Protein-Protein Interaction Prediction by Integrating Sequence Information and Heterogeneous Network Representation

Protein-protein interaction (PPI) plays an important role in regulating cells and signals. PPI deregulation will lead to many diseases, including pernicious anemia or cancer. Despite the ongoing efforts of the bioassay group, continued data incompleteness limits our ability to understand the molecular roots of human disease. Therefore, it is urgent to develop a computational method that accurately and quickly detects PPIs. In this paper, a highly efficient model is proposed for predicting PPIs through heterogeneous network by combining local feature with global feature. Heterogeneous network is collected from several valuable datasets, containing five types of nodes and nine interactions among them. Local feature is extracted from protein sequence by k-mer method. Global feature is extracted from heterogeneous network by LINE (Large-scale Information Network Embedding). Protein representation is obtained from local feature and global feature by concatenation. Finally, random forest is trained to classify and predict potential protein pairs. The proposed method is demonstrated on STRING dataset and achieved an average 86.55% prediction accuracy with 0.9308 AUC. Extensive contrast experiments are performed with different protein representations and different classifiers. Obtained experiment results illustrate that proposed method is economically viable, which provides a new perspective for future research.