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10.1038/srep43350 http://scihub22266oqcxt.onion/10.1038/srep43350 C5327384!5327384 !28240231     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 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\28240231 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Sci+Rep 2017 ; 7 (ä): 43350 Nephropedia Template TP {{tp|p=28240231 |t=2017. Identification of outcome-related driver mutations in cancer using conditional co-occurrence distributions |pdf=|usr=}}{{28240231 }} gab.com Text Identification of outcome-related driver mutations in cancer using conditional co-occurrence distributions JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=28240231 #FOAvip Previous methods proposed for the detection of cancer driver mutations have been based on the estimation of background mutation rate, impact on protein function, or network influence. In this paper, we instead focus on those factors influencing patient survival. To this end, an approximation of the log-rank test has been systematically applied, even though it assumes a large and similar number of patients in both risk groups, which is violated in cancer genomics. Here, we propose VALORATE, a novel algorithm for the estimation of the null distribution for the log-rank, independent of the number of mutations. VALORATE is based on conditional distributions of the co-occurrences between events and mutations. The results, achieved through simulations, comparisons with other methods, analyses of TCGA and ICGC cancer datasets, and validations, suggest that VALORATE is accurate, fast, and can identify both known and novel gene mutations. Our proposal and results may have important implications in cancer biology, bioinformatics analyses, and ultimately precision medicine. Twit Text FOAVip Identification of outcome-related driver mutations in cancer using conditional co-occurrence distributions ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=28240231 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28240231 #FOAvip English Wikipedia <ref>{{Cite pmid|28240231 }}</ref> Identification of outcome-related driver mutations in cancer using conditional co-occurrence distributions #MMPMID28240231 Treviño V ; Martínez-Ledesma E ; Tamez-Peña J Sci Rep 2017[Feb]; 7 (ä): 43350 PMID28240231 show ga Previous methods proposed for the detection of cancer driver mutations have been based on the estimation of background mutation rate, impact on protein function, or network influence. In this paper, we instead focus on those factors influencing patient survival. To this end, an approximation of the log-rank test has been systematically applied, even though it assumes a large and similar number of patients in both risk groups, which is violated in cancer genomics. Here, we propose VALORATE, a novel algorithm for the estimation of the null distribution for the log-rank, independent of the number of mutations. VALORATE is based on conditional distributions of the co-occurrences between events and mutations. The results, achieved through simulations, comparisons with other methods, analyses of TCGA and ICGC cancer datasets, and validations, suggest that VALORATE is accurate, fast, and can identify both known and novel gene mutations. Our proposal and results may have important implications in cancer biology, bioinformatics analyses, and ultimately precision medicine. |*Algorithms [MESH] |*Gene Expression Regulation, Neoplastic [MESH] |*Mutation [MESH] |Computational Biology/methods/*statistics & numerical data [MESH] |Datasets as Topic [MESH] |Gene Regulatory Networks [MESH] |Humans [MESH] |Neoplasm Proteins/*genetics/metabolism [MESH] |Neoplasms/*genetics/metabolism/mortality/pathology [MESH] |Precision Medicine [MESH] |Software [MESH] |Statistical Distributions [MESH]Identification of outcome-related driver mutations in cancer using con(epmc).pdf DeepDyve Pubget Overpricing