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10.1038/srep45714 http://scihub22266oqcxt.onion/10.1038/srep45714 C5377940!5377940!28368026     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       Sci+Rep 2017 ; 7 (ä): ä Nephropedia Template TP {{tp|p=28368026|t=2017. Network-based characterization and prediction of human DNA repair genes and pathways |pdf=|usr=}}{{28368026}} gab.com Text Network-based characterization and prediction of human DNA repair genes and pathways JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=28368026 #FOAvip Network biology is a useful strategy to understand cell?s functional organization. In this study, for the first time, we successfully introduced network approaches to study properties of human DNA repair genes. Compared with non-DNA repair genes, we found distinguishing features for DNA repair genes: (i) they tend to have higher degrees; (ii) they tend to be located at global network center; (iii) they tend to interact directly with each other. Based on these features, we developed the first algorithm to predict new DNA repair genes. We tested several machine-learning models and found that support vector machine with kernel function of radial basis function (RBF) achieve the best performance, with precision?=?0.74 and area under curve (AUC)?=?0.96. In the end, we applied the algorithm to predict new DNA repair genes and got 32 new candidates. Literature supporting four of the predictions was found. We believe the network approaches introduced here might open a new avenue to understand DNA repair genes and pathways. The suggested algorithm and the predicted genes might be helpful for scientists in the field. Twit Text FOAVip Network-based characterization and prediction of human DNA repair genes and pathways ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=28368026 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28368026 #FOAvip English Wikipedia <ref>{{Cite pmid|28368026}}</ref> Network-based characterization and prediction of human DNA repair genes and pathways #MMPMID28368026 Li YH; Zhang GG Sci Rep 2017[]; 7 (ä): ä PMID28368026show ga Network biology is a useful strategy to understand cell?s functional organization. In this study, for the first time, we successfully introduced network approaches to study properties of human DNA repair genes. Compared with non-DNA repair genes, we found distinguishing features for DNA repair genes: (i) they tend to have higher degrees; (ii) they tend to be located at global network center; (iii) they tend to interact directly with each other. Based on these features, we developed the first algorithm to predict new DNA repair genes. We tested several machine-learning models and found that support vector machine with kernel function of radial basis function (RBF) achieve the best performance, with precision?=?0.74 and area under curve (AUC)?=?0.96. In the end, we applied the algorithm to predict new DNA repair genes and got 32 new candidates. Literature supporting four of the predictions was found. We believe the network approaches introduced here might open a new avenue to understand DNA repair genes and pathways. The suggested algorithm and the predicted genes might be helpful for scientists in the field. äNetwork-based characterization and prediction of human DNA repair gene(epmc).pdf DeepDyve Pubget Overpricing