ReviewMicrobial cell factories2022
Machine learning for data integration in human gut microbiome.
Review in Microbial cell factories, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 2 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
73 citing papers in PubMed, 2 syntheses or guidelines pooled it, 108 citations in OpenAlex.
- Beyond just correlation: causal machine learning for the microbiome, from prediction to health policy with econometric tools.Frontiers in microbiology · 2025Pooled it
- A bibliometric study on the impact of gut microbiota on the efficacy of immune checkpoint inhibitors in cancer patients: analysis of the top 100 cited articles.Frontiers in immunology · 2024Pooled it
- From gut dysbiosis to eubiosis: understanding microbiome recovery as an ecological process.Gut microbes · 2026Review
- The Role of Artificial Intelligence and Machine Learning in Revolutionizing Probiotic Research.Microorganisms · 2026Review
- The silent pharmacist: Harnessing the gut microbiome to improve therapy in hematologic malignancies.Translational oncology · 2026Review
- Microbiome and aging: Trajectories of microbiome age across human ecosystems and their systemic effects.iMeta · 2026Review
- Oligosaccharide prebiotics in functional foods and therapeutics: innovations and challenges.3 Biotech · 2026Review
- Decoding Microbiota-Immune Interplay in Viral Pathogenesis: Toward Next-Generation Antiviral Therapies.Probiotics and antimicrobial proteins · 2026Review
- Probiotic-Plant Bioactive Synergy in Gut Health: Mechanisms, Antimicrobial Activity, and Translational Challenges.Nutrients · 2026Review
- From diet to function: using synthetic microbial communities to map gut microbial interactions.NPJ biofilms and microbiomes · 2026Review
- Functional Reconstruction of the Gut Microecosystem in Gastrointestinal Motility Disorders: Roles of Probiotics, Prebiotics, Synbiotics and Postbiotics.Probiotics and antimicrobial proteins · 2026Review
- AI-empowered human microbiome research.Gut · 2026Review
- Review
- Dairy Bioactive Compounds as Precision Modulators of Gut Microbiota: From Molecular Mechanisms to Personalized Immunometabolic Health.Foods (Basel, Switzerland) · 2026Review
- Identification of Key Microbial Signatures Associated With Breast Cancer: Machine Learning-Based Approaches Using Gut Microbiome Data.Cancer medicine · 2026Article
- Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.mSystems · 2026Review
- Taxonomic and functional remodeling of the gut microbiota during aging and implications for microbiota-derived biomarkers.World journal of microbiology & biotechnology · 2026Review
- Review
- The gut microbiota and metabolomics in the pathogenesis of type 2 diabetes mellitus combined with coronary atherosclerotic heart disease.Scientific reports · 2026Article
- Distinct Gut Microbiota Signatures Characterize Glucoregulatory Effects of Prenatal Choline and Betaine in Wistar Rat Offspring.The Journal of nutrition · 2026Article
13 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
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Recent studies have demonstrated that gut microbiota plays critical roles in various human diseases. High-throughput technology has been widely applied to characterize the microbial ecosystems, which led to an explosion of different types of molecular profiling data, such as metagenomics, metatranscriptomics and metabolomics. For analysis of such data, machine learning algorithms have shown to be useful for identifying key molecular signatures, discovering potential patient stratifications, and particularly for generating models that can accurately predict phenotypes. In this review, we first discuss how dysbiosis of the intestinal microbiota is linked to human disease development and how potential modulation strategies of the gut microbial ecosystem can be used for disease treatment. In addition, we introduce categories and workflows of different machine learning approaches, and how they can be used to perform integrative analysis of multi-omics data. Finally, we review advances of machine learning in gut microbiome applications and discuss related challenges. Based on this we conclude that machine learning is very well suited for analysis of gut microbiome and that these approaches can be useful for development of gut microbe-targeted therapies, which ultimately can help in achieving personalized and precision medicine.
Indexed as
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.