ArticleAmerican journal of physiology. Gastrointestinal and liver physiology2021
Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases.
Article in American journal of physiology. Gastrointestinal and liver physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.
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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
56 citing papers in PubMed, 1 synthesis or guideline pooled it, 88 citations in OpenAlex.
- A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.Inflammatory bowel diseases · 2022Pooled it
- Microbiome-metabolome generated bile acids gatekeep infliximab efficacy in Crohn's disease by licensing M1 suppression and Treg dominance.Journal of advanced research · 2026Observational
- Translating Gut Microbiota into Diagnostics: A Multidimensional Approach for the Diagnosis of Inflammatory Bowel Disease.Gut and liver · 2026Review
- From dysbiosis to precision medicine: targeting the microbial-metabolic axis in IBD management.Frontiers in cellular and infection microbiology · 2026Review
- Predicting anti-CCP positivity and early rheumatoid arthritis onset from routine laboratory parameters: a SHAP-explained machine learning pipeline.Frontiers in medicine · 2026Article
- Gastrointestinal microbiota and inflammasomes interplay in health and disease: a gut feeling.Gut · 2025Review
- Peripheral Blood TCR Clonotype Diversity as a Biomarker for Colorectal Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- SHAP-based binarization enhances metataxonomic machine learning with application to gut microbiota of inflammatory bowel disease.Scientific reports · 2025Article
- IgG4 unveiled: navigating the interplay with Crohn's disease - from immunology insights to machine learning.Annals of medicine and surgery (2012) · 2025Review
- Artificial intelligence in inflammatory bowel disease.Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association · 2025Review
- Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases.BMC microbiology · 2025Article
- Diagnostic Methods and Biomarkers in Inflammatory Bowel Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Operationalizing Team Science at the Academic Cancer Center Network to Unveil the Structure and Function of the Gut Microbiome.Journal of clinical medicine · 2025Review
- Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges.Chronic diseases and translational medicine · 2025Review
- Using gut microbiome metagenomic hypervariable features for diabetes screening and typing through supervised machine learning.Microbial genomics · 2025Article
- Paradigm Shift in Inflammatory Bowel Disease Management: Precision Medicine, Artificial Intelligence, and Emerging Therapies.Journal of clinical medicine · 2025Review
- Biomarker Quantification of Gut Dysbiosis-Derived Inflammation: A Review.Journal of inflammation research · 2025Article
- Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review.American journal of translational research · 2025Review
- Artificial intelligence in inflammatory bowel disease: innovations in diagnosis, monitoring, and personalized care.Therapeutic advances in gastroenterology · 2025Review
- Utility of Machine Learning to Characterize Gut Microbiota Dysbiosis and Its Clinical Implications in Inflammatory Bowel Disease.Journal of inflammation research · 2025Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Despite the availability of various diagnostic tests for inflammatory bowel diseases (IBD), misdiagnosis of IBD occurs frequently, and thus, there is a clinical need to further improve the diagnosis of IBD. As gut dysbiosis is reported in patients with IBD, we hypothesized that supervised machine learning (ML) could be used to analyze gut microbiome data for predictive diagnostics of IBD. To test our hypothesis, fecal 16S metagenomic data of 729 subjects with IBD and 700 subjects without IBD from the American Gut Project were analyzed using five different ML algorithms. Fifty differential bacterial taxa were identified [linear discriminant analysis effect size (LEfSe): linear discriminant analysis (LDA) score > 3] between the IBD and non-IBD groups, and ML classifications trained with these taxonomic features using random forest (RF) achieved a testing area under the receiver operating characteristic curves (AUC) of ∼0.80. Next, we tested if operational taxonomic units (OTUs), instead of bacterial taxa, could be used as ML features for diagnostic classification of IBD. Top 500 high-variance OTUs were used for ML training, and an improved testing AUC of ∼0.82 (RF) was achieved. Lastly, we tested if supervised ML could be used for differentiating Crohn's disease (CD) and ulcerative colitis (UC). Using 331 CD and 141 UC samples, 117 differential bacterial taxa (LEfSe: LDA score > 3) were identified, and the RF model trained with differential taxonomic features or high-variance OTU features achieved a testing AUC > 0.90. In summary, our study demonstrates the promising potential of artificial intelligence via supervised ML modeling for predictive diagnostics of IBD using gut microbiome data.
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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.