Abstract
Effective drug-drug interaction (DDI) prediction can prevent adverse reactions and side effects caused by taking multiple drugs at the same time. However, most methods that obtain drug information through large-scale biomedical knowledge graphs (KGs), ignore the problem of high noise and complexity, and have certain limitations in obtaining rich neighborhood information for each entity in the KG. Therefore, this paper proposes an end-to-end method called Knowledge Subgraph and High-order Feature-aware Structure (KSHFS) to address DDI prediction. In KSHFS, this paper first designs a subgraph extraction module to reduce the noise caused by the KG, remove irrelevant information, and effectively utilize the entity information in external knowledge graphs to assist DDI prediction. Then, a high-order feature-aware module is designed to aggregate entity information propagated from high-order neighbors, learn high-order structural embeddings for each entity, and effectively capture potential semantic neighborhood features of drug pairs. Finally, in binary DDI prediction, a self-attention mechanism is used for feature fusion to predict drug interaction events. The experimental results demonstrate that the KSHFS model outperforms the baseline models in binary and multi-relation DDI prediction based on various evaluation metrics, including AUC, AUPR, and F1.
Supported by the Natural Science Foundation of Shandong Province (ZR2022MF333).
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This work was supported by the Natural Science Foundation of Shandong Province (ZR2022MF333).
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Wang, N., Gao, Q., Fan, J. (2024). KSHFS: Research on Drug-Drug Interaction Prediction Based on Knowledge Subgraph and High-Order Feature-Aware Structure. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1964. Springer, Singapore. https://doi.org/10.1007/978-981-99-8141-0_37
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