ArticleiScience2024
Explainable AI-prioritized plasma and fecal metabolites in inflammatory bowel disease and their dietary associations.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Microbiota-Derived Metabolites in the Epigenetic Regulation of Redox Homeostasis.Antioxidants (Basel, Switzerland) · 2026Review
- Probiotics and Postbiotics in Life-Style Disease Management: A Comprehensive Review on the Technologies in the Era of Omics and Artificial Intelligence.Probiotics and antimicrobial proteins · 2026Review
- Microbiome-metabolome generated bile acids gatekeep infliximab efficacy in Crohn's disease by licensing M1 suppression and Treg dominance.Journal of advanced research · 2026Observational
- Beyond Feature Selection: Interpretable Machine Learning for Mechanistic Insights in Metabolomics.Biology · 2026Review
- Interplay of Microbiome, Oxidative Stress and Inflammation in Health and Disease.Antioxidants (Basel, Switzerland) · 2026Review
- Artificial Intelligence in the Nutritional Management of Inflammatory Bowel Disease: A Scoping Review.Journal of multidisciplinary healthcare · 2026Review
- Explainable artificial intelligence for personalized management of inflammatory bowel disease: A minireview of recent advances.World journal of gastroenterology · 2025Review
- I-SVVS: integrative stochastic variational variable selection to explore joint patterns of multi-omics microbiome data.Briefings in bioinformatics · 2025Article
- Utility of Machine Learning to Characterize Gut Microbiota Dysbiosis and Its Clinical Implications in Inflammatory Bowel Disease.Journal of inflammation research · 2025Review
- Using deep neural networks and LASSO regression to predict miRNA expression changes based on mRNA data.Frontiers in bioinformatics · 2025Article
- Identifying inflammatory bowel disease subtypes: a comprehensive exploration of transcriptomic data and machine learning-based approaches.Therapeutic advances in gastroenterology · 2025Article
- Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data.Frontiers in oncology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fecal metabolites effectively discriminate inflammatory bowel disease (IBD) and show differential associations with diet. Metabolomics and AI-based models, including explainable AI (XAI), play crucial roles in understanding IBD. Using datasets from the UK Biobank and the Human Microbiome Project Phase II IBD Multi'omics Database (HMP2 IBDMDB), this study uses multiple machine learning (ML) classifiers and Shapley additive explanations (SHAP)-based XAI to prioritize plasma and fecal metabolites and analyze their diet correlations. Key findings include the identification of discriminative metabolites like glycoprotein acetyl and albumin in plasma, as well as nicotinic acid metabolites andurobilin in feces. Fecal metabolites provided a more robust disease predictor model (AUC [95%]: 0.93 [0.87-0.99]) compared to plasma metabolites (AUC [95%]: 0.74 [0.69-0.79]), with stronger and more group-differential diet-metabolite associations in feces. The study validates known metabolite associations and highlights the impact of IBD on the interplay between gut microbial metabolites and diet.
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Registered trials
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