SCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions
This provides a powerful tool for disease target discovery in biology, though it appears incremental as it builds on existing multimodal and contrastive learning approaches.
The paper tackled the problem of predicting protein-protein interactions by addressing limitations in cross-modal feature fusion and false-negative suppression, achieving state-of-the-art accuracy of 98.13% and AUC of 99.69% on benchmark datasets.
Protein-protein interaction (PPI) prediction plays a pivotal role in deciphering cellular functions and disease mechanisms. To address the limitations of traditional experimental methods and existing computational approaches in cross-modal feature fusion and false-negative suppression, we propose SCMPPI-a novel supervised contrastive multimodal framework. By effectively integrating sequence-based features (AAC, DPC, ESMC-CKSAAP) with network topology (Node2Vec embeddings) and incorporating an enhanced contrastive learning strategy with negative sample filtering, SCMPPI achieves superior prediction performance. Extensive experiments on eight benchmark datasets demonstrate its state-of-the-art accuracy(98.13%) and AUC(99.69%), along with excellent cross-species generalization (AUC>99%). Successful applications in CD9 networks, Wnt pathway analysis, and cancer-specific networks further highlight its potential for disease target discovery, establishing SCMPPI as a powerful tool for multimodal biological data analysis.