SCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866913809833132032 |
|---|---|
| author | XU, Shengrui Lu, Tianchi Wang, Zikun Zhai, Jixiu |
| author_facet | XU, Shengrui Lu, Tianchi Wang, Zikun Zhai, Jixiu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02698 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions XU, Shengrui Lu, Tianchi Wang, Zikun Zhai, Jixiu Machine Learning Artificial Intelligence Quantitative Methods 92C40, 68T07 I.2.6; J.3 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. |
| title | SCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions |
| topic | Machine Learning Artificial Intelligence Quantitative Methods 92C40, 68T07 I.2.6; J.3 |
| url | https://arxiv.org/abs/2504.02698 |