Metapath-Based Deep Convolutional Neural Network for Predicting miRNA-Target Association on Heterogeneous Network

Predicting the interactions between microRNAs (miRNAs) and target genes is of great significance for understanding the regulatory mechanism of miRNA and treating complex diseases. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for revealing miRNA-associated target genes. However, there are still some limitations about automatically learn the feature information of the network in the existing methods. Since network representation learning can self-adaptively capture structure information of the network, we propose a framework based on heterogeneous network representation, MDCNN (Metapath-Based Deep Convolutional Neural Network), to predict the associations between miRNAs and target genes. MDCNN samples the paths between the node pairs in the form of meta-path based on the heterogeneous information network (HIN) about miRNAs and target genes. Then the node feature and the path feature which is learned by the Deep Convolutional Neural Network (DCNN) are spliced together as the representation of the miRNA-target gene, to predict the miRNA-target gene interactions. The experiment results indicate that the performance of MDCNN outperforms other methods in multiple validation metrics by fivefold cross validation. We set an ablation study to identify the necessity of miRNA similarity and target gene similarity for improving the prediction ability of MDCNN. The case studies on hsa-miR-26b-5p and CDKN1A further demonstrates that MDCNN can successfully predict potential miRNA-target gene interactions.