Predicting lncRNA–Protein Interactions by Heterogenous Network Embedding - Zhao, Guoqing; Li, Pengpai; Qiao, Xu; Han, Xianhua; Liu, Zhi-Ping (Frontiers in Genetics, 2022)
lncRNA-protein interactions play essential roles in a variety of cellular processes. However, the experimental methods 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 a lncRNA-protein heterogeneous network. The experimental results indicate that LncPNet achieves promising performance on benchmark datasets extracted from NPInter database with accuracy of 0.930 and area under ROC curve (AUC) of 0.971 respectively. In addition, we further compare our method with other eight state-of-the-art methods, the results illustrate that our method achieves superior prediction performance. LncPNet provides an effective method via a new perspective of representing lncRNA-protein heterogeneous network, which will greatly reduce the cost of experiments.