Abstract
Microbial communities are found in nearly all environments and play a critical role in defining ecosystem service. Understanding the relationship between these microbial communities and their environment is essential for prediction of community structure, robustness, and response to ecosystem changes. Microbial Assemblage Prediction (MAP) describes microbial community structure as an artificial neural network (ANN) that models the microbial community as functions of environmental parameters and community intra-microbial interactions. MAP models can be used to predict community assemblages over a wide range of possible environmental parameters, extrapolate the results of point observations across spatial scales, and make predictions about how microbial communities may fluctuate as the result of changes in their environment.
The submitted manuscript has been created by UChicago Argonne, LLC, operator of Argonne National Laboratory (“Argonne”). Argonne, a US Department of Energy Office of Science laboratory, is operated under contract no. DE-AC02-06CH11357. The US Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in the said article to reproduce and prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the government.
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Larsen, P., Dai, Y., Collart, F.R. (2015). Predicting Bacterial Community Assemblages Using an Artificial Neural Network Approach. In: Cartwright, H. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 1260. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2239-0_3
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DOI: https://doi.org/10.1007/978-1-4939-2239-0_3
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