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10.1155/2016/6802832 http://scihub22266oqcxt.onion/10.1155/2016/6802832 C5021882!5021882!27660761     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 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Res+Int 2016 ; 2016 (ä): ä Nephropedia Template TP {{tp|p=27660761|t=2016. ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier |pdf=|usr=}}{{27660761}} gab.com Text ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=27660761 #FOAvip Protein fold classification plays an important role in both protein functional analysis and drug design. The number of proteins in PDB is very large, but only a very small part is categorized and stored in the SCOPe database. Therefore, it is necessary to develop an efficient method for protein fold classification. In recent years, a variety of classification methods have been used in many protein fold classification studies. In this study, we propose a novel classification method called proFold. We import protein tertiary structure in the period of feature extraction and employ a novel ensemble strategy in the period of classifier training. Compared with existing similar ensemble classifiers using the same widely used dataset (DD-dataset), proFold achieves 76.2% overall accuracy. Another two commonly used datasets, EDD-dataset and TG-dataset, are also tested, of which the accuracies are 93.2% and 94.3%, higher than the existing methods. ProFold is available to the public as a web-server. Twit Text FOAVip ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=27660761 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27660761 #FOAvip English Wikipedia <ref>{{Cite pmid|27660761}}</ref> ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier #MMPMID27660761 Chen D; Tian X; Zhou B; Gao J Biomed Res Int 2016[]; 2016 (ä): ä PMID27660761show ga Protein fold classification plays an important role in both protein functional analysis and drug design. The number of proteins in PDB is very large, but only a very small part is categorized and stored in the SCOPe database. Therefore, it is necessary to develop an efficient method for protein fold classification. In recent years, a variety of classification methods have been used in many protein fold classification studies. In this study, we propose a novel classification method called proFold. We import protein tertiary structure in the period of feature extraction and employ a novel ensemble strategy in the period of classifier training. Compared with existing similar ensemble classifiers using the same widely used dataset (DD-dataset), proFold achieves 76.2% overall accuracy. Another two commonly used datasets, EDD-dataset and TG-dataset, are also tested, of which the accuracies are 93.2% and 94.3%, higher than the existing methods. ProFold is available to the public as a web-server. äProFold: Protein Fold Classification with Additional Structural Featur(epmc).pdf DeepDyve Pubget Overpricing