LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model

• Published in: Frontiers in genetics Vol. 14; p. 1122909
• Main Authors: Wei, Meng-Meng, Yu, Chang-Qing, Li, Li-Ping, You, Zhu-Hong, Ren, Zhong-Hao, Guan, Yong-Jian, Wang, Xin-Fei, Li, Yue-Chao
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 10.02.2023
• Subjects: behavioral features; Genetics; heterogeneous information network; HIN2Vec; lncRNA-protein interaction; network embedding; HIN2Vec; behavioral features; heterogeneous information network; network embedding; lncRNA-protein interaction
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2023.1122909

LncRNA-protein interaction plays 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 predict lncRNA-protein interactions. In this work, a model for heterogeneous network embedding based on meta-path, namely LPIH2V, is proposed. The heterogeneous network is composed of lncRNA similarity networks, protein similarity networks, and known lncRNA-protein interaction networks. The behavioral features are 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 5-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 behavior properties by meta-path wandering in heterogeneous networks. LPIH2V would be beneficial in forecasting interactions between lncRNA and protein.