Multi-scale topology and position feature learning and relationship-aware graph reasoning for prediction of drug-related microbes.

Journal: Bioinformatics (Oxford, England)
Published Date: Feb 1, 2024

Abstract

MOTIVATION: The human microbiome may impact the effectiveness of drugs by modulating their activities and toxicities. Predicting candidate microbes for drugs can facilitate the exploration of the therapeutic effects of drugs. Most recent methods concentrate on constructing of the prediction models based on graph reasoning. They fail to sufficiently exploit the topology and position information, the heterogeneity of multiple types of nodes and connections, and the long-distance correlations among nodes in microbe-drug heterogeneous graph.

Authors

  • Ping Xuan
    School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
  • Jing Gu
    Department of Epidemiology and Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Hui Cui
    Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, PR China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, PR China.
  • Shuai Wang
    Department of Intensive Care Unit, China-Japan Union Hospital of Jilin University, Changchun, China.
  • Nakaguchi Toshiya
    Center for Frontier Medical Engineering, Chiba University, Chiba 2638522, Japan.
  • Cheng Liu
    Key Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Anhui Province Key Laboratory of Polar Environment and Global Change, University of Science and Technology of China, Hefei 230026, China. Electronic address: chliu81@ustc.edu.cn.
  • Tiangang Zhang
    School of Mathematical Science, Heilongjiang University, Harbin 150080, China. zhang@hlju.edu.cn.

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