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10.18632/oncotarget.19481 http://scihub22266oqcxt.onion/10.18632/oncotarget.19481 C5601632!5601632!28938536     free     free     free 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 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Oncotarget 2017 ; 8 (35): 58050-60 Nephropedia Template TP {{tp|p=28938536|t=2017. Integrating omics data and protein interaction networks to prioritize driver genes in cancer |pdf=|usr=}}{{28938536}} gab.com Text Integrating omics data and protein interaction networks to prioritize driver genes in cancer JO: Oncotarget http://www.kidney.de/pget.php?&tw=1&pmid=28938536 #FOAvip Although numerous approaches have been proposed to discern driver from passenger, identification of driver genes remains a critical challenge in the cancer genomics field. Driver genes with low mutated frequency tend to be filtered in cancer research. In addition, the accumulation of different omics data necessitates the development of algorithmic frameworks for nominating putative driver genes. In this study, we presented a novel framework to identify driver genes through integrating multi-omics data such as somatic mutation, gene expression, and copy number alterations. We developed a computational approach to detect potential driver genes by virtue of their effect on their neighbors in network. Application to three datasets (head and neck squamous cell carcinoma (HNSC), thyroid carcinoma (THCA) and kidney renal clear cell carcinoma (KIRC)) from The Cancer Genome Atlas (TCGA), by comparing the Precision, Recall and F1 score, our method outperformed DriverNet and MUFFINN in all three datasets. In addition, our method was less affected by protein length compared with DriverNet. Lastly, our method not only identified the known cancer genes but also detected the potential rare drivers (PTPN6 in THCA, SRC, GRB2 and PTPN6 in KIRC, MAPK1 and SMAD2 in HNSC). Twit Text FOAVip Integrating omics data and protein interaction networks to prioritize driver genes in cancer ????Oncotarget http://www.kidney.de/pget.php?&tw=1&pmid=28938536 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28938536 #FOAvip English Wikipedia <ref>{{Cite pmid|28938536}}</ref> Integrating omics data and protein interaction networks to prioritize driver genes in cancer #MMPMID28938536 Zhang T; Zhang D Oncotarget 2017[Aug]; 8 (35): 58050-60 PMID28938536show ga Although numerous approaches have been proposed to discern driver from passenger, identification of driver genes remains a critical challenge in the cancer genomics field. Driver genes with low mutated frequency tend to be filtered in cancer research. In addition, the accumulation of different omics data necessitates the development of algorithmic frameworks for nominating putative driver genes. In this study, we presented a novel framework to identify driver genes through integrating multi-omics data such as somatic mutation, gene expression, and copy number alterations. We developed a computational approach to detect potential driver genes by virtue of their effect on their neighbors in network. Application to three datasets (head and neck squamous cell carcinoma (HNSC), thyroid carcinoma (THCA) and kidney renal clear cell carcinoma (KIRC)) from The Cancer Genome Atlas (TCGA), by comparing the Precision, Recall and F1 score, our method outperformed DriverNet and MUFFINN in all three datasets. In addition, our method was less affected by protein length compared with DriverNet. Lastly, our method not only identified the known cancer genes but also detected the potential rare drivers (PTPN6 in THCA, SRC, GRB2 and PTPN6 in KIRC, MAPK1 and SMAD2 in HNSC). äIntegrating omics data and protein interaction networks to prioritize (epmc).pdf DeepDyve Pubget Overpricing