A novel synonymous processing method based on amino acid substitution matrics for the classification of G-protein-coupled receptors

Extracting valuable features and filtering out redundancy are the key challenges to determine the overall classification performance for G-protein-coupled receptors (GPCRs). In this study, we consider improving the feature synonym problem, and put forward a novel feature knowledge mining strategy based on functional word clustering and integration. The essence behind the method is the novel feature knowledge mining strategy. Through evaluating the independence of each candidate feature using the evolutionary hypothesis based on residue substitution matrices, clustering candidate features, and fusing them by retaining the main functional words, the proposed strategy adds a layer between the feature extraction layer and the prediction layer. Based on the proposed method, four classic machine learning algorithms in conjunction with the feature extraction method were applied to classify GPCRs at all family levels. Surprisingly, these classifiers achieve considerable performance in almost all evaluation criteria which indicated the validity and superiority of the proposed molecular evolution based feature extraction method.