Multi-view heterogeneous molecular network representation learning for protein-protein interaction prediction

Abstract

Background: Protein-protein interaction (PPI) plays an important role in regulating cells and signals. Despite the ongoing efforts of the bioassay group, continued incomplete data limits our ability to understand the molecular roots of human disease. Therefore, it is urgent to develop a computational method to predict PPIs from the perspective of molecular system.

Methods: In this paper, a highly efficient computational model, MTV-PPI, is proposed for PPI prediction based on a heterogeneous molecular network by learning inter-view protein sequences and intra-view interactions between molecules simultaneously. On the one hand, the inter-view feature is extracted from the protein sequence by k-mer method. On the other hand, we use a popular embedding method LINE to encode the heterogeneous molecular network to obtain the intra-view feature. Thus, the protein representation used in MTV-PPI is constructed by the aggregation of its inter-view feature and intra-view feature. Finally, random forest is integrated to predict potential PPIs.

Results: To prove the effectiveness of MTV-PPI, we conduct extensive experiments on a collected heterogeneous molecular network with the accuracy of 86.55%, sensitivity of 82.49%, precision of 89.79%, AUC of 0.9301 and AUPR of 0.9308. Further comparison experiments are performed with various protein representations and classifiers to indicate the effectiveness of MTV-PPI in predicting PPIs based on a complex network.

Conclusion: The achieved experimental results illustrate that MTV-PPI is a promising tool for PPI prediction, which may provide a new perspective for the future interactions prediction researches based on heterogeneous molecular network.

Keywords: Heterogeneous molecular network; LINE; Network representation learning; Protein sequence; Protein–protein interaction.

Fig. 1

The overview of MTV-PPI

Fig. 2

An illustration of the process of extracting inter-view feature

Fig. 3

An illustration of first-order proximity and second-order proximity in LINE

Fig. 4

ROC and PR curves obtained by MTV-PPI and all baseline algorithms

Fig. 5

Predictive performances with two different aggregators

Fig. 6

Results with different network embedding algorithms

Fig. 7

ROC and PR curves obtained by various features

Fig. 8

ROC and PR curves obtained by various classifiers