ArticleNature methods2012
Predicting bacterial community assemblages using an artificial neural network approach.
Article in Nature methods, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
61 citing papers in PubMed.
- Predicting the seed microbiome using phylogeny-driven machine learning.Environmental microbiome · 2026Article
- Predicting microbial community structure and temporal dynamics by using graph neural network models.Nature communications · 2025Article
- Living Kombucha Electronics with Proteinoids.ACS omega · 2025Article
- Bayesian networks for network inference in biology.Journal of the Royal Society, Interface · 2025Review
- Forecasting the dynamics of a complex microbial community using integrated meta-omics.Nature ecology & evolution · 2024Article
- Predicting global distributions of eukaryotic plankton communities from satellite data.ISME communications · 2023Article
- Variations in the Intestinal Microbiota of the Chinese Soft-Shelled Turtle (Animals : an open access journal from MDPI · 2023Article
- Predicting microbial community compositions in wastewater treatment plants using artificial neural networks.Microbiome · 2023Article
- Environmental microbiology going computational-Predictive ecology and unpredicted discoveries.Environmental microbiology · 2023Article
- Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics.eLife · 2022Article
- Article
- Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges.Frontiers in microbiology · 2022Review
- Multiple thresholds and trajectories of microbial biodiversity predicted across browning gradients by neural networks and decision tree learning.ISME communications · 2021Article
- Predicting microbiomes through a deep latent space.Bioinformatics (Oxford, England) · 2021Article
- Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring.Computational and structural biotechnology journal · 2021Review
- Impact of Temporal pH Fluctuations on the Coexistence of Nasal Bacteria in anFrontiers in microbiology · 2021Article
- Molecular Ecological Network Complexity Drives Stand Resilience of Soil Bacteria to Mining Disturbances among Typical Damaged Ecosystems in China.Microorganisms · 2020Article
- Community Compositions of Phytoplankton and Eukaryotes during the Mixing Periods of a Drinking Water Reservoir: Dynamics and Interactions.International journal of environmental research and public health · 2020Article
- Article
- Community ecology as a framework for human microbiome research.Nature medicine · 2019Review
1 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
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22504588What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.