ArticleJournal of clinical medicine2020
Usefulness of Machine Learning-Based Gut Microbiome Analysis for Identifying Patients with Irritable Bowels Syndrome.
Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 3 of them syntheses that pooled it.
What it found
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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
38 citing papers in PubMed, 3 syntheses or guidelines pooled it, 73 citations in OpenAlex.
- SCFA Alterations in IBS Subtypes: Systematic Evidence and Future Prospects for Biomarker-Based Diagnosis in India.Journal of microbiology and biotechnology · 2026Pooled it
- Diagnostic accuracy and quality of artificial intelligence models in irritable bowel syndrome: A systematic review.World journal of gastroenterology · 2025Pooled it
- Changes in Fecal Short-Chain Fatty Acids in IBS Patients and Effects of Different Interventions: A Systematic Review and Meta-Analysis.Nutrients · 2024Pooled it
- Frailty-Preventing Effect of an Intervention Program Using a Novel Complete Nutritional "COMB-FP Meal": A Pilot Randomized Control Trial.Nutrients · 2023Trial
- Review
- An adaptive weight self-distillation deep learning framework for phenotype prediction from longitudinal gut microbiome data.BMC microbiology · 2026Article
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Reframing precision nutrition in irritable bowel syndrome: a mechanism-informed conceptual framework for responder prediction and clinical translation.Frontiers in immunology · 2026Review
- Emerging role of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2025Review
- Decoding longitudinal microbiome trajectories: an interpretable machine learning approach for biomarker discovery and prediction.Briefings in bioinformatics · 2025Article
- The Gut Mycobiome for Precision Medicine.Journal of fungi (Basel, Switzerland) · 2025Review
- Exploring the role of normalization and feature selection in microbiome disease classification pipelines.GigaScience · 2025Article
- Peripheral Levels of Selected Biomarkers in Patients with Post-Sarcoidosis Chronic Fatigue Syndrome.Journal of inflammation research · 2025Article
- Longitudinal Microbiome-based Interpretable Machine Learning for Identification of Time-Varying Biomarkers in Early Prediction of Disease Outcomes.bioRxiv : the preprint server for biology · 2024Article
- Microbiome Shifts and Their Impact on Gut Physiology in Irritable Bowel Syndrome.International journal of molecular sciences · 2024Review
- Temporal and fertilizer-dependent dynamics of soil bacterial communities in buckwheat fields under long-term management.Scientific reports · 2024Article
- Methodology for biomarker discovery with reproducibility in microbiome data using machine learning.BMC bioinformatics · 2024Article
- A Systematic Review: Fecal Bacterial Profile in Patients with Irritable Bowel Syndrome Analyzed with the GA-Map Dysbiosis Test Based on the 16S rRNA Gene of Bacterial Species or Groups.Clinical and experimental gastroenterology · 2024Review
- The Potential Use of Artificial Intelligence in Irritable Bowel Syndrome Management.Diagnostics (Basel, Switzerland) · 2023Review
- Effect ofJournal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
18 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Irritable bowel syndrome (IBS) is diagnosed by subjective clinical symptoms. We aimed to establish an objective IBS prediction model based on gut microbiome analyses employing machine learning. We collected fecal samples and clinical data from 85 adult patients who met the Rome III criteria for IBS, as well as from 26 healthy controls. The fecal gut microbiome profiles were analyzed by 16S ribosomal RNA sequencing, and the determination of short-chain fatty acids was performed by gas chromatography-mass spectrometry. The IBS prediction model based on gut microbiome data after machine learning was validated for its consistency for clinical diagnosis. The fecal microbiome alpha-diversity indices were significantly smaller in the IBS group than in the healthy controls. The amount of propionic acid and the difference between butyric acid and valerate were significantly higher in the IBS group than in the healthy controls (
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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.