IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning.

Journal: BMC bioinformatics
PMID: 31881820

Abstract

BACKGROUND: Viral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions must be discovered. However, current computational methods for predicting virus-receptor interactions are limited.

Authors

  • Cheng Yan
    Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA.
  • Guihua Duan
    School of Computer Science and Engineering, Central South University, Changsha, China.
  • Fang-Xiang Wu
  • Jianxin Wang

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