Multi-View Hypergraph Contrastive Learning for Uncertainty-Aware circRNA–Disease Association Prediction

Circular RNAs (circRNAs) have a covalently closed structure and high stability in complex biological samples, making them promising disease biomarkers. However, the sparsity and imbalance of known circRNA–disease associations (CDAs) necessitate computational approaches for CDA prediction. Competition among multiple circRNAs for shared miRNA pools gives rise to group-level ceRNA regulation, motivating the modeling of higher-order relationships among nodes. Three challenges remain in existing methods: insufficient modeling of higher-order cooperative relationships in similarity networks, incomplete integration of complementary predictive signals, and a lack of sample-level uncertainty awareness in fusion decisions. To address these challenges, we propose MHUCDA, a CDA prediction framework that integrates multi-view hypergraph contrastive learning with uncertainty-aware dual-branch fusion. Hypergraph convolution is used instead of conventional pairwise graph convolution to capture higher-order cooperative regulatory relationships in similarity networks. Cross-view contrastive learning enforces semantic consistency across multiple similarity views. A dual-branch architecture independently models candidate associations from two complementary perspectives, semantic propagation and local structural features, while a gating network dynamically assigns per-sample branch weights. Dual-branch prediction divergence is used as an empirical indicator of relative prediction risk, and predictions for high-divergence samples are adjusted toward a neutral reference value. On the CircR2Disease benchmark under five-fold cross-validation, MHUCDA achieves an AUC of 0.9687 and an AUPR of 0.9623, with consistent performance in separate within-dataset evaluations on three additional datasets. Uncertainty stratification analysis reveals an AUC gap of 0.1263 between the highest- and lowest-confidence groups, supporting the use of branch divergence for empirical risk stratification. Case analysis links selected high-confidence predictions to experimentally verified ceRNA regulatory axes, supporting further investigation of model scores for candidate prioritization.