Conditional Forest Models Built Using Metagenomic Data Accurately Predicted Salmonella Contamination in Northeastern Streams-Reference-Cited by-同舟云学术

Conditional Forest Models Built Using Metagenomic Data Accurately Predicted Salmonella Contamination in Northeastern Streams

Published:2023-04-13 Issue:2 Volume:11 Page:
ISSN:2165-0497
Container-title:Microbiology Spectrum
language:en
Short-container-title:Microbiol Spectr

Author:

Chung Taejung¹²,Yan Runan¹²,Weller Daniel L.³^ORCID,Kovac Jasna¹²^ORCID

Affiliation:

1. Department of Food Science, The Pennsylvania State University, University Park, Pennsylvania, USA

2. Microbiome Center, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA

3. Department of Statistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA

Abstract

Understanding the associations between surface water microbiome composition and the presence of foodborne pathogens, such as Salmonella , can facilitate the identification of novel indicators of Salmonella contamination. This study assessed the utility of microbiome data and three machine learning algorithms for predicting Salmonella contamination of Northeastern streams.

Funder

USDA | National Institute of Food and Agriculture

PSU | Institutes of Energy and the Environment, Pennsylvania State University

Publisher

American Society for Microbiology

Subject

Infectious Diseases,Cell Biology,Microbiology (medical),Genetics,General Immunology and Microbiology,Ecology,Physiology

Link

https://journals.asm.org/doi/pdf/10.1128/spectrum.00381-23

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