ReviewFrontiers in microbiology2021
Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.
Review in Frontiers in microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 204 papers, 6 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
204 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- STROBE-causal machine learning for the human microbiome: systematic review on methodological innovations and validation frameworks.Frontiers in microbiology · 2026Pooled it
- Worldwide research trends on theFrontiers in immunology · 2026Pooled it
- Structural and functional brain abnormal alteration in patients with type 2 diabetes mellitus: A coordinate-based meta-analysis.Translational psychiatry · 2025Pooled it
- Cross-cohort analysis identifies shared gut microbial signatures and validates microbial risk scores for colorectal cancer.Journal of translational medicine · 2025Pooled it
- Beyond just correlation: causal machine learning for the microbiome, from prediction to health policy with econometric tools.Frontiers in microbiology · 2025Pooled it
- Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa.PLoS computational biology · 2022Pooled it
- Gut Colonization With Vancomycin-Resistant Enterococcus Shapes the Gut Microbiome in the Intensive Care Unit.The Journal of infectious diseases · 2025Trial
- The Efficacy of Short-Term Weight Loss Programs and Consumption of Natural Probiotic Bryndza Cheese on Gut Microbiota Composition in Women.Nutrients · 2021Trial
- The cervicovaginal gut microbiota axis as a key determinant of systemic physiology.Gut microbes · 2026Review
- HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing.Computer methods in applied mechanics and engineering · 2026Article
- Artificial intelligence in human microbiome research: from data integration to explainable translation.Archives of microbiology · 2026Review
- Microbiome-based diagnostic biomarkers in pancreatic ductal adenocarcinoma: Current evidence and translational challenges (Review).Oncology letters · 2026Review
- From trees to graphs: rethinking phylogeny in microbiome prediction.Applied and environmental microbiology · 2026Article
- Predicting bacterial vaginosis incidence using artificial neural networks.PLoS computational biology · 2026Article
- Adaptive graph learning of microbial phylogeny enables accurate and interpretable microbiome-based host phenotype prediction.Applied and environmental microbiology · 2026Article
- Next-generation probiotics: an outlook into current applications and future developments.Nature reviews. Microbiology · 2026Review
- Microbiome and aging: Trajectories of microbiome age across human ecosystems and their systemic effects.iMeta · 2026Review
- Gut Microbiome Dysbiosis in Atopic Dermatitis: Pathogenic Mechanisms, Gut-Skin Axis Disruption, and Emerging Microbiota-Targeted Therapies.Biomedicines · 2026Review
- Machine learning reveals biocontrol agents shaping disease outcome in natural Arabidopsis populations.Nature communications · 2026Article
- Microbiome-guided cancer immunotherapy: immune mechanisms, resistance pathways, and translational opportunities for precision oncology.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
144 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
29 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.
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Identifiers
What Socratic 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 Socratic graph.