Protein-protein interaction (PPI) not only plays a critical role in cell life activities, but also plays an important role in discovering the mechanism of biological activity, protein function, and disease states. Developing computational methods is of great significance for PPIs prediction since experimental methods are time-consuming and laborious. In this paper, we proposed a PPI prediction algorithm called GRNN-PPI only using the amino acid sequence information based on general regression neural network and two feature extraction methods. Specifically, we designed a new feature extraction method named Mutation Spectral Radius (MSR) to extract evolutionary information by the BLOSUM62 matrix. Meanwhile, we integrated another feature extraction method, autocorrelation description, which can completely extract information on physicochemical properties and protein sequences. The principal component analysis was applied to eliminate noise, and the general regression neural network was adopted as a classifier. The prediction accuracy of the yeast, human, and Helicobacter pylori1 (H. pylori1) data sets were 97.47%, 99.63%, and 99.97%, respectively. In addition, we also conducted experiments on two important PPI networks and six independent data sets. All results were significantly higher than some state-of-the-art methods used for comparison, showing that our method is feasible and robust.
Keywords: BLOSUM62 matrix; general regression neural network; protein−protein interaction; spectral radius.