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Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning
Open accessAbstract
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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A supervised ML classifier for AD based on gut transcriptomics and microbiome identified 35 predictive genes and 50 microbiota features that help distinguish patients with AD from healthy controls.
“For instance, a supervised ML classifier for AD based on gut transcriptomics and microbiome identified 35 predictive genes (including GRP1, CCL22, TTC27) and 50 microbiota features (such as Akkermanisia spp, Verrucomicrobia spp, Propionibacterium spp) that help distinguish patients with AD from healthy controls (Jiang et al, 2022).”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Jiang et al, 2022
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Metadata
- Authors
- Jiang, Z., Li, J., Kong, N., Kim, J.-H., Kim, B.-S., Lee, M.-J., Park, Y. M., Lee, S.-Y., Hong, S.-J., & Sul, J. H.
- Affiliations
- Tsinghua University (CN)
- University of California, Los Angeles (US)
- Korea Advanced Institute of Science and Technology (KR)
- University of Ulsan (KR)
- Hallym University (KR)
- Asan Medical Center (KR)
- Corresponding author
- Hong, Soo-Jong
- Sul, Jae Hoon
- ORCID
- Venue
- Scientific Reports
- Published
- 2022-01-07
- Volume
- 12
- Issue
- 1
- Pages
- 290-290
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC8741793
- OpenAlex
- W4205621089
- Cited by
- 54
- References
- 60
- Citations by year
- 2026: 16 · 2025: 15 · 2024: 10 · 2023: 10 · 2022: 3
- FWCI
- 9.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation of Korea (2017M3A9F3043834)
- National Institute of Environmental Health Sciences (K01 ES028064)
- National Science Foundation (#1705197)
- National Institute of Neurological Disorders and Stroke (R01 NS102371)
- Topics
- Health Sciences › Medicine › Dermatology › Dermatology and Skin Diseases
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Asthma and respiratory diseases
- Health Sciences › Medicine › Dermatology › Contact Dermatitis and Allergies
- Keywords
- atopic dermatitis, automated diagnosis, transcriptome, gut microbiota, host-microbe interactions, machine learning classifiers, biomarkers, gene selection, pediatric skin disease
- MeSH terms
- Gastrointestinal Microbiome, Supervised Machine Learning, Colon, Dermatitis, Atopic, Diagnosis, Computer-Assisted, Female, Humans, Infant, Male, Predictive Value of Tests, Case-Control Studies, Gene Expression Profiling, Host-Pathogen Interactions, Transcriptome, Dysbiosis
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