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10.1016/j.neo.2017.04.008 http://scihub22266oqcxt.onion/10.1016/j.neo.2017.04.008 C5476451!5476451!28624626     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       Neoplasia 2017 ; 19 (7): 574-82 Nephropedia Template TP {{tp|p=28624626|t=2017. Alternative Polyadenylation Patterns for Novel Gene Discovery and Classification in Cancer |pdf=|usr=}}{{28624626}} gab.com Text Alternative Polyadenylation Patterns for Novel Gene Discovery and Classification in Cancer JO: Neoplasia http://www.kidney.de/pget.php?&tw=1&pmid=28624626 #FOAvip Certain aspects of diagnosis, prognosis, and treatment of cancer patients are still important challenges to be addressed. Therefore, we propose a pipeline to uncover patterns of alternative polyadenylation (APA), a hidden complexity in cancer transcriptomes, to further accelerate efforts to discover novel cancer genes and pathways. Here, we analyzed expression data for 1045 cancer patients and found a significant shift in usage of poly(A) signals in common tumor types (breast, colon, lung, prostate, gastric, and ovarian) compared to normal tissues. Using machine-learning techniques, we further defined specific subsets of APA events to efficiently classify cancer types. Furthermore, APA patterns were associated with altered protein levels in patients, revealed by antibody-based profiling data, suggesting functional significance. Overall, our study offers a computational approach for use of APA in novel gene discovery and classification in common tumor types, with important implications in basic research, biomarker discovery, and precision medicine approaches. Twit Text FOAVip Alternative Polyadenylation Patterns for Novel Gene Discovery and Classification in Cancer ????Neoplasia http://www.kidney.de/pget.php?&tw=1&pmid=28624626 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28624626 #FOAvip English Wikipedia <ref>{{Cite pmid|28624626}}</ref> Alternative Polyadenylation Patterns for Novel Gene Discovery and Classification in Cancer #MMPMID28624626 Begik O; Oyken M; Cinkilli Alican T; Can T; Erson-Bensan AE Neoplasia 2017[Jul]; 19 (7): 574-82 PMID28624626show ga Certain aspects of diagnosis, prognosis, and treatment of cancer patients are still important challenges to be addressed. Therefore, we propose a pipeline to uncover patterns of alternative polyadenylation (APA), a hidden complexity in cancer transcriptomes, to further accelerate efforts to discover novel cancer genes and pathways. Here, we analyzed expression data for 1045 cancer patients and found a significant shift in usage of poly(A) signals in common tumor types (breast, colon, lung, prostate, gastric, and ovarian) compared to normal tissues. Using machine-learning techniques, we further defined specific subsets of APA events to efficiently classify cancer types. Furthermore, APA patterns were associated with altered protein levels in patients, revealed by antibody-based profiling data, suggesting functional significance. Overall, our study offers a computational approach for use of APA in novel gene discovery and classification in common tumor types, with important implications in basic research, biomarker discovery, and precision medicine approaches. äAlternative Polyadenylation Patterns for Novel Gene Discovery and Clas(epmc).pdf DeepDyve Pubget Overpricing