic Medicine.

He KY1, Ge D2, He MM3,4.

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Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA.
BioSciKin Co., Ltd., Nanjing 210042, China.
BioSciKin Co., Ltd., Nanjing 210042, China.
Computation and Informatics in Biology and Medicine, University of Wisconsin-Madison, Madison, WI 53706, USA.


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.


Big Data analytics; clinically actionable genetic variants; electronic health records; healthcare; next-generation sequencing

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