title: Predicting bacterial community assemblages using an artificial neural network approach.
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.
authors.affiliation: Argonne National Laboratory, Biosciences Division, University of Illinois at Chicago, Chicago, IL, USA, plarsen@anl.gov.
authors.forename: Peter
authors.initials: P
authors.lastname: Larsen
authors.forename: Yang
authors.initials: Y
authors.lastname: Dai
authors.forename: Frank R
authors.initials: FR
authors.lastname: Collart
mesh_terms: D004777:Environment; D056186:Metagenomics / Q000379:methods*; D059013:Microbial Consortia; D016571:Neural Networks, Computer; D012984:Software