Hierarchical graph attention network for miRNA-disease association prediction

Abstract

Many biological studies show that the mutation and abnormal expression of microRNAs (miRNAs) could cause a variety of diseases. As an important biomarker for disease diagnosis, miRNA is helpful to understand pathogenesis, and could promote the identification, diagnosis and treatment of diseases. However, the pathogenic mechanism how miRNAs affect these diseases has not been fully understood. Therefore, predicting the potential miRNA-disease associations is of great importance for the development of clinical medicine and drug research. In this study, we proposed a novel deep learning model based on hierarchical graph attention network for predicting miRNA-disease associations (HGANMDA). Firstly, we constructed a miRNA-disease-lncRNA heterogeneous graph based on known miRNA-disease associations, miRNA-lncRNA associations and disease-lncRNA associations. Secondly, the node-layer attention was applied to learn the importance of neighbor nodes based on different meta-paths. Thirdly, the semantic-layer attention was applied to learn the importance of different meta-paths. Finally, a bilinear decoder was employed to reconstruct the connections between miRNAs and diseases. The extensive experimental results indicated that our model achieved good performance and satisfactory results in predicting miRNA-disease associations.

Keywords: disease; hierarchical graph attention network; lncRNA; meta-path; miRNA.

Graphical abstract

Figure 1

Flowchart of HGANMDA model for predicting miRNA-disease associations

Figure 2

ROC curves performed by HGANMDA model based on HMDD v.2.0

Figure 3

P-R curves performed by HGANMDA model based on HMDD v.2.0

Figure 4

The average Acc., Prec., Recall, F1 score, and AUC values of HGANMDA under different feature aggregation methods according to 5-fold cross-validation

Figure 5

Dimension of the semantic-layer attention vector q