Abstract
Drug-Target Interaction (DTI) prediction usually devotes to accurately identify the potential binding targets on proteins so as to guide the drug development. However, the sparse imbalance of known drug-target pairs remains a challenge for high-quality representation learning of drugs and targets, interfering with accurate prediction. The labeled drug-target pairs are far less than the missed since the obtained DTIs are recorded with pathogenic proteins and sophisticated bio-experiments. Therefore, we propose a deep learning paradigm via Heterogeneous graph data Augmentation and node Similarity (HAS) to solve the sparse imbalanced problem on drug-target interaction prediction. Heterogeneous graph data augmentation is devised to generate multi-view augmented graphs through a heterogeneous neighbors sampling strategy. Then the consistency across different graph structures is captured using graph contrastive optimization. Node similarity is calculated on the heterogeneous entity association matrices, aiming to integrate similarity information and heterogeneous attribute gain for drug-target interaction prediction. Extensive experiments show that HAS offers superior performance in sparse imbalanced scenarios compared state-of-the-art methods. Ablation studies prove the effectiveness of heterogeneous graph data augmentation and node similarity.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (61503273, 61702356), Industry-University Cooperation Education Program of the Ministry of Education, and Shanxi Scholarship Council of China.
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Wang, R., Zhang, Z., Zhang, Y., Jiang, Z., Sun, S., Zhang, C. (2022). Sparse Imbalanced Drug-Target Interaction Prediction via Heterogeneous Data Augmentation and Node Similarity. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13280. Springer, Cham. https://doi.org/10.1007/978-3-031-05933-9_43
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DOI: https://doi.org/10.1007/978-3-031-05933-9_43
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