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WGMFDDA: A Novel Weighted-Based Graph Regularized Matrix Factorization for Predicting Drug-Disease Associations

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Intelligent Computing Methodologies (ICIC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12465))

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Abstract

Identification of drug-disease associations play an important role for expediting drug development. In comparison with biological experiments for drug repositioning, computational methods may reduce costs and shorten the development cycle. Thus, a number of computational approaches have been proposed for drug repositioning recently. In this study, we develop a novel computational model WGMFDDA to infer potential drug-disease association using weighted graph regularized matrix factorization (WGMF). Firstly, the disease similarity and drug similarity are calculated on the basis of the medical description information of diseases and chemical structures of drugs, respectively. Then, weighted \( K \)-nearest neighbor is implemented to reformulate the drug-disease association adjacency matrix. Finally, the framework of graph regularized matrix factorization is utilized to reveal unknown associations of drug with disease. To evaluate prediction performance of the proposed WGMFDDA method, ten-fold cross-validation is performed on Fdataset. WGMFDDA achieves a high AUC value of 0.939. Experiment results show that the proposed method can be used as an efficient tool in the field of drug-disease association prediction, and can provide valuable information for relevant biomedical research.

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Acknowledgement

This work was supported in part by the NSFC Excellent Young Scholars Program, under Grants 61722212, in part by the Science and Technology Project of Jiangxi Provincial Department of Education, under Grants GJJ180830, GJJ190834.

Competing Interests

The authors declare that they have no competing interests.

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Yichun University, Yichun, Jiangxi 336000, China

    Mei-Neng Wang & Xue-Jun Xie

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

    Zhu-Hong You, Li-Ping Li & Zhan-Heng Chen

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

    Zhu-Hong You & Zhan-Heng Chen

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  1. Mei-Neng Wang
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  2. Zhu-Hong You
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Correspondence to Zhu-Hong You .

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Editors and Affiliations

  1. Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

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Wang, MN., You, ZH., Li, LP., Chen, ZH., Xie, XJ. (2020). WGMFDDA: A Novel Weighted-Based Graph Regularized Matrix Factorization for Predicting Drug-Disease Associations. In: Huang, DS., Premaratne, P. (eds) Intelligent Computing Methodologies. ICIC 2020. Lecture Notes in Computer Science(), vol 12465. Springer, Cham. https://doi.org/10.1007/978-3-030-60796-8_47

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  • DOI: https://doi.org/10.1007/978-3-030-60796-8_47

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