ArticleFrontiers in microbiology2022
Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes.
Article in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Differential Immune Priming Effects of Banana Extracellular Self-DNA Derived from Bananas with Varying Disease Severities AgainstJournal of fungi (Basel, Switzerland) · 2026Article
- From dysbiosis to precision medicine: targeting the microbial-metabolic axis in IBD management.Frontiers in cellular and infection microbiology · 2026Review
- Specification curve analysis of the TEDDY study reveals large variation in microbiome-based T1D predictive performance.Nature communications · 2025Article
- KADAIF: an anomaly detection method for complex microbiome data.Bioinformatics (Oxford, England) · 2025Article
- AI-driven multi-omics modeling of myalgic encephalomyelitis/chronic fatigue syndrome.Nature medicine · 2025Article
- IgG4 unveiled: navigating the interplay with Crohn's disease - from immunology insights to machine learning.Annals of medicine and surgery (2012) · 2025Review
- Identification of Key Features Pivotal to the Characteristics and Functions of Gut Bacteria Taxa through Machine Learning Methods.Current gene therapy · 2025Article
- Article
- Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease.Journal of translational medicine · 2025Article
- BioMapAI: Artificial Intelligence Multi-Omics Modeling of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome.bioRxiv : the preprint server for biology · 2025Article
- Early subclinical stages of the inflammatory bowel diseases: insights from human and animal studies.American journal of physiology. Gastrointestinal and liver physiology · 2025Review
- Utility of Machine Learning to Characterize Gut Microbiota Dysbiosis and Its Clinical Implications in Inflammatory Bowel Disease.Journal of inflammation research · 2025Review
- Development and Validation of Predictive Models for Inflammatory Bowel Disease Diagnosis: A Machine Learning and Nomogram-Based Approach.Journal of inflammation research · 2025Article
- Gut-microbiota-based ensemble model predicts prognosis of pediatric inflammatory bowel disease.iScience · 2024Article
- Challenges in IBD Research 2024: Precision Medicine.Inflammatory bowel diseases · 2024Review
- A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions.Frontiers in microbiology · 2024Review
- Comprehensive data optimization and risk prediction framework: machine learning methods for inflammatory bowel disease prediction based on the human gut microbiome data.Frontiers in microbiology · 2024Article
- A Machine Learning-Based Diagnostic Model for Crohn's Disease and Ulcerative Colitis Utilizing Fecal Microbiome Analysis.Microorganisms · 2023Article
- Crohn's Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome.Diagnostics (Basel, Switzerland) · 2023Article
- Potential Oral Microbial Markers for Differential Diagnosis of Crohn's Disease and Ulcerative Colitis Using Machine Learning Models.Microorganisms · 2023Article
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Authors and funding
4 authors.
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Abstract
Inflammatory bowel disease (IBD) is a chronic disease with unknown pathophysiological mechanisms. There is evidence of the role of microorganims in this disease development. Thanks to the open access to multiple omics data, it is possible to develop predictive models that are able to prognosticate the course and development of the disease. The interpretability of these models, and the study of the variables used, allows the identification of biological aspects of great importance in the development of the disease. In this work we generated a metagenomic signature with predictive capacity to identify IBD from fecal samples. Different Machine Learning models were trained, obtaining high performance measures. The predictive capacity of the identified signature was validated in two external cohorts. More precisely a cohort containing samples from patients suffering Ulcerative Colitis and another from patients suffering Crohn's Disease, the two major subtypes of IBD. The results obtained in this validation (AUC 0.74 and AUC = 0.76, respectively) show that our signature presents a generalization capacity in both subtypes. The study of the variables within the model, and a correlation study based on text mining, identified different genera that play an important and common role in the development of these two subtypes.
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