MRGBMDAT: a multi-relational graph encoder network with bilinear fusion for miRNA-disease association type prediction

MicroRNAs (miRNAs) are key post-transcriptional regulators involved in diverse biological processes, and their dysregulation is closely associated with the onset and progression of many diseases. Accurate prediction of miRNA-disease association types is therefore essential for understanding disease mechanisms and advancing precision medicine. Although computational methods provide efficient alternatives to wet-lab experiments, existing approaches often focus on binary association prediction, inadequately integrate local semantic dependencies and global topological structures, and suffer from class imbalance.

To address these limitations, we propose MRGBMDAT, a multi-relational graph encoder network with bilinear fusion for miRNA-disease association type prediction. Specifically, a multi-relational graph convolution module with bidirectional cross-attention captures global topological structures, while a local subgraph sampling module extracts local semantic dependencies. A bilinear fusion decoder with element-wise attention jointly models their linear and nonlinear interactions. In addition, an iterative feature similarity-based negative sample selection strategy is introduced to alleviate class imbalance. Experimental results on the HMDD v3.2 dataset demonstrate that MRGBMDAT significantly outperforms five state-of-the-art methods across multiple evaluation metrics, exhibiting strong discriminative power and generalization capability.

The source code is publicly available at https://github.com/CDMBlab/MRGBMDAT.