ArticleMicrobiome2023
Predicting microbial community compositions in wastewater treatment plants using artificial neural networks.
Article in Microbiome, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Integrated global assessment ofEco-Environment & Health · 2026Article
- Soil microbiome prediction using traditional machine learning and deep learning models.Scientific reports · 2026Article
- The microbial community metabolic regime adapts to hydraulic disturbance in river-lake systems with high-frequency regulation.Environmental microbiome · 2025Article
- Predicting microbial community structure and temporal dynamics by using graph neural network models.Nature communications · 2025Article
- Harnessing Engineered Microbial Consortia for Xenobiotic Bioremediation: Integrating Multi-Omics and AI for Next-Generation Wastewater Treatment.Journal of xenobiotics · 2025Review
- Machine learning-based identification of wastewater treatment plant-specific microbial indicators using 16S rRNA gene sequencing.Scientific reports · 2025Article
- Unlocking the potential of designed microbial consortia: A breakthrough for sustainable waste management and climate resilience.Environmental science and ecotechnology · 2025Article
- Microbial function in food fermentations: Current research and future directions.Current research in microbial sciences · 2025Article
- Agricultural practices influence soil microbiome assembly and interactions at different depths identified by machine learning.Communications biology · 2024Article
- A directional electrode separator improves anodic biofilm current density in a well-mixed single-chamber bioelectrochemical system.Enzyme and microbial technology · 2024Article
- Microbial core communities in activated sludge plants are strongly affected by immigration and geography.Environmental microbiome · 2024Article
- MicroMPN: methods and software for high-throughput screening of microbe suppression in mixed populations.Microbiology spectrum · 2024Article
- Re-considering and designing microbiomes for future waste biorefinery.Microbial biotechnology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundActivated sludge (AS) of wastewater treatment plants (WWTPs) is one of the world's largest artificial microbial ecosystems and the microbial community of the AS system is closely related to WWTPs' performance. However, how to predict its community structure is still unclear.
resultsHere, we used artificial neural networks (ANN) to predict the microbial compositions of AS systems collected from WWTPs located worldwide. The predictive accuracy R
conclusionsWe demonstrated that the microbial compositions and major functional groups of AS systems are predictable using our approach, and IndConInf has a significant impact on the prediction. Our results provide a better understanding of the factors affecting AS communities through the prediction of the microbial community of AS systems, which could lead to insights for improved operating parameters and control of community structure. Video Abstract.
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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.