ReviewISME communications2022
Machine learning and deep learning applications in microbiome research.
Review in ISME communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 158 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
158 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Maternal influences on oral microbiome development and implications for early childhood health: a systematic review.Infection and immunity · 2026Pooled it
- STROBE-causal machine learning for the human microbiome: systematic review on methodological innovations and validation frameworks.Frontiers in microbiology · 2026Pooled it
- Meta-Analysis of the Gut Microbiome: An African American Representation.International journal of environmental research and public health · 2025Pooled it
- Harnessing plant-microbe interactions: strategies for enhancing resilience and nutrient acquisition for sustainable agriculture.Frontiers in plant science · 2025Pooled it
- Decoding the microbiome: artificial intelligence-targeted gut microenvironment breakthroughs in personalized cancer therapy.Gut microbes · 2026Review
- Artificial intelligence in human microbiome research: from data integration to explainable translation.Archives of microbiology · 2026Review
- Evaluating Vaginal Microbiome Effects on Antiretroviral Exposure in the Female Genital Tract Using Pharmacometric and Machine Learning Approaches.Clinical pharmacology and therapeutics · 2026Article
- The Integrated Management of Sugar-Beet Soilborne Diseases Through Rhizosphere Microbiome Strategies: A Critical Review of Agronomic Evidence and Application Gaps.Plants (Basel, Switzerland) · 2026Review
- Inferred functional microbiome features are associated with clinical trajectories of low-grade cervical lesions.BMC microbiology · 2026Article
- Major Depressive Disorder Signatures: A Review of Artificial Intelligence (AI)-Powered Insights Into Gut Dysbiosis.Health science reports · 2026Article
- High-accuracy SNV calling for bacterial isolates using deep learning with AccuSNV.Genome research · 2026Article
- Artificial intelligence-driven advancements in agricultural biotechnology.Journal, genetic engineering & biotechnology · 2026Review
- Robust and Interpretable Metagenomic Modeling Through Structure-Aware Multi-View Learning and Attribution-Guided Biological Insight.Research square · 2026Article
- Microbiome and aging: Trajectories of microbiome age across human ecosystems and their systemic effects.iMeta · 2026Review
- Next-generation probiotics: an outlook into current applications and future developments.Nature reviews. Microbiology · 2026Review
- Beyond omics: From descriptive profiling to causal and scalable design of fermentation microbiomes.iMeta · 2026Article
- Machine learning reveals biocontrol agents shaping disease outcome in natural Arabidopsis populations.Nature communications · 2026Article
- Enhancing the understanding of environmental microbiomes through topic modeling: a quantitative and qualitative analysis.Environmental microbiome · 2026Article
- Predicting the seed microbiome using phylogeny-driven machine learning.Environmental microbiome · 2026Article
- SubCom analysis: dissecting microbial community functions into taxon-specific contributions and interaction contexts.Microbiome · 2026Article
98 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The many microbial communities around us form interactive and dynamic ecosystems called microbiomes. Though concealed from the naked eye, microbiomes govern and influence macroscopic systems including human health, plant resilience, and biogeochemical cycling. Such feats have attracted interest from the scientific community, which has recently turned to machine learning and deep learning methods to interrogate the microbiome and elucidate the relationships between its composition and function. Here, we provide an overview of how the latest microbiome studies harness the inductive prowess of artificial intelligence methods. We start by highlighting that microbiome data - being compositional, sparse, and high-dimensional - necessitates special treatment. We then introduce traditional and novel methods and discuss their strengths and applications. Finally, we discuss the outlook of machine and deep learning pipelines, focusing on bottlenecks and considerations to address them.
Identifiers
What 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.