Adaptive Masked Reconstruction for Multi-Scale circRNA-miRNA Representation Learning

Predicting circRNA-miRNA interactions requires learning from incomplete association networks and RNA sequence-derived evidence. We present SMASH, a framework thatcombines multi-scale hypergraph learning with adaptive masked reconstruction. SMASH represents each RNA using its training association profile and fused sequence, structural, and composition similarities. These features are propagated through interaction-derived and similarity-derived structures. The model retains representations from successive propagation depths and learns RNA-specific weights to combine them, allowing each RNA to draw on different neighborhood ranges. To provide auxiliary supervision, a reconstruction branch recovers masked RNA representations. Its masking probabilities are adjusted using historical reconstruction errors, assigning higher probabilities to RNAs with larger errors while maintaining the average masking probability. The encoder is trained jointly on interaction prediction and masked reconstruction. The resulting RNA representations are then used by a CatBoost classifier to score candidate interactions. Under association-level five-fold cross-validation, SMASH achieves mean AUC/AUPR values of 0.9470/0.9487, 0.9604/0.9596, and 0.9572/0.9566 on CMI-9905, CMI-9589, and CMI-20208, respectively.