ArticleGut microbes2025
Best practices for developing microbiome-based disease diagnostic classifiers through machine learning.
Article in Gut microbes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- The cervicovaginal gut microbiota axis as a key determinant of systemic physiology.Gut microbes · 2026Review
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- NFS1 activates PI3K/AKT/mTOR signaling to upregulate GPX4 expression and enhance ferroptosis resistance in osteosarcoma.Functional & integrative genomics · 2026Article
- Development and validation of a deep learning model to predict heparin response in critically ill patients with deep vein thrombosis receiving continuous intravenous heparin infusion.Blood science (Baltimore, Md.) · 2026Article
- Review
- Cross-site oral and esophageal microbiome signatures define diagnostic patterns and reveal mechanistic drivers of ESCC.iScience · 2026Article
- Maternal and infant gut microbiome.iMeta · 2026Review
- Lung microbiome predictors of epigenetic aging and potential associations with smoking and electronic cigarette use.Clinical epigenetics · 2026Article
- An adaptive weight self-distillation deep learning framework for phenotype prediction from longitudinal gut microbiome data.BMC microbiology · 2026Article
- GMrepo v3: a curated human gut microbiome database with expanded disease coverage and enhanced cross-dataset biomarker analysis.Nucleic acids research · 2026Article
- Early-life microbiome trajectories as biomarkers to predict health outcomes.Microbiome research reports · 2026Review
- An approach for diagnosis of diarrhea in neonatal piglets based on the core gut microbiota and machine learning.Frontiers in microbiology · 2026Article
- Reframing precision nutrition in irritable bowel syndrome: a mechanism-informed conceptual framework for responder prediction and clinical translation.Frontiers in immunology · 2026Review
- Identification of disease ameliorated metabolite candidates from Gut Microbes and their interacting targets based on a novel estimating function.Frontiers in microbiology · 2026Article
- Oral microbiome dysbiosis in autism spectrum disorder: the oral-gut-brain axis and future perspectives: a narrative review.Frontiers in microbiology · 2026Review
- Bioinformatics and experimental analysis identify CHN2 and MEF2C as diagnostic biomarkers for tuberculosis.American journal of translational research · 2026Article
- Transcriptomics-based analysis of muscle injury mechanism mediated by inflammatory imbalance in the Duchenne muscular dystrophy muscle microenvironment.Frontiers in neurology · 2026Article
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- Interactions between upper respiratory microbiota and psychological distress in pulmonary nodule patients: a retrospective nested case-control study based on machine learning and exploratory mediation analysis.International journal of surgery (London, England) · 2025Article
- Applications of Genome-wide Screening of CRISPR/Cas9 Knockout Library in Identifying Novel Molecular Targets for Xenobiotics.Environment & health (Washington, D.C.) · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The human gut microbiome, crucial in various diseases, can be utilized to develop diagnostic models through machine learning (ML). The specific tools and parameters used in model construction such as data preprocessing, batch effect removal and modeling algorithms can impact model performance and generalizability. To establish an generally applicable workflow, we divided the ML process into three above-mentioned steps and optimized each sequentially using 83 gut microbiome cohorts across 20 diseases. We tested a total of 156 tool-parameter-algorithm combinations and benchmarked them according to internal- and external- AUCs. At the data preprocessing step, we identified four data preprocessing methods that performed well for regression-type algorithms and one method that excelled for non-regression-type algorithms. At the batch effect removal step, we identified the "ComBat" function from the
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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.