BioERP: biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Predicting entity relationship can greatly benefit important biomedical problems. Recently, a large amount of biomedical heterogeneous networks (BioHNs) are generated and offer opportunities for developing network-based learning approaches to predict relationships among entities. However, current researches slightly explored BioHNs-based self-supervised representation learning methods, and are hard to simultaneously capturing local- and global-level association information among entities.

Authors

  • Xiaoqi Wang
    Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, 02111, USA.
  • Yaning Yang
    Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui 230026, China.
  • Kenli Li
    College of Computer Science and Electronic Engineering & National Supercomputer Centre in Changsha, Hunan University, Changsha, China.
  • Wentao Li
    State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, Hubei, People's Republic of China.
  • Fei Li
    Institute for Precision Medicine, Tsinghua University, Beijing, China.
  • Shaoliang Peng
    School of Computer Science, National University of Defense Technology, Changsha, China.