Abstract
Identifying new indications for existing drugs is a crucial role in drug research and development. Computational-based methods are normally regarded as an effective way to infer drugs with new indications. They, though effective, normally fall short of capturing semantic higher-order connectivity patterns presented in heterogeneous biological information networks (HBINs) when learning the respective embeddings of drugs and diseases. To overcome this problem, we propose a novel Multi-level Subgraph Representation Learning model, namely MSRLDDA, for drug-disease association (DDA) prediction. In particular, MSRLDDA first defines different meta-paths to construct semantic subgraphs such that the mechanisms of how drugs act on diseases can be revealed. For each subgraph, a particular graph neural network model is adopted to conduct the representation learning process from different perspectives. By doing so, more expressive representations of drugs and diseases are obtained at multi-level. Experimental results on two benchmark datasets demonstrate that MSRLDDA performs better than several state-of-the-art drug repositioning models. This is a strong indicator that the consideration of higher-order connectivity patterns gains new insight into DDA prediction with improved accuracy.
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Acknowledgments
This work was supported in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region under grant 2021D01D05, in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences, the Xinjiang Tianchi Talents Program under grant E33B9401, in part by CAS Light of the West Multidisciplinary Team project under grant xbzg-zdsys-202114.
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Zhao, BW. et al. (2023). Multi-level Subgraph Representation Learning for Drug-Disease Association Prediction Over Heterogeneous Biological Information Network. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_14
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DOI: https://doi.org/10.1007/978-981-99-4749-2_14
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