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)
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-TextKeywords:
Big Data analytics; clinically actionable genetic variants; electronic health records; healthcare; next-generation sequencing
▼
Figures
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).
Share & Cite This Article
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.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
Related Articles
Article Metrics
Comments
[Return to top]
Int. J. Mol. Sci.
EISSN 1422-0067
Published by MDPI AG, Basel, Switzerland
RSS
E-Mail Table of Contents Alert