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Knowledge-Graph-Based Drug Repositioning against COVID-19 by Graph Convolutional Network with Attention Mechanism

Author

Listed:
  • Mingxuan Che

    (Department of Information Engineering, Dalian University, Dalian 116622, China)

  • Kui Yao

    (Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China)

  • Chao Che

    (Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China)

  • Zhangwei Cao

    (Department of Software Engineering, Dalian University, Dalian 116622, China)

  • Fanchen Kong

    (Department of Software Engineering, Dalian University, Dalian 116622, China)

Abstract

The current global crisis caused by COVID-19 almost halted normal life in most parts of the world. Due to the long development cycle for new drugs, drug repositioning becomes an effective method of screening drugs for COVID-19. To find suitable drugs for COVID-19, we add COVID-19-related information into our medical knowledge graph and utilize a knowledge-graph-based drug repositioning method to screen potential therapeutic drugs for COVID-19. Specific steps are as follows. Firstly, the information about COVID-19 is collected from the latest published literature, and gene targets of COVID-19 are added to the knowledge graph. Then, the information of COVID-19 of the knowledge graph is extracted and a drug–disease interaction prediction model based on Graph Convolutional Network with Attention (Att-GCN) is established. Att-GCN is used to extract features from the knowledge graph and the prediction matrix reconstructed through matrix operation. We evaluate the model by predicting drugs for both ordinary diseases and COVID-19. The model can achieve area under curve (AUC) of 0.954 and area under the precise recall area curve (AUPR) of 0.851 for ordinary diseases. On the drug repositioning experiment for COVID-19, five drugs predicted by the models have proved effective in clinical treatment. The experimental results confirm that the model can predict drug–disease interaction effectively for both normal diseases and COVID-19.

Suggested Citation

  • Mingxuan Che & Kui Yao & Chao Che & Zhangwei Cao & Fanchen Kong, 2021. "Knowledge-Graph-Based Drug Repositioning against COVID-19 by Graph Convolutional Network with Attention Mechanism," Future Internet, MDPI, vol. 13(1), pages 1-10, January.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:1:p:13-:d:476018
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    References listed on IDEAS

    as
    1. Yong Liu & Min Wu & Chunyan Miao & Peilin Zhao & Xiao-Li Li, 2016. "Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-26, February.
    2. Yunan Luo & Xinbin Zhao & Jingtian Zhou & Jinglin Yang & Yanqing Zhang & Wenhua Kuang & Jian Peng & Ligong Chen & Jianyang Zeng, 2017. "A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information," Nature Communications, Nature, vol. 8(1), pages 1-13, December.
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    Cited by:

    1. Francesco Piccialli & Vincenzo Schiano Cola & Fabio Giampaolo & Salvatore Cuomo, 2021. "The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic," Information Systems Frontiers, Springer, vol. 23(6), pages 1467-1497, December.
    2. Jie Yu & Yaliu Li & Chenle Pan & Junwei Wang, 2021. "A Classification Method for Academic Resources Based on a Graph Attention Network," Future Internet, MDPI, vol. 13(3), pages 1-16, March.

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