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10.1073/pnas.1424272112 http://scihub22266oqcxt.onion/10.1073/pnas.1424272112 C4485084!4485084!26056275     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26056275&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       Proc+Natl+Acad+Sci+U+S+A 2015 ; 112 (25): 7731-6 Nephropedia Template TP {{tp|p=26056275|t=2015. Inference of transcriptional regulation in cancers |pdf=|usr=}}{{26056275}} gab.com Text Inference of transcriptional regulation in cancers JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=26056275 #FOAvip We developed an efficient and accurate computational framework, RABIT (regression analysis with background integration), and comprehensively integrated public transcription factor (TF)-binding profiles with TCGA tumor-profiling datasets in 18 cancer types. To systematically search for cancer-associated TFs, RABIT controls the effect of tumor-confounding factors on transcriptional regulation, such as copy number alteration, DNA methylation, and TF somatic mutation. Our predicted TF regulatory activity in tumors is highly consistent with the knowledge from cancer gene databases and reveals many previously unidentified cancer-associated TFs. We also analyzed RNA-binding protein regulation in cancer and demonstrated that RABIT is a general platform for predicting oncogenic gene expression regulators. Twit Text FOAVip Inference of transcriptional regulation in cancers ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=26056275 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26056275 #FOAvip English Wikipedia <ref>{{Cite pmid|26056275}}</ref> Inference of transcriptional regulation in cancers #MMPMID26056275 Jiang P; Freedman ML; Liu JS; Liu XS Proc Natl Acad Sci U S A 2015[Jun]; 112 (25): 7731-6 PMID26056275show ga We developed an efficient and accurate computational framework, RABIT (regression analysis with background integration), and comprehensively integrated public transcription factor (TF)-binding profiles with TCGA tumor-profiling datasets in 18 cancer types. To systematically search for cancer-associated TFs, RABIT controls the effect of tumor-confounding factors on transcriptional regulation, such as copy number alteration, DNA methylation, and TF somatic mutation. Our predicted TF regulatory activity in tumors is highly consistent with the knowledge from cancer gene databases and reveals many previously unidentified cancer-associated TFs. We also analyzed RNA-binding protein regulation in cancer and demonstrated that RABIT is a general platform for predicting oncogenic gene expression regulators. äInference of transcriptional regulation in cancers (epmc).pdf DeepDyve Pubget Overpricing