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

Meng Meng Wei, Chang Qing Yu, Li Ping Li, Zhu Hong You, Zhong Hao Ren, Yong Jian Guan, Xin Fei Wang, Yue Chao Li

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish
Article number1122909
JournalFrontiers in Genetics
Volume14
DOIs
StatePublished - 10 Feb 2023

Keywords

  • behavioral features
  • heterogeneous information network
  • HIN2Vec
  • lncRNA-protein interaction
  • network embedding

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