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10.3390/ijms18020412 http://scihub22266oqcxt.onion/10.3390/ijms18020412 C5343946!5343946!28212287     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28212287.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Int+J+Mol+Sci 2017 ; 18 (2): ä Nephropedia Template TP {{tp|p=28212287|t=2017. Big Data Analytics for Genomic Medicine |pdf=|usr=}}{{28212287}} gab.com Text Big Data Analytics for Genomic Medicine JO: Int J Mol Sci http://www.kidney.de/pget.php?&tw=1&pmid=28212287 #FOAvip 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. Twit Text FOAVip Big Data Analytics for Genomic Medicine ????Int J Mol Sci http://www.kidney.de/pget.php?&tw=1&pmid=28212287 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28212287 #FOAvip English Wikipedia <ref>{{Cite pmid|28212287}}</ref> Big Data Analytics for Genomic Medicine #MMPMID28212287 He KY; Ge D; He MM Int J Mol Sci 2017[Feb]; 18 (2): ä PMID28212287show ga 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. äBig Data Analytics for Genomic Medicine (epmc).pdf DeepDyve Pubget Overpricing