Lncrna-Mirna Interaction Prediction From The Heterogeneous Network Through Graph Embedding Ensemble Learning

LncRNA-miRNA interactions play crucial roles in gene regulatory networks and can reveal functions of lncRNAs and miRNAs. Although several methods have been proposed to infer interactions on the lncRNA-miRNA interaction network, few attentions have been paid to fully exploiting the structure of lncRNA-miRNA interaction network. In this paper, we propose a Graph Embedding Ensemble Learning method (abbreviated as "GEEL") to predict lncRNA-miRNA interactions. First, we collect lncRNA sequences and miRNA sequences to calculate lncRNA-lncRNA sequence similarity and miRNA-miRNA sequence similarity, and then we combine them with the known lncRNA-miRNA interactions to construct a heterogeneous network, which takes lncRNAs and miRNAs as nodes. We adopt graph embedding methods to learn representations of lncRNAs and miRNAs from the heterogeneous network, and then merge the representations of lncRNAs and miRNAs to represent the lncRNA-miRNA pairs. Random forest classifiers are built based on the merged representations to predict lncRNA-miRNA interactions. We consider five different graph embedding methods, and evaluate the corresponding models. Further, we use individual graph embedding method-based model as base predictors and build a high-level ensemble model. The experimental results show that GEEL achieves AUPR score of 0.7004 and AUC score of 0.9537, and outperforms base predictors and other state-of-the-art methods. In conclusion, GEEL is an effective tool for lncRNA-miRNA interaction prediction.