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Predicting bacterial community assemblages using an artificial neural network approach

Larsen, Peter E.; Field, Dawn; Gilbert, Jack A.. 2012 Predicting bacterial community assemblages using an artificial neural network approach. Nature Methods, 9 (6). 621-625. 10.1038/nmeth.1975

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Abstract/Summary

Understanding the interactions between the Earth's microbiome and the physical, chemical and biological environment is a fundamental goal of microbial ecology. We describe a bioclimatic modeling approach that leverages artificial neural networks to predict microbial community structure as a function of environmental parameters and microbial interactions. This method was better at predicting observed community structure than were any of several single-species models that do not incorporate biotic interactions. The model was used to interpolate and extrapolate community structure over time with an average Bray-Curtis similarity of 89.7. Additionally, community structure was extrapolated geographically to create the first microbial map derived from single-point observations. This method can be generalized to the many microbial ecosystems for which detailed taxonomic data are currently being generated, providing an observation-based modeling technique for predicting microbial taxonomic structure in ecological studies.

Item Type: Publication - Article
Digital Object Identifier (DOI): 10.1038/nmeth.1975
Programmes: CEH Topics & Objectives 2009 - 2012 > Biodiversity
CEH Sections: Hails
ISSN: 1548-7091
Additional Keywords: bioinformatics, microbiology, systems biology
NORA Subject Terms: Biology and Microbiology
Date made live: 17 Jan 2013 14:53 +0 (UTC)
URI: http://nora.nerc.ac.uk/id/eprint/21084

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