ArticleNature communications2025
Predicting microbial community structure and temporal dynamics by using graph neural network models.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Foundation Models for Microbiome Research: From Sequence Semantics to Community Dynamics and Multimodal World Models.Advanced genetics (Hoboken, N.J.) · 2026Review
- Probiotics and Postbiotics in Life-Style Disease Management: A Comprehensive Review on the Technologies in the Era of Omics and Artificial Intelligence.Probiotics and antimicrobial proteins · 2026Review
- Omics-Guided Construction of Microbial Consortia for Reproducible Traditional Fermented Foods and Beverages.Foods (Basel, Switzerland) · 2026Review
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- Artificial intelligence in microbiology: implications for metagenomics, diagnostics, and AMR surveillance.Biomedical engineering online · 2026Review
- Network Analysis in Microbiome Research: Methods, Tools, and Applications.Methods in molecular biology (Clifton, N.J.) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Understanding species-level abundance dynamics in complex microbial communities is key to managing microbial ecosystems, yet it remains a major challenge. In wastewater treatment plants (WWTPs), the presence and abundance of process-critical bacteria are essential for removing or recycling pollutants. However, individual species can fluctuate without recurring patterns. Accurately forecasting these dynamics is critical for preventing failures and guiding process optimization. We have developed a graph neural network-based model that uses only historical relative abundance data to predict future dynamics. Each model is trained and tested on individual time-series from 24 full-scale Danish WWTPs (4709 samples collected over 3-8 years, 2-5 times per month). It accurately predicts species dynamics up to 10 time points ahead (2-4 months), sometimes up to 20 (8 months). The approach, implemented as the "mc-prediction" workflow, is also tested on other datasets, including a human gut microbiome, showing its suitability for any longitudinal microbial dataset.
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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.