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10.1073/pnas.1717139115 http://scihub22266oqcxt.onion/10.1073/pnas.1717139115 C5879673!5879673!29531073     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Proc+Natl+Acad+Sci+U+S+A 2018 ; 115 (13): E2970-9 Nephropedia Template TP {{tp|p=29531073|t=2018. Predicting cancer outcomes from histology and genomics using convolutional networks |pdf=|usr=}}{{29531073}} gab.com Text Predicting cancer outcomes from histology and genomics using convolutional networks JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=29531073 #FOAvip Predicting the expected outcome of patients diagnosed with cancer is a critical step in treatment. Advances in genomic and imaging technologies provide physicians with vast amounts of data, yet prognostication remains largely subjective, leading to suboptimal clinical management. We developed a computational approach based on deep learning to predict the overall survival of patients diagnosed with brain tumors from microscopic images of tissue biopsies and genomic biomarkers. This method uses adaptive feedback to simultaneously learn the visual patterns and molecular biomarkers associated with patient outcomes. Our approach surpasses the prognostic accuracy of human experts using the current clinical standard for classifying brain tumors and presents an innovative approach for objective, accurate, and integrated prediction of patient outcomes. Twit Text FOAVip Predicting cancer outcomes from histology and genomics using convolutional networks ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=29531073 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29531073 #FOAvip English Wikipedia <ref>{{Cite pmid|29531073}}</ref> Predicting cancer outcomes from histology and genomics using convolutional networks #MMPMID29531073 Mobadersany P; Yousefi S; Amgad M; Gutman DA; Barnholtz-Sloan JS; Velázquez Vega JE; Brat DJ; Cooper LAD Proc Natl Acad Sci U S A 2018[Mar]; 115 (13): E2970-9 PMID29531073show ga Predicting the expected outcome of patients diagnosed with cancer is a critical step in treatment. Advances in genomic and imaging technologies provide physicians with vast amounts of data, yet prognostication remains largely subjective, leading to suboptimal clinical management. We developed a computational approach based on deep learning to predict the overall survival of patients diagnosed with brain tumors from microscopic images of tissue biopsies and genomic biomarkers. This method uses adaptive feedback to simultaneously learn the visual patterns and molecular biomarkers associated with patient outcomes. Our approach surpasses the prognostic accuracy of human experts using the current clinical standard for classifying brain tumors and presents an innovative approach for objective, accurate, and integrated prediction of patient outcomes. äPredicting cancer outcomes from histology and genomics using convoluti(epmc).pdf DeepDyve Pubget Overpricing