融合序列与网络特征，用对比学习提升蛋白质互作预测准确率
SCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions
- 结合序列与网络拓扑特征，设计监督对比学习框架
- 在8个数据集上达98.13%准确率，跨物种AUC超99%
- 适合生物医学研究者用于疾病靶点发现
蛋白质-蛋白质互作（PPI）预测对解析细胞功能和疾病机制至关重要。针对传统实验方法及现有计算模型在跨模态特征融合与假阴性抑制方面的局限，本文提出SCMPPI——一种新型监督对比多模态框架。通过有效整合基于序列的特征（AAC、DPC、ESMC-CKSAAP）与网络拓扑特征（Node2Vec嵌入），并引入带负样本过滤的增强对比学习策略，SCMPPI显著提升预测性能。在8个基准数据集上的大量实验表明，其达到98.13%的准确率和99.69%的AUC，且具备出色的跨物种泛化能力（AUC>99%）。在CD9网络、Wnt通路分析及癌症特异性网络中的成功应用，进一步凸显其在疾病靶点发现中的潜力，确立了SCMPPI作为多模态生物数据分析的强大工具地位。
原文摘要 · Abstract (English)
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.
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