BioNet: a large-scale and heterogeneous biological network model for interaction prediction with graph convolution.

Journal: Briefings in bioinformatics
Published Date:

Abstract

MOTIVATION: Understanding chemical-gene interactions (CGIs) is crucial for screening drugs. Wet experiments are usually costly and laborious, which limits relevant studies to a small scale. On the contrary, computational studies enable efficient in-silico exploration. For the CGI prediction problem, a common method is to perform systematic analyses on a heterogeneous network involving various biomedical entities. Recently, graph neural networks become popular in the field of relation prediction. However, the inherent heterogeneous complexity of biological interaction networks and the massive amount of data pose enormous challenges. This paper aims to develop a data-driven model that is capable of learning latent information from the interaction network and making correct predictions.

Authors

  • Xi Yang
    Department of Health Outcomes and Biomedical Informatics.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Jing-Lun Ma
    College of Computer, National University of Defense Technology, China.
  • Yan-Long Qiu
    College of Computer, National University of Defense Technology, China.
  • Kai Lu
    College of Computer, National University of Defense Technology, China.
  • Dong-Sheng Cao
    Xiangya School of Pharmaceutical Sciences , Central South University , Changsha 410013 , Hunan , P. R. China.
  • Cheng-Kun Wu
    State Key Laboratory of High-Performance Computing, College of Computer, National University of Defense Technology, China.