Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces.

Journal: BMC bioinformatics
Published Date: Dec 27, 2019

Abstract

BACKGROUND: Detection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery. Existing machine-learning methods rely only on experimentally validated drug-target interactions (i.e., positive samples) for the predictions. Their performance is severely impeded by the lack of reliable negative samples.

Authors

  • Yi Zheng
    Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, China.
  • Hui Peng
    College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
  • Xiaocai Zhang
    Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway Ultimo, Sydney, 2007, Australia.
  • Zhixun Zhao
    Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway Ultimo, Sydney, 2007, Australia.
  • Xiaoying Gao
    School of Engineering and Computer Science, Victoria University of Wellington, Cotton Building, Kelburn Campus, Wellington, 6140, New Zealand.
  • Jinyan Li

Keywords

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