Predicting miRNA-Disease Associations From miRNA-Gene-Disease Heterogeneous Network With Multi-Relational Graph Convolutional Network Model.

Abstract

MiRNAs are reported to be linked to the pathogenesis of human complex diseases. Disease-related miRNAs may serve as novel bio-marks and drug targets. This work focuses on designing a multi-relational Graph Convolutional Network model to predict miRNA-disease associations (HGCNMDA) from a Heterogeneous network. HGCNMDA introduces a gene layer to construct a miRNA-gene-disease heterogeneous network. We refine the features of nodes into initial and inductive features so that the direct and indirect associations between diseases and miRNA can be considered simultaneously. Then HGCNMDA learns feature embeddings for miRNAs and disease through a multi-relational graph convolutional network model that can assign appropriate weights to different types of edges in the heterogeneous network. Finally, the miRNA-disease associations were decoded by the inner product between miRNA and disease feature embeddings. We apply our model to predict human miRNA-disease associations. The HGCNMDA is superior to the other state-of-the-art models in identifying missing miRNA-disease associations and also performs well on recommending related miRNAs/diseases to new diseases/ miRNAs. The codes are available at https://github.com/weiba/HGCNMDA.