ArticleiMeta2022
Predicting microbiome compositions from species assemblages through deep learning.
Article in iMeta, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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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
36 citing papers in PubMed.
- Deciphering microbial community dynamics using cross-sectional data-informed NeuralODE.Microbiome · 2026Article
- Review
- Predicting the seed microbiome using phylogeny-driven machine learning.Environmental microbiome · 2026Article
- From chaos to order: cross-kingdom coordination buffers microbial communities against typhoon impacts.BMC microbiology · 2026Article
- Integrating host-microbiome multi-omics with machine learning: methods, benchmarks, and translational applications.Science China. Life sciences · 2026Review
- AI-empowered human microbiome research.Gut · 2026Review
- A Microbial Odyssey: Reshaping Probiotic Destinies through Advanced Delivery Technologies.Probiotics and antimicrobial proteins · 2026Review
- Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- KeySDL: sparse dictionary learning for keystone microbe identification from steady-state observations using a dynamical-systems model.BioData mining · 2026Article
- Quorum-sensing microbial dark matter enables core functions in wastewater microbiomes.ISME communications · 2026Article
- Knowledge graph embedding for predicting and analyzing microbial interactions.Scientific reports · 2025Article
- Credible inferences in microbiome research: ensuring rigour, reproducibility and relevance in the era of AI.Nature reviews. Gastroenterology & hepatology · 2025Review
- Deep Learning Transforms Phage-Host Interaction Discovery from Metagenomic Data.bioRxiv : the preprint server for biology · 2025Article
- Deep learning for microbiome-informed precision nutrition.National science review · 2025Article
- The spatiotemporal transcriptional profiling of murine brain during cerebral malaria progression and after artemisinin treatment.Nature communications · 2025Article
- Agricultural practices influence soil microbiome assembly and interactions at different depths identified by machine learning.Communications biology · 2024Article
- Emergent ecological patterns and modelling of gut microbiomes in health and in disease.PLoS computational biology · 2024Article
- The active effect ofiMetaOmics · 2024Article
- Deep learning methods in metagenomics: a review.Microbial genomics · 2024Review
- Effects of operational parameters on bacterial communities in Hong Kong and global wastewater treatment plants.mSystems · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Microbes can form complex communities that perform critical functions in maintaining the integrity of their environment or their hosts' well-being. Rationally managing these microbial communities requires improving our ability to predict how different species assemblages affect the final species composition of the community. However, making such a prediction remains challenging because of our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. To overcome this challenge, we present a deep learning framework that automatically learns the map between species assemblages and community compositions from training data only, without knowing any of the above processes. First, we systematically validate our framework using synthetic data generated by classical population dynamics models. Then, we apply our framework to data from
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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.