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10.18632/oncotarget.20365 http://scihub22266oqcxt.onion/10.18632/oncotarget.20365 C5652333!5652333 !29100375     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\29100375 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Oncotarget 2017 ; 8 (44 ): 77121-77136 Nephropedia Template TP {{tp|p=29100375 |t=2017. MLACP: machine-learning-based prediction of anticancer peptides |pdf=|usr=}}{{29100375 }} gab.com Text MLACP: machine-learning-based prediction of anticancer peptides JO: Oncotarget http://www.kidney.de/pget.php?&tw=1&pmid=29100375 #FOAvip Cancer is the second leading cause of death globally, and use of therapeutic peptides to target and kill cancer cells has received considerable attention in recent years. Identification of anticancer peptides (ACPs) through wet-lab experimentation is expensive and often time consuming; therefore, development of an efficient computational method is essential to identify potential ACP candidates prior to in vitro experimentation. In this study, we developed support vector machine- and random forest-based machine-learning methods for the prediction of ACPs using the features calculated from the amino acid sequence, including amino acid composition, dipeptide composition, atomic composition, and physicochemical properties. We trained our methods using the Tyagi-B dataset and determined the machine parameters by 10-fold cross-validation. Furthermore, we evaluated the performance of our methods on two benchmarking datasets, with our results showing that the random forest-based method outperformed the existing methods with an average accuracy and Matthews correlation coefficient value of 88.7% and 0.78, respectively. To assist the scientific community, we also developed a publicly accessible web server at www.thegleelab.org/MLACP.html. Twit Text FOAVip MLACP: machine-learning-based prediction of anticancer peptides ????Oncotarget http://www.kidney.de/pget.php?&tw=1&pmid=29100375 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29100375 #FOAvip English Wikipedia <ref>{{Cite pmid|29100375 }}</ref> MLACP: machine-learning-based prediction of anticancer peptides #MMPMID29100375 Manavalan B ; Basith S ; Shin TH ; Choi S ; Kim MO ; Lee G Oncotarget 2017[Sep]; 8 (44 ): 77121-77136 PMID29100375 show ga Cancer is the second leading cause of death globally, and use of therapeutic peptides to target and kill cancer cells has received considerable attention in recent years. Identification of anticancer peptides (ACPs) through wet-lab experimentation is expensive and often time consuming; therefore, development of an efficient computational method is essential to identify potential ACP candidates prior to in vitro experimentation. In this study, we developed support vector machine- and random forest-based machine-learning methods for the prediction of ACPs using the features calculated from the amino acid sequence, including amino acid composition, dipeptide composition, atomic composition, and physicochemical properties. We trained our methods using the Tyagi-B dataset and determined the machine parameters by 10-fold cross-validation. Furthermore, we evaluated the performance of our methods on two benchmarking datasets, with our results showing that the random forest-based method outperformed the existing methods with an average accuracy and Matthews correlation coefficient value of 88.7% and 0.78, respectively. To assist the scientific community, we also developed a publicly accessible web server at www.thegleelab.org/MLACP.html. äMLACP: machine-learning-based prediction of anticancer peptides (epmc).pdf DeepDyve Pubget Overpricing