The goal of this study for designing and developing of novel anticancer peptides arises mainly from the following aspects: (i) establishing a benchmark dataset with low homology sequences to train and test the prediction model with the rigorous validation and assessment, (ii) constructing a prediction model based on a newly-developed efficient linear method (ELM) (originally introduced by Shoombuatong et al that can automatically identify feature importance from a large pool of protein features (AAC, DPC, and PCP) for providing insights into the physicochemical properties as well as mechanisms of anticancer peptides, (iii) designing highly efficacious anticancer peptide using ELM model, (iv) validating the ability of the proposed anticancer peptide using the wet experimental technique and (v) developing a user-friendly web server to facilitate the cancer biologists or experimental scientists for designing and developing the better therapeutic peptides.
