ArticleCell regeneration (London, England)2025
Identification of a 10-species microbial signature of inflammatory bowel disease by machine learning and external validation.
Article in Cell regeneration (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- From Multi-Omics to Molecular Causality: A Five-Tier Evidence Framework for the Single-Bacterium-Metabolite Axis in IBD.Microorganisms · 2026Review
- Recent Advances in Artificial Intelligence for Endoscopic and Multimodal Assessment of Inflammatory Bowel Disease: A Review.International journal of general medicine · 2026Review
- From dysbiosis to precision medicine: targeting the microbial-metabolic axis in IBD management.Frontiers in cellular and infection microbiology · 2026Review
- Utility of Machine Learning to Characterize Gut Microbiota Dysbiosis and Its Clinical Implications in Inflammatory Bowel Disease.Journal of inflammation research · 2025Review
- Predicting Risk of Post-Treatment Relapse in Patients with Inflammatory Bowel Disease Based on Intestinal Microbiota.Journal of inflammation research · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Genetic and microbial factors influence inflammatory bowel disease (IBD), prompting our study on non-invasive biomarkers for enhanced diagnostic precision. Using the XGBoost algorithm and variable analysis and the published metadata, we developed the 10-species signature XGBoost classification model (XGB-IBD10). By using distinct species signatures and prior machine and deep learning models and employing standardization methods to ensure comparability between metagenomic and 16S sequencing data, we constructed classification models to assess the XGB-IBD10 precision and effectiveness. XGB-IBD10 achieved a notable accuracy of 0.8722 in testing samples. In addition, we generated metagenomic sequencing data from collected 181 stool samples to validate our findings, and the model reached an accuracy of 0.8066. The model's performance significantly improved when trained on high-quality data from the Chinese population. Furthermore, the microbiome-based model showed promise in predicting active IBD. Overall, this study identifies promising non-invasive biomarkers associated with IBD, which could greatly enhance diagnostic accuracy.
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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.