ArticleGut microbes
Comprehensive assessment of machine learning methods for diagnosing gastrointestinal diseases through whole metagenome sequencing data.
Article in Gut microbes. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A human gut metagenome-assembled genome catalogue spanning 41 countries supports genome-scale metabolic models.Nature microbiology · 2026Pooled it
- Artificial intelligence in human microbiome research: from data integration to explainable translation.Archives of microbiology · 2026Review
- Periodontal Dysbiosis and Premature Atherosclerosis: A Critical Appraisal of the Oral-Gut-Vascular Axis.Biomedicines · 2026Review
- Multidisciplinary Delphi consensus statement on minimal standards for clinical metadata and end points in microbiome studies.Nature reviews. Gastroenterology & hepatology · 2026Review
- Identification of Key Microbial Signatures Associated With Breast Cancer: Machine Learning-Based Approaches Using Gut Microbiome Data.Cancer medicine · 2026Article
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- Best practices for developing microbiome-based disease diagnostic classifiers through machine learning.Gut microbes · 2025Article
- Evaluation of nomogram prediction model for postoperative deep vein thrombosis in patients with non-small cell lung cancer.Translational cancer research · 2025Article
- Rumen Microbiota-Based Machine Learning Approach for Predicting Heat Stress and Identifying Associated Microbes.Microbial ecology · 2025Article
- Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer.BMC cancer · 2025Article
- Utility of Machine Learning to Characterize Gut Microbiota Dysbiosis and Its Clinical Implications in Inflammatory Bowel Disease.Journal of inflammation research · 2025Review
- Benchmarking and optimizing microbiome-based bioinformatics workflow for non-invasive detection of intestinal tumors.Microbiome research reports · 2025Article
- Gut-Microbiome Interactions: Characterization, Therapeutic Implications and Machine Learning.Sage open pathologyReview
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The gut microbiome, linked significantly to host diseases, offers potential for disease diagnosis through machine learning (ML) pipelines. These pipelines, crucial in modeling diseases using high-dimensional microbiome data, involve selecting profile modalities, data preprocessing techniques, and classification algorithms, each impacting the model accuracy and generalizability. Despite whole metagenome shotgun sequencing (WMS) gaining popularity for human gut microbiome profiling, a consensus on the optimal methods for ML pipelines in disease diagnosis using WMS data remains elusive. Addressing this gap, we comprehensively evaluated ML methods for diagnosing Crohn's disease and colorectal cancer, using 2,553 fecal WMS samples from 21 case-control studies. Our study uncovered crucial insights: gut-specific, species-level taxonomic features proved to be the most effective for profiling; batch correction was not consistently beneficial for model performance; compositional data transformations markedly improved the models; and while nonlinear ensemble classification algorithms typically offered superior performance, linear models with proper regularization were found to be more effective for diseases that are linearly separable based on microbiome data. An optimal ML pipeline, integrating the most effective methods, was validated for generalizability using holdout data. This research offers practical guidelines for constructing reliable disease diagnostic ML models with fecal WMS 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.