Identification of immune correlates of protection in Shigella infection by application of machine learning.

Journal: Journal of biomedical informatics
Published Date: Oct 1, 2017

Abstract

BACKGROUND: Immunologic correlates of protection are important in vaccine development because they give insight into mechanisms of protection, assist in the identification of promising vaccine candidates, and serve as endpoints in bridging clinical vaccine studies. Our goal is the development of a methodology to identify immunologic correlates of protection using the Shigella challenge as a model.

Authors

  • Jorge M Arevalillo
    Department of Statistics and Operational Research, University Nacional Educación a Distancia, Paseo Senda del Rey 9, 28040 Madrid, Spain. Electronic address: jmartin@ccia.uned.es.
  • Marcelo B Sztein
    Center for Vaccine Development, Departments of Pediatrics and Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA. Electronic address: msztein@medicine.umaryland.edu.
  • Karen L Kotloff
    Center for Vaccine Development, Departments of Pediatrics and Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA. Electronic address: KKOTLOFF@medicine.umaryland.edu.
  • Myron M Levine
    Center for Vaccine Development, Departments of Pediatrics and Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA. Electronic address: MLevine@medicine.umaryland.edu.
  • Jakub K Simon
    Merck & Co., Inc., Kenilworth, NJ, USA. Electronic address: jakub.simon@merck.com.

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