ArticleScientific reports2022
Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- The Emerging Role of Gut Microbiota in Inflammatory Skin Diseases: A Systematic Review.Experimental dermatology · 2026Pooled it
- Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures.Allergy · 2026Review
- Artificial intelligence in atopic dermatitis: Current applications and future perspectives.Chinese medical journal · 2026Review
- Artificial intelligence-enabled precision medicine for inflammatory skin diseases.The Journal of investigative dermatology · 2026Review
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- Host-microbe multi-omics and succinotype profiling have prognostic value for future relapse in patients with inflammatory bowel disease.Gut microbes · 2025Article
- Advancements in artificial intelligence for atopic dermatitis: diagnosis, treatment, and patient management.Annals of medicine · 2025Review
- Machine learning-based prediction models for atopic dermatitis diagnosis and evaluation.Fundamental research · 2025Article
- Atopic dermatitis: a comprehensive updated review of this intriguing disease with futuristic insights.Inflammopharmacology · 2025Review
- Multi-Omics Profiling of the 42-Day Infant Gut as a Pilot Predictor of Atopic Dermatitis at One Year: A Birth Cohort Study in China.Journal of inflammation research · 2025Article
- Application and research progress of artificial intelligence in allergic diseases.International journal of medical sciences · 2025Review
- Artificial intelligence in pediatric allergy research.European journal of pediatrics · 2024Review
- The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment.Current allergy and asthma reports · 2024Review
- Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases.Life (Basel, Switzerland) · 2024Review
- The role of the microbiome in allergic dermatitis-related otitis externa: a multi-species comparative review.Frontiers in veterinary science · 2024Review
- Overview of data preprocessing for machine learning applications in human microbiome research.Frontiers in microbiology · 2023Review
- Obesity and the microbiome in atopic dermatitis: Therapeutic implications for PPAR-γ agonists.Frontiers in allergy · 2023Review
- Artificial Intelligence in microbiomes analysis: A review of applications in dermatology.Frontiers in microbiology · 2023Review
- bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease.Frontiers in neuroinformatics · 2022Article
- Emerging Uses of Artificial Intelligence in Chronic Dermatologic Disease: A Scoping Review.Journal of cutaneous medicine and surgeryArticle
Corrections and comments
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Authors and funding
10 authors at 7 institutions in 3 countries.
Funding
Abstract
Atopic dermatitis (AD) is a common skin disease in childhood whose diagnosis requires expertise in dermatology. Recent studies have indicated that host genes-microbial interactions in the gut contribute to human diseases including AD. We sought to develop an accurate and automated pipeline for AD diagnosis based on transcriptome and microbiota data. Using these data of 161 subjects including AD patients and healthy controls, we trained a machine learning classifier to predict the risk of AD. We found that the classifier could accurately differentiate subjects with AD and healthy individuals based on the omics data with an average F1-score of 0.84. With this classifier, we also identified a set of 35 genes and 50 microbiota features that are predictive for AD. Among the selected features, we discovered at least three genes and three microorganisms directly or indirectly associated with AD. Although further replications in other cohorts are needed, our findings suggest that these genes and microbiota features may provide novel biological insights and may be developed into useful biomarkers of AD prediction.
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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.