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10.1093/bib/bbu024 http://scihub22266oqcxt.onion/10.1093/bib/bbu024 C4375394!4375394!25053743     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=25053743&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\25053743.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Brief+Bioinform 2015 ; 16 (2): 183-92 Nephropedia Template TP {{tp|p=25053743|t=2015. Kernel methods for large-scale genomic data analysis |pdf=|usr=}}{{25053743}} gab.com Text Kernel methods for large-scale genomic data analysis JO: Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=25053743 #FOAvip Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by today?s explosive data growth in genomics. They provide a practical and principled approach to learning how a large number of genetic variants are associated with complex phenotypes, to help reveal the complexity in the relationship between the genetic markers and the outcome of interest. In this review, we highlight the potential key role it will have in modern genomic data processing, especially with regard to integration with classical methods for gene prioritizing, prediction and data fusion. Twit Text FOAVip Kernel methods for large-scale genomic data analysis ????Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=25053743 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25053743 #FOAvip English Wikipedia <ref>{{Cite pmid|25053743}}</ref> Kernel methods for large-scale genomic data analysis #MMPMID25053743 Wang X; Xing EP; Schaid DJ Brief Bioinform 2015[Mar]; 16 (2): 183-92 PMID25053743show ga Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by today?s explosive data growth in genomics. They provide a practical and principled approach to learning how a large number of genetic variants are associated with complex phenotypes, to help reveal the complexity in the relationship between the genetic markers and the outcome of interest. In this review, we highlight the potential key role it will have in modern genomic data processing, especially with regard to integration with classical methods for gene prioritizing, prediction and data fusion. äKernel methods for large-scale genomic data analysis (epmc).pdf DeepDyve Pubget Overpricing