Abstract
Computational drug repositioning is a promising strategy in discovering new indicators for approved or experimental drugs. However, most of computational-based methods fall short of taking into account the non-Euclidean nature of biomedical network data. To address this challenge, we propose a graph representation learning model, called DDAGTP, for drug repositioning using graph transition probability matrix in heterogenous information networks (HINs), In particular, DDAGTP first integrates three different types of drug-disease, drug-protein and protein-disease association networks and their biological knowledge to construct a heterogeneous information network (HIN). Then, a graph convolution autoencoder model is adopted by combining graph transfer probabilities to learn the feature representation of drugs and diseases. Finally, DDAGTP incorporates a CatBoost classifier to complete the task of drug-disease association prediction. Experimental results demonstrate that DDAGTP achieves the excellent performance on all benchmark datasets when compared with state-of-the-art prediction models in terms of several evaluation metrics.
D.-X. Li and X. Deng---Co-first authors
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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, in part by CAS Light of the West Multidisciplinary Team project under grant xbzg-zdsys-202114, and in part by the Xinjiang Tianchi Talents Program under grant E33B9401.
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Li, DX. et al. (2023). A Novel Graph Representation Learning Model for Drug Repositioning Using Graph Transition Probability Matrix Over Heterogenous Information Networks. 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_16
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