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Predicting Potential Drug–Disease Associations Based on Hypergraph Learning with Subgraph Matching

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Abstract

The search for potential drug–disease associations (DDA) can speed up drug development cycles, reduce costly wasted resources, and accelerate disease treatment by repurposing existing drugs that can control further disease progression. As technologies such as deep learning continue to mature, many researchers tend to use emerging technologies to predict potential DDA. The performance of DDA prediction is still challenging and there is some space for improvement due to issues such as the small number of existing associations and possible noise in the data. To better predict DDA, we propose a computational approach based on hypergraph learning with subgraph matching (HGDDA). In particular, HGDDA first extracts feature subgraph information in the validated drug–disease association network and proposes a negative sampling strategy based on similarity network to reduce the data imbalance. Second, the hypergraph Unet module is used by extracting Finally, the potential DDA is predicted by designing a hypergraph combination module to convolution and pooling the two constructed hypergraphs separately, and calculating the difference information between the subgraphs using cosine similarity for node matching. The performance of HGDDA is verified under two standard datasets by 10-fold cross-validation (10-CV), and the results outperform existing drug–disease prediction methods. In addition, to validate the overall utility of the model, the top 10 drugs for the specific disease are predicted through the case study and validated using the CTD database.

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Funding

This research is supported by Scientific Research Fund Project of the Education Department of Liaoning Province (No. LJKZ0028).

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Authors and Affiliations

  1. Key Laboratory of Big Data Applied Technology State Ethnic Affairs Commission, Dalian Minzu University, Dalian, 116650, China

    Yuanxu Wang, Jinmiao Song, Mingjie Wei & Xiaodong Duan

  2. School of Computer Science and Engineering, Dalian Minzu University, Dalian, 116650, China

    Yuanxu Wang, Mingjie Wei & Xiaodong Duan

  3. School of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China

    Jinmiao Song

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  1. Yuanxu Wang
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Correspondence to Jinmiao Song.

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Wang, Y., Song, J., Wei, M. et al. Predicting Potential Drug–Disease Associations Based on Hypergraph Learning with Subgraph Matching. Interdiscip Sci Comput Life Sci 15, 249–261 (2023). https://doi.org/10.1007/s12539-023-00556-0

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