Multi-label Learning for Predicting the Activities of Antimicrobial Peptides

Abstract

Abstract Antimicrobial peptides (AMPs) are peptide antibiotics with a broad spectrum of antimicrobial activities. Activity prediction of AMPs from their amino acid sequences is of great therapeutic importance but imposes challenges on prediction methods due to label interactions. In this paper we propose a novel multi-label learning model to address this problem. A weighted K-nearest neighbor classifier is adopted for efficient representation learning of the sequence data. A multiple linear regression model is then employed to learn a mapping from the classifier score vectors to the target labels, with label correlations considered. Several popular multi-label learning algorithms and feature extraction methods were tested on a comprehensive, up-to-date AMP dataset with twelve biological activities covered and its filtered version with five activities covered. The experimental results showed that our proposed method has competitive performance with previous works and could be used as a powerful engine for activity prediction of AMPs.