ACP-2DCNN: Deep learning-based model for improving prediction of anticancer peptides using two-dimensional convolutional neural network

Cancer is the most dangerous disease of humans, causing countless deaths and suffering. The treatment of cancer with cancer peptides is exciting as they have many attractive benefits. In recent years, many researchers have focused on anticancer peptides (ACPs) that are critical for the advancement of novel cancer therapies. The prediction of ACPs by experimental methods is costly and laborious and often generates unsatisfactory predictions. It is highly demanded to identify ACPs by advanced algorithms. In this study, we present a novel deep learning-based method named, ACP-2DCNN for improving the prediction of anticancer peptides. The important features are extracted by Dipeptide Deviation from Expected Mean (DDE) while model training and prediction are performed by Two-dimensional Convolutional Neural Network (2D CNN). The empirical results demonstrate that the proposed method has achieved the best performance and can predict ACPs more accurately comparatively existing methods in the literature.