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Filtering by: Contributor Centers for Disease Control and Prevention (funding agency) Creator Alexander G. McFarland Creator Anahid A. Moghadam Type Work Keyword Machine learning Keyword Quantitative metagenomics Subject Machine learning Subject Metagenomics Subject Quantitative research

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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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