Machine learning: novel bioinformatics approaches for combating antimicrobial resistance.

Journal: Current opinion in infectious diseases
Published Date: Dec 1, 2017

Abstract

PURPOSE OF REVIEW: Antimicrobial resistance (AMR) is a threat to global health and new approaches to combating AMR are needed. Use of machine learning in addressing AMR is in its infancy but has made promising steps. We reviewed the current literature on the use of machine learning for studying bacterial AMR.

Authors

  • Nenad Macesic
    aDivision of Infectious Diseases, Columbia University Medical Center bDepartment of Biomedical Informatics, Columbia University, New York City, New York, USA cDepartment of Infectious Diseases, Austin Health, Heidelberg, Victoria, Australia.
  • Fernanda Polubriaginof
    Department of Biomedical Informatics, Columbia University, New York, USA.
  • Nicholas P Tatonetti
    Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, 622 West 168th St VC5, New York, NY 10032, USA. Electronic address: nick.tatonetti@columbia.edu.

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