GSSCMI: Efficient Co-Attention and Multimodal Contrastive Learning for Enhanced circRNA-miRNA Interaction Prediction

Existing circRNA-miRNA interaction prediction methods have not fully exploited the spatial folding information in circRNAs and pre-miRNAs. Furthermore, current methods inadequately address intramolecular modal consistency and intermolecular discrimination across distinct modalities, leading to suboptimal discriminative performance. Traditional attention mechanisms are computationally intensive and lack fine-grained, balanced modal weight allocation, impeding efficient feature fusion. To address these limitations, we propose GSSCMI, a novel method comprising three key components. First, an information integration module incorporates similarity information, sequence features, and secondary structure features. Second, a multimodal contrastive learning module processes features across three modalities, enhancing both intra-modal consistency for individual circRNA/miRNA and inter-modal discrimination between different circRNAs/miRNAs. Third, we innovatively design an efficient co-attention mechanism that simultaneously modulates fused modalities through unified and fine-grained attention scores, achieving balanced fusion while significantly reducing computational overhead. Experimental results demonstrate GSSCMI outperforms existing methods, with improvements of 9.53% in MCC and 6.40% in F1. Ablation studies further show the efficient co-attention reduces convergence iterations by approximately 58%. Compared to the initial co-attention, it reduces computational cost by 42.94% while improving MCC by 13.32% and ACC by 6.69%. Additionally, we identified regulatory sites through structure-based A-to-I editing and elucidated sequence-level inter-token dependencies via attention visualization.