Task-Conditioned Dual-Graph Residual with Marginal Constrained Refinement for circRNA-miRNA Interaction

Accurate circRNA-miRNA interaction prediction requires integrating heterogeneous biological similarities with unevenly distributed interaction evidence. Different RNAs may rely on different similarity views, while recorded interaction networks vary substantially in endpoint connectivity. TAPER learns RNA-specific preferences over sequence, structural, and composition similarities from interaction-recovery utility estimated within each training fold. These preferences are used to construct task-conditioned biological graphs. A degree-based bipartite reference is then fitted to the training interaction network, and the standardized interaction residual is separated into graph-smooth and complementary components. The graph-smooth component is combined with a graph-regularized factor model to obtain base interaction probabilities, while the complementary component provides neighborhood evidence for candidate-specific refinement. A Bernoulli projection preserves the row and column marginals of the base predictor during this refinement. In five-fold transductive evaluation, TAPER achieves AUCs of 0.9721, 0.9827, and 0.9691 on CMI-9905, CMI-9589, and CMI-20208, respectively. Ablations on CMI-9905 show lower AUC and AUPR after each component removal. TAPER outperforms the tested mechanism alternatives, while literature evidence and expression profiles contextualize its top-ranked predictions.