Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors.

Journal: Bioinformatics (Oxford, England)
Published Date: Jun 1, 2020

Abstract

MOTIVATION: Identification of virulence factors (VFs) is critical to the elucidation of bacterial pathogenesis and prevention of related infectious diseases. Current computational methods for VF prediction focus on binary classification or involve only several class(es) of VFs with sufficient samples. However, thousands of VF classes are present in real-world scenarios, and many of them only have a very limited number of samples available.

Authors

  • Dandan Zheng
    Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA.
  • Guansong Pang
    Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA 5005, Australia.
  • Bo Liu
    Wuhan United Imaging Healthcare Surgical Technology Co., Ltd., Wuhan, China.
  • Lihong Chen
    NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100176, China.
  • Jian Yang
    Drug Discovery and Development Research Group, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, SK, Canada.

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