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10.1016/j.semnephrol.2015.04.003 http://scihub22266oqcxt.onion/10.1016/j.semnephrol.2015.04.003 C4518206!4518206 !26215861     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26215861 &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 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26215861 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Semin+Nephrol 2015 ; 35 (3 ): 237-44 Nephropedia Template TP {{tp|p=26215861 |t=2015. Genes Caught In Flagranti: Integrating Renal Transcriptional Profiles With Genotypes and Phenotypes |pdf=|usr=}}{{26215861 }} gab.com Text Genes Caught In Flagranti: Integrating Renal Transcriptional Profiles With Genotypes and Phenotypes JO: Semin Nephrol http://www.kidney.de/pget.php?&tw=1&pmid=26215861 #FOAvip In the past decade, population genetics has gained tremendous success in identifying genetic variations that are statistically relevant to renal diseases and kidney function. However, it is challenging to interpret the functional relevance of the genetic variations found by population genetics studies. In this review, we discuss studies that integrate multiple levels of data, especially transcriptome profiles and phenotype data, to assign functional roles of genetic variations involved in kidney function. Furthermore, we introduce state-of-the-art machine learning algorithms, Bayesian networks, support vector machines, and Gaussian process regression, which have been applied successfully to integrating genetic, regulatory, and clinical information to predict clinical outcomes. These methods are likely to be deployed successfully in the nephrology field in the near future. Twit Text FOAVip Genes Caught In Flagranti: Integrating Renal Transcriptional Profiles With Genotypes and Phenotypes ????Semin Nephrol http://www.kidney.de/pget.php?&tw=1&pmid=26215861 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26215861 #FOAvip English Wikipedia <ref>{{Cite pmid|26215861 }}</ref> Genes Caught In Flagranti: Integrating Renal Transcriptional Profiles With Genotypes and Phenotypes #MMPMID26215861 Guan Y ; Martini S ; Mariani LH Semin Nephrol 2015[May]; 35 (3 ): 237-44 PMID26215861 show ga In the past decade, population genetics has gained tremendous success in identifying genetic variations that are statistically relevant to renal diseases and kidney function. However, it is challenging to interpret the functional relevance of the genetic variations found by population genetics studies. In this review, we discuss studies that integrate multiple levels of data, especially transcriptome profiles and phenotype data, to assign functional roles of genetic variations involved in kidney function. Furthermore, we introduce state-of-the-art machine learning algorithms, Bayesian networks, support vector machines, and Gaussian process regression, which have been applied successfully to integrating genetic, regulatory, and clinical information to predict clinical outcomes. These methods are likely to be deployed successfully in the nephrology field in the near future. |*Algorithms [MESH] |Computational Biology/*methods [MESH] |Gene Expression Profiling/*methods [MESH] |Gene Regulatory Networks/*genetics [MESH] |Genotype [MESH] |Humans [MESH] |Kidney Diseases/*genetics/metabolism [MESH] |Phenotype [MESH]Genes Caught In Flagranti: Integrating Renal Transcriptional Profiles (epmc).pdf DeepDyve Pubget Overpricing