LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model - Wei, Meng-Meng; Yu, Chang-Qing; Li, Li-Ping; You, Zhu-Hong; Ren, Zhong-Hao; Guan, Yong-Jian; Wang, Xin-Fei; Li, Yue-Chao (Frontiers in Genetics, 2023)
LncRNA-protein interaction play an important role in the development and treatment of many human diseases. As the experimental approaches to determine lncRNA–protein interactions are expensive and time-consuming, considering that there are few calculation methods, therefore, it is urgent to develop efficient and accurate methods to extract the interaction between lncRNA and protein. In this work, a heterogeneous network model-based HIN2Vec, namely LPIH2V, is pro-posed. The heterogeneous network is composed of lncRNA similarity networks, protein similarity networks, and known lncRNA-protein interaction networks. The behavioral features are then extracted in a heterogeneous network using the HIN2Vec method of network embedding. The results showed that LPIH2V obtains an AUC of 0.97 and ACC of 0.95 in the five-fold cross-validation test. The model successfully showed superiority and good generalization ability. Compared to other models, LPIH2V not only extracts attribute characteristics by similarity, but also acquires node behavior characteristics by meta-path wandering in heterogeneous networks. LPIH2V would be beneficial in forecasting interactions between lncRNA and protein.