ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree - Li, Yanjuan; Ma, Di; Chen, Dong; Chen, Yu (Frontiers in Genetics, 2023)
Cancer is one of the most dangerous diseases, it annually kills millions of people globally. Anticancer peptides can treat cancer with small side effects. Therefore, identifying anticancer peptide has been a hot topic. In this study, we proposed an improved anti-cancer peptide predictor named ACP-GBDT to identify anticancer peptide based on sequence information. ACP-GBDT employed mixed-feature of AAIndex and SVMProt-188D to encode peptide sequences, and adopted GBDT to train the prediction model. The mixed-feature considers both the amino acid composition information of peptide sequence and the physicochemical properties of amino acid, it can effectively distinguish anticancer peptides from non-anticancer ones which is verified by experiments of independent test and 10-fold cross-validation. Finally, the comparison results on benchmark dataset proved that the developed ACP-GBDT is more simple and effective in the identification of anticancer peptides than existing methods.