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Filtering by: Contributor Centers for Disease Control and Prevention (funding agency) Creator Alexander G. McFarland Creator K. Allison Perry-Dow Keyword Environmental surveillance Keyword Infection prevention Keyword Low biomass Keyword Machine learning Language English Publisher Microbiome Resource type Research Paper Subject Environmental monitoring Subject Quantitative research

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1. An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics

Description:: Cite this article: Shen, J., McFarland, A.G., Blaustein, R.A. et al. An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics. Microbiome 10, 206 (2022). https://doi.org/10.1186/s40168-022-01412-x and Background: Effective surveillance of microbial communities in the healthcare environment is increasingly important in infection prevention. Metagenomics-based techniques are promising due to their untargeted nature but are currently challenged by several limitations: (1) they are not powerful enough to extract valid signals out of the background noise for low-biomass samples,...
Keyword:: Quantification, Quantitative metagenomics, Low biomass, Viability, Machine learning, Environmental surveillance, Sequencing depth prediction, and Infection prevention
Subject:: Metagenomics, Quantitative research, Nosocomial infections--Prevention, Machine learning, and Environmental monitoring
Creator:: K. Allison Perry-Dow, Jiaxian Shen, Vincent B. Young, Ryan A. Blaustein, Mary K. Hayden, Alexander G. McFarland, Anahid A. Moghadam, Laura J. Rose, and Erica M. Hartmann
Contributor:: Centers for Disease Control and Prevention (funding agency)
Owner:: Jiaxian SHEN
Publisher:: Microbiome
Language:: English
Date Uploaded:: 12/19/2022
Date Modified:: 12/19/2022
Date Created:: 2022-12-02
Resource Type:: Research Paper
Alternate Identifier:: DOI 10.1186/s40168-022-01412-x, PMCID PMC9716758, and PMID 36457108

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1. An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics