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A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information

Author

Listed:
  • Yunan Luo

    (Tsinghua University
    University of Illinois at Urbana-Champaign)

  • Xinbin Zhao

    (Tsinghua University)

  • Jingtian Zhou

    (Tsinghua University)

  • Jinglin Yang

    (Tsinghua University)

  • Yanqing Zhang

    (Tsinghua University)

  • Wenhua Kuang

    (Tsinghua University)

  • Jian Peng

    (University of Illinois at Urbana-Champaign)

  • Ligong Chen

    (Tsinghua University
    Sichuan University)

  • Jianyang Zeng

    (Tsinghua University)

Abstract

The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug–target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug–target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug–target interactions and repurpose existing drugs.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-00680-8
    DOI: 10.1038/s41467-017-00680-8
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    Cited by:

    1. Yuxuan Wang & Ying Xia & Junchi Yan & Ye Yuan & Hong-Bin Shen & Xiaoyong Pan, 2023. "ZeroBind: a protein-specific zero-shot predictor with subgraph matching for drug-target interactions," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Jiahua Rao & Jiancong Xie & Qianmu Yuan & Deqin Liu & Zhen Wang & Yutong Lu & Shuangjia Zheng & Yuedong Yang, 2024. "A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    3. Qing Ye & Chang-Yu Hsieh & Ziyi Yang & Yu Kang & Jiming Chen & Dongsheng Cao & Shibo He & Tingjun Hou, 2021. "A unified drug–target interaction prediction framework based on knowledge graph and recommendation system," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    4. Xiaomin Liang & Daifeng Li & Min Song & Andrew Madden & Ying Ding & Yi Bu, 2019. "Predicting biomedical relationships using the knowledge and graph embedding cascade model," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-23, June.
    5. Wen Zhang & Xiang Yue & Guifeng Tang & Wenjian Wu & Feng Huang & Xining Zhang, 2018. "SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions," PLOS Computational Biology, Public Library of Science, vol. 14(12), pages 1-21, December.
    6. 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.

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