Predicting miRNA-disease association based on Hybrid Graph Autoencoder

Since microRNA (miRNA) can participate in the post-transcriptional regulation of gene expression, they can become potential markers of diseases, therapeutic targets and regulatory factors of disease development. So it is vital to predict miRNA-disease association(MDA). However, recent researches lack feature learning based on diverse views, and pay little attention to the sequence features of miRNA. In this study, we extract the initial feature from miRNA sequences and build homogeneous graph and heterogeneous graph for miRNA-disease association. To effectively learn low-dimensional representations of miRNA and disease features from different perspectives, we propose a Hybrid Graph Autoencoder model (HGAMDA). The model learn the embedding of nodes from multiple homogeneous graphs separately, utilizing graph-level attention mechanisms to learn the importance of different homogeneous graphs. Then, it further learns low-dimensional features of nodes in the heterogeneous graph. The experimental results show that the AUC value of HGAMDA in 5-fold cross-validation is 0.9878 on HMDD v4.0 dataset, significantly outperforming other baseline methods.