iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest - Zhao, Dongxu; Teng, Zhixia; Li, Yanjuan; Chen, Dong (Frontiers in Genetics, 2021)
Recently, several anti-inflammatory peptides (AIPs) have been found in the process of inflammatory response, and these peptides have been used to treat some inflammatory and autoimmune disease. Therefore, identifying AIPs accurately from given amino acid sequences is critical for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics and the acceleration of their application in therapy. In the paper, a random forest-based model called iAIP for identifying AIPs is proposed. Firstly, the original samples were encoded with three feature extraction methods, including g-gap dipeptide composition(GDC), dipeptide deviation from the expected mean(DDE) and amino acid composition(AAC). Secondly, the optimal feature subset is generated by a two-step feature selection method, in which the feature is ranked by the Analysis of Variance (ANOVA) method, and the optimal feature subset is generated by the incremental feature selection strategy. Finally, the optimal feature subset is inputted to random forest, and the identification model is constructed. Experiment result showed that the iAIPs achieved an AUC value of 0.822 on an independent test dataset, which indicated that our proposed model has better performance than the existing methods. Furthermore, the extraction of features for peptide sequences provides the basis for evolutionary analysis. The study of peptide identification is helpful to understand the diversity of species and analyze the evolutionary history of species.