Authors
- Bin Deng Northwest Normal University
- Huifang Ma Northwest Normal University
- Ruijia Zhang Northwest Normal University
- Meihuizi Jia Northwest Normal University
- Rui Bing Northwest Normal University
DOI:
https://doi.org/10.1609/aaai.v40i1.36980Abstract
Protein-Protein Interactions (PPIs) prediction is crucial for understanding cellular functions and disease mechanisms. Existing deep learning–based methods primarily rely on direct interaction within the PPI network to update protein representations. However, (1) such networks overlook the potential associations between functionally similar proteins, limiting the smoothing capability of Graph Neural Networks (GNNs) in learning representations for similar nodes. (2) Additionally, most approaches fail to adequately model the latent dependencies among interaction types (edge labels), which hinders their performance in PPI prediction tasks. To address these limitations, we propose TELC-PPI, a topology-enhanced and label correlation-aware model for protein-protein interactions prediction. Specifically, TELC-PPI first identifies similar proteins by leveraging both the topological information of the PPI network and the label distributions of nodes, constructing similarity edges. Then, it incorporates label co-occurrence statistics into the learning of label embeddings. Experimental results on multiple datasets and under various data split settings demonstrate that TELC-PPI significantly outperforms existing methods, validating the effectiveness of our model design.
How to Cite
Deng, B., Ma, H., Zhang, R., Jia, M., & Bing, R. (2026). Topology-Enhanced and Label Correlation-Aware Model for Protein-Protein Interaction Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 202–210. https://doi.org/10.1609/aaai.v40i1.36980
Issue
Section
AAAI Technical Track on Application Domains I