Protein-Protein Interactions (PPIs) play a vital role in most cellular processes. Although many efforts have been devoted to detecting protein interactions by high-throughput experiments, these methods are obviously expensive and tedious. Targeting these inevitable disadvantages, this study develops a novel computational method to predict PPIs using information on protein sequences, which is highly efficient and accurate. The improvement mainly comes from the use of the Rotation Forest (RF) classifier and the Local Phase Quantization (LPQ) descriptor from the Physicochemical Property Response (PR) Matrix of protein amino acids. When performed on three PPI datasets including Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori, we obtained good results of average accuracies of 93.8%, 97.96%, and 89.47%, which are much better than in previous studies. Extensive validations have also been explored to evaluate the performance of the Rotation Forest ensemble classifier with the state-of-the-art Support Vector Machine classifier. These promising results indicate that the proposed method might play a complementary role for future proteomics research.
Keywords: Local Phase Quantization; Physicochemical Property Response Matrix (PR); Rotation Forest; protein-protein interaction.