ECMLF: An Explainable Cross-Modal Learning Framework for Cold-Start Protein-Protein Interaction Prediction

Cold-start protein-protein interaction (PPI) prediction aims to identify potential interactions involving newly discovered proteins that lack sufficient experimentally observed interaction records, thereby facilitating the functional characterization of these proteins. Its central challenge is to capture the similarity between new and known proteins using information from diverse modalities. However, existing methods have three limitations in capturing such similarity: (1) only partial modalities are considered, resulting in incomplete or biased similarity modeling; (2) the similarity between new and known proteins is computed offline and independently across modalities, causing inconsistency between similarity modeling and cold-start PPI prediction; (3) current explanations are largely single-modal and feature-oriented, overlooking biologically grounded evidence for interaction mechanisms. To address these issues, we propose an explainable cross-modal learning framework (ECMLF) with three components. First, comprehensive modalities of intrinsic amino acid sequences and four external biomedical knowledge graphs are leveraged to capture similarity between new and known proteins. Second, an adaptive gated fusion module is designed to combine similarity modeling and cold-start PPI prediction within an end-to-end learning framework. Finally, a dual-pathway explanation mechanism is proposed to provide biologically grounded explanations of interaction mechanisms. Extensive experiments on two real datasets show that ECMLF achieves superior prediction accuracy and explanation quality compared with existing methods for cold-start PPI prediction. The source code and datasets are submitted as Supplementary Materials for Reproducibility.