Human-associated microbial signatures: Examining their predictive value

Dan Knights; Laura Wegener Parfrey; Jesse Zaneveld; Catherine Lozupone; Rob Knight

doi:10.1016/j.chom.2011.09.003

Human-associated microbial signatures: Examining their predictive value

Dan Knights, Laura Wegener Parfrey, Jesse Zaneveld, Catherine Lozupone, Rob Knight

Biotechnology Institute

Research output: Contribution to journal › Comment/debate › peer-review

104 Scopus citations

Abstract

Host-associated microbial communities are unique to individuals, affect host health, and correlate with disease states. Although advanced technologies capture detailed snapshots of microbial communities, high within- and between-subject variation hampers discovery of microbial signatures in diagnostic or forensic settings. We suggest turning to machine learning and discuss key directions toward harnessing human-associated microbial signatures.

Original language	English (US)
Pages (from-to)	292-296
Number of pages	5
Journal	Cell Host and Microbe
Volume	10
Issue number	4
DOIs	https://doi.org/10.1016/j.chom.2011.09.003
State	Published - Oct 4 2011

Bibliographical note

Funding Information:
This piece describes work in our lab funded in part by the Crohns and Colitis Foundation of America, the National Institutes of Health, the Bill and Melinda Gates Foundation, the Colorado Center for Biofuels and Biorefining, and the Howard Hughes Medical Institute.

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abstract = "Host-associated microbial communities are unique to individuals, affect host health, and correlate with disease states. Although advanced technologies capture detailed snapshots of microbial communities, high within- and between-subject variation hampers discovery of microbial signatures in diagnostic or forensic settings. We suggest turning to machine learning and discuss key directions toward harnessing human-associated microbial signatures.",

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AU - Knight, Rob

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AB - Host-associated microbial communities are unique to individuals, affect host health, and correlate with disease states. Although advanced technologies capture detailed snapshots of microbial communities, high within- and between-subject variation hampers discovery of microbial signatures in diagnostic or forensic settings. We suggest turning to machine learning and discuss key directions toward harnessing human-associated microbial signatures.

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Human-associated microbial signatures: Examining their predictive value

Abstract

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