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) |
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Pages (from-to) | 292-296 |
Number of pages | 5 |
Journal | Cell Host and Microbe |
Volume | 10 |
Issue number | 4 |
DOIs |
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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.