Predicting bacterial community assemblages using an artificial neural network approach View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-06

AUTHORS

Peter E Larsen, Dawn Field, Jack A Gilbert

ABSTRACT

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. More... »

PAGES

621

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nmeth.1975

DOI

http://dx.doi.org/10.1038/nmeth.1975

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PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22504588


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