ArticleFrontiers in immunology2025
Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Early-mid gestation as a critical predictive window for infantile atopic dermatitis: A longitudinal multi-matrix metabolomic study.The World Allergy Organization journal · 2026Article
- Current research landscape and future prospects of in silico modeling approaches for atopic dermatitis.JID innovations : skin science from molecules to population health · 2026Review
- Gut Microbiome Dysbiosis in Atopic Dermatitis: Pathogenic Mechanisms, Gut-Skin Axis Disruption, and Emerging Microbiota-Targeted Therapies.Biomedicines · 2026Review
- Development and validation of an in-hospital cardiogenic shock prediction model for AMI patients based on machine learning.BMC cardiovascular disorders · 2026Article
- Interpretable machine learning for presurgical differentiation of Hürthle cell carcinoma and adenoma: a SHAP-augmented approach.Frontiers in endocrinology · 2026Article
- Early-life microbiome trajectories as biomarkers to predict health outcomes.Microbiome research reports · 2026Review
- Early-Life Allergen Sensitization Phenotypes and Exploratory Machine-Learning Interpretation of School-Age Asthma Risk.Journal of asthma and allergy · 2026Article
- The gut‑skin axis: Emerging insights in understanding and treating skin diseases through gut microbiome modulation (Review).International journal of molecular medicine · 2025Review
- Integrating Machine Learning to Identify Key Microbiota of Gut Community Changes Across Different Stages in Dahe Black Pigs.Microorganisms · 2025Article
- Advancing time-since-interval estimation for clandestine graves: a forensic ecogenomics perspective into burial and translocation timelines using massively parallel sequencing.Frontiers in microbiology · 2025Review
- Machine Learning-based Diagnostic Potential of Bipolar Disorder Using Gut Microbiota Signatures.IET systems biologyArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The "gut-skin axis" has been proposed to play an important role in the development and symptoms of atopic dermatitis. Therefore, we have constructed an interpretable machine learning framework to quantitatively screen key gut flora. Methods: The 16S rRNA dataset, after applying the centered log-ratio transformation, was analyzed using five different machine learning models: random forest, light gradient boosting machine, extreme gradient boosting, support vector machine with radial kernel, and logistic regression. Interpretable machine learning methods, such as SHAP values, were used to identify significant features associated with atopic dermatitis. Results: Random forest performed better than the other "tree" models in the validation partitions. The SHAP global dependency plot indicated that Conclusion: Machine learning models combined with SHAP could be used to quantitatively screen key gut flora in atopic dermatitis patients, providing doctors with an intuitive understanding of 16S rRNA sequencing data to support precision medicine in care and recovery.
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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 Socratic graph.