Multi-Relation Graph Embedding for Predicting miRNA-Target Gene Interactions by Integrating Gene Sequence Information.

Abstract

Accumulated studies have found that miRNAs are in charge of many complex diseases such as cancers by modulating gene expression. Predicting miRNA-target interactions is beneficial for uncovering the crucial roles of miRNAs in regulating target genes and the progression of diseases. The emergence of large-scale genomic and biological data as well as the recent development in heterogeneous networks provides new opportunities for miRNA target identification. Compared with conventional methods, computational methods become a decent solution for high efficiency. Thus, designing a method that could excavate valid information from the heterogeneous network and gene sequences is in great demand for improving the prediction accuracy. In this study, we proposed a graph-based model named MRMTI for the prediction of miRNA-target interactions. MRMTI utilized the multi-relation graph convolution module and the Bi-LSTM module to incorporate both network topology and sequential information. The learned embeddings of miRNAs and genes were then used to calculate the prediction scores of miRNA-target pairs. Comparisons with other state-of-the-art graph embedding methods and existing bioinformatic tools illustrated the superiority of MRMTI under multiple criteria metrics. Three variants of MRMTI implied the positive effect of multi-relation. The experimental results of case studies further demonstrated the prominent ability of MRMTI in predicting novel associations.