MLACP: machine-learning-based prediction of anticancer peptides.

ABSTRACT: 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

SUBMITTER: Manavalan B 

PROVIDER: S-EPMC5652333 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature