An Efficient Ensemble Learning Approach for Predicting Protein-Protein Interactions by Integrating Protein Primary Sequence and Evolutionary Information.

Abstract

Protein-protein interactions (PPIs) perform a very important function in many cellular processes, including signal transduction, post-translational modifications, apoptosis, and cell growth. Deregulation of PPIs results in many diseases, including cancer and pernicious anemia. Although many high-throughput methods have been applied to generate a large amount of PPIs data, they are generally expensive, inefficient and labor-intensive. Hence, there is an urgent need for developing a computational method to accurately and rapidly detect PPIs. In this article, we proposed a highly efficient approach to predict PPIs by integrating a new protein sequence substitution matrix feature representation and ensemble weighted sparse representation model classifier. The proposed method is demonstrated on Saccharomyces cerevisiae dataset and achieved 99.26% prediction accuracy with 98.53% sensitivity at precision of 100%, which is shown to have much higher predictive accuracy than current state-of-the-art algorithms. Extensive experiments are performed with the benchmark data set from Human and Helicobacter pylori that the proposed method achieves outstanding better success rates than other existing approaches in this problem. Experiment results illustrate that our proposed method presents an economical approach for computational building of PPI networks, which can be a helpful supplementary method for future proteomics researches.