A heterogeneous information network learning model with neighborhood-level structural representation for predicting lncRNA-miRNA interactions

Abstract

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are closely related to the treatment of human diseases. Traditional biological experiments often require time-consuming and labor-intensive in their search for mechanisms of disease. Computational methods are regarded as an effective way to predict unknown lncRNA-miRNA interactions (LMIs). However, most of them complete their tasks by mainly focusing on a single lncRNA-miRNA network without considering the complex mechanism between biomolecular in life activities, which are believed to be useful for improving the accuracy of LMI prediction. To address this, a heterogeneous information network (HIN) learning model with neighborhood-level structural representation, called HINLMI, to precisely identify LMIs. In particular, HINLMI first constructs a HIN by integrating nine interactions of five biomolecules. After that, different representation learning strategies are applied to learn the biological and network representations of lncRNAs and miRNAs in the HIN from different perspectives. Finally, HINLMI incorporates the XGBoost classifier to predict unknown LMIs using final embeddings of lncRNAs and miRNAs. Experimental results show that HINLMI yields a best performance on the real dataset when compared with state-of-the-art computational models. Moreover, several analysis experiments indicate that the simultaneous consideration of biological knowledge and network topology of lncRNAs and miRNAs allows HINLMI to accurately predict LMIs from a more comprehensive perspective. The promising performance of HINLMI also reveals that the utilization of rich heterogeneous information can provide an alternative insight for HINLMI to identify novel interactions between lncRNAs and miRNAs.