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10.1080/15592294.2017.1329068 http://scihub22266oqcxt.onion/10.1080/15592294.2017.1329068 C5687335!5687335!28524769     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Epigenetics 2017 ; 12 (7): 505-14 Nephropedia Template TP {{tp|p=28524769|t=2017. Machine learning for epigenetics and future medical applications |pdf=|usr=}}{{28524769}} gab.com Text Machine learning for epigenetics and future medical applications JO: Epigenetics http://www.kidney.de/pget.php?&tw=1&pmid=28524769 #FOAvip Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets. The power of this novel ML approach and our ability to predict epigenetic phenomena and associated disease is suggested. The current approach requires extensive computation of features over the genome. A promising new approach is to introduce Deep Learning (DL) for the generation and simultaneous computation of novel genomic features tuned to the classification task. This approach can be used with any genomic or biological data set applied to medicine. The application of molecular epigenetic data in advanced machine learning analysis to medicine is the focus of this review. Twit Text FOAVip Machine learning for epigenetics and future medical applications ????Epigenetics http://www.kidney.de/pget.php?&tw=1&pmid=28524769 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28524769 #FOAvip English Wikipedia <ref>{{Cite pmid|28524769}}</ref> Machine learning for epigenetics and future medical applications #MMPMID28524769 Holder LB; Haque MM; Skinner MK Epigenetics 2017[]; 12 (7): 505-14 PMID28524769show ga Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets. The power of this novel ML approach and our ability to predict epigenetic phenomena and associated disease is suggested. The current approach requires extensive computation of features over the genome. A promising new approach is to introduce Deep Learning (DL) for the generation and simultaneous computation of novel genomic features tuned to the classification task. This approach can be used with any genomic or biological data set applied to medicine. The application of molecular epigenetic data in advanced machine learning analysis to medicine is the focus of this review. äMachine learning for epigenetics and future medical applications (epmc).pdf DeepDyve Pubget Overpricing