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A Gated Recurrent Unit Model for Drug Repositioning by Combining Comprehensive Similarity Measures and Gaussian Interaction Profile Kernel

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Abstract

Drug repositioning can find new uses for existing drugs and accelerate the processing of new drugs research and developments. It is noteworthy that the number of successful drug repositioning stories is increasing rapidly. Various computational methods have been presented to predict novel drug-disease associations for drug repositioning based on similarity measures among drugs and diseases or heterogeneous networks. However, there are some known associations between drugs and diseases that previous studies not utilized. In this work, we proposed a GRU model to predict potential drug-disease interactions by using comprehensive similarity. 10-fold cross-validation and common evaluation indicators are used to evaluate the performance of our model. Our model outperformed existing methods. The experimental results proved our model is a useful tool for drug repositioning and biochemical medicine research.

The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.

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Acknowledgments

This work is supported by the National Science Foundation of China, under Grants 61572506, in part by the NSFC Excellent Young Scholars Program, under Grants 61722212, in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences.

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Authors and Affiliations

  1. Department of Network Security, Xijing University, Xi’an, 710123, China

    Tao Wang

  2. Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China

    Hai-Cheng Yi, Zhu-Hong You, Li-Ping Li, Yan-Bin Wang & Leon Wong

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Hai-Cheng Yi

  4. Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Lun Hu

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  1. Tao Wang
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  2. Hai-Cheng Yi
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  4. Li-Ping Li
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  5. Yan-Bin Wang
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  6. Lun Hu
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  7. Leon Wong
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Contributions

Hai-Cheng Yi, Zhu-Hong You conceived the algorithm, carried out analyses, prepared the datasets, carried out experiments, and wrote the manuscript; Li-Ping Li, Yan-Bin Wang, Lun Hu and Leon Wong designed, performed and analyzed experiments and wrote the manuscript; All authors read and approved the final manuscript.

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Correspondence to Hai-Cheng Yi .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Nanchang Institute of Technology, Nanchang, China

    Zhi-Kai Huang

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Wang, T. et al. (2019). A Gated Recurrent Unit Model for Drug Repositioning by Combining Comprehensive Similarity Measures and Gaussian Interaction Profile Kernel. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_33

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  • DOI: https://doi.org/10.1007/978-3-030-26969-2_33

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