A Multi-View Heterogeneous Dual-Stream Graph Autoencoder for Predicting circRNA–Disease Associations

Circular RNAs (circRNAs) participate in gene regulation and disease-associated processes. Computational prediction of circRNA–disease associations (CDAs) can help prioritize candidates for biological investigation. Here, we propose a multi-view heterogeneous dual-stream graph autoencoder (MHDA) for CDA prediction. MHDA combines multi-view graph attention encoding, heterogeneous relation enhancement, dual-stream association reconstruction, and contrastive regularization. The encoder learns representations from complementary similarity views and adaptively fuses them. A heterogeneous attention module then integrates within-type and cross-type relationships. The decoder combines element-wise interactions with concatenated pair representations through gated fusion to score candidate associations. On the CircR2Disease dataset, MHDA achieves an AUC of 0.9633 and an AUPR of 0.9599 under five-fold cross-validation. Ablation analyses support the contributions of the main model components. Of the top 25 ranked candidate associations, 22 have supporting evidence in the published literature. These results suggest that MHDA is useful for prioritizing potential CDAs and provides promising candidates for subsequent experimental validation.