Machine learning performance in a microbial molecular autopsy context: A cross-sectional postmortem human population study.

Journal: PloS one
Published Date: Jan 1, 2019

Abstract

BACKGROUND: The postmortem microbiome can provide valuable information to a death investigation and to the human health of the once living. Microbiome sequencing produces, in general, large multi-dimensional datasets that can be difficult to analyze and interpret. Machine learning methods can be useful in overcoming this analytical challenge. However, different methods employ distinct strategies to handle complex datasets. It is unclear whether one method is more appropriate than others for modeling postmortem microbiomes and their ability to predict attributes of interest in death investigations, which require understanding of how the microbial communities change after death and may represent those of the once living host.

Authors

  • Yu Zhang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Jennifer L Pechal
    Michigan State University, Department of Entomology, East Lansing, MI, United States of America.
  • Carl J Schmidt
    Department of Food and Animal Sciences, University of Delaware, Newark, DE, 19711, USA.
  • Heather R Jordan
    Mississippi State University, Department of Biological Sciences, Mississippi State, MS, United States of America.
  • Wesley W Wang
    Texas A&M University, Department of Chemistry, College Station, TX, United States of America.
  • M Eric Benbow
    Michigan State University, Department of Entomology, East Lansing, MI, United States of America.
  • Sing-Hoi Sze
    Texas A&M University, Department of Computer Science and Engineering, College Station, TX, United States of America.
  • Aaron M Tarone
    Texas A&M University, Department of Entomology, College Station, TX, United States of America.

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