ReviewArchives of microbiology2026
Artificial intelligence in human microbiome research: from data integration to explainable translation.
Review in Archives of microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The human microbiome is integral to health, metabolism, immunity, and physical protection, with dysbiosis linked to metabolic disorders, autoimmune conditions, and cancer. High-throughput sequencing has generated massive datasets, but analyzing these complex, multidimensional data poses significant challenges. Artificial intelligence (AI), including machine learning, deep learning, and related approaches, offers powerful solutions for data integration, pattern recognition, functional annotation, and predictive modeling. AI applications in microbiome research include identifying microbial biomarkers, predicting disease susceptibility, elucidating microbial interactions, and optimizing personalized interventions. AI also enables integration of microbiome data with host genomics, transcriptomics, and clinical metadata, providing a holistic view of host-microbiome dynamics. Despite advances, challenges such as data heterogeneity, model interpretability, and limited sample sizes remain. This narrative review synthesizes literature from 2015 to 2025 without a formal systematic methodology. We conclude with three priority areas for AI-microbiome translation: improved interpretability, longitudinal modeling, and clinical integration. Leveraging AI in this field holds significant promise for accelerating precision medicine, improving diagnostics, and developing microbiome-targeted therapies, if interpretability and validation challenges are addressed.
Indexed as
Identifiers
42859175What 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.