Predicting lncRNA-Protein Interactions by Heterogenous Network Embedding

Abstract

lncRNA-protein interactions play essential roles in a variety of cellular processes. However, the experimental methods for systematically mapping of lncRNA-protein interactions remain time-consuming and expensive. Therefore, it is urgent to develop reliable computational methods for predicting lncRNA-protein interactions. In this study, we propose a computational method called LncPNet to predict potential lncRNA-protein interactions by embedding an lncRNA-protein heterogenous network. The experimental results indicate that LncPNet achieves promising performance on benchmark datasets extracted from the NPInter database with an accuracy of 0.930 and area under ROC curve (AUC) of 0.971. In addition, we further compare our method with other eight state-of-the-art methods, and the results illustrate that our method achieves superior prediction performance. LncPNet provides an effective method via a new perspective of representing lncRNA-protein heterogenous network, which will greatly benefit the prediction of lncRNA-protein interactions.

Keywords: LncPNet; computational method; heterogenous network; lncRNA–protein interaction; network embedding.