MACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.

ABSTRACT: Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for helping experimental scientists predict ACPs. However, the performance of existing methods still needs to be improved. In this study, we present a novel approach for the accurate prediction of ACPs, which involves the following two steps: (i) We applied a two-step feature selection protocol on seven feature encodings that cover various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtained their corresponding optimal feature-based models. The resu

SUBMITTER: Boopathi V 

PROVIDER: S-EPMC6514805 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature