Anticancer peptides (ACPs) emerged as promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from the given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as promising tool that helps experimental scientists for the prediction of 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 two-step feature selection protocol on seven feature encodings that covers various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtain their corresponding optimal feature-based models; the resultant predicted probability of ACPs were further utilized as feature vector. (ii) the predicted probability feature vectors were in turn used as an input to support vector machine to develop the final prediction model.
