Prediction of antioxidant proteins using hybrid feature representation method and random forest

Natural antioxidant proteins are mainly found in plants and animals, which interact to eliminate excessive free radicals and protect cells and DNA from damage, prevent and treat some diseases. Therefore, accurate identification of antioxidant proteins is important for the development of new drugs and research of related diseases. This article proposes novel method based on the combination of random forest and hybrid features that can accurately predict antioxidant proteins. Four single feature extraction methods (188D, profile-based Auto-cross covariance (ACC-PSSM), N-gram, and g-gap) and hybrid feature representation methods were used to feature extraction. Three feature selection methods (MRMD, t-SNE, and the optimal feature set selection) were adopted to determine the optimal features. The new hybrid feature vectors derived by combining 188D with the other three features all have indicators ranging from 0.9550 to 0.9990. The novel method showed better performance compared with the other methods.