Machine-learning-driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis.
Adult
Algorithms
Allergens
Biomarkers
/ metabolism
Databases, Genetic
Dermatitis, Allergic Contact
/ diagnosis
Dermatitis, Irritant
/ diagnosis
Diagnosis, Differential
Female
Gene Expression Regulation
Gene Regulatory Networks
Humans
Irritants
Leukocytes
/ metabolism
Machine Learning
Male
Patch Tests
Reproducibility of Results
Severity of Illness Index
Skin
/ pathology
Transcriptome
/ genetics
allergic contact dermatitis
artificial intelligence
biomarker
irritant contact dermatitis
machine learning
Journal
Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876
Informations de publication
Date de publication:
29 12 2020
29 12 2020
Historique:
pubmed:
16
12
2020
medline:
11
2
2021
entrez:
15
12
2020
Statut:
ppublish
Résumé
Contact dermatitis tremendously impacts the quality of life of suffering patients. Currently, diagnostic regimes rely on allergy testing, exposure specification, and follow-up visits; however, distinguishing the clinical phenotype of irritant and allergic contact dermatitis remains challenging. Employing integrative transcriptomic analysis and machine-learning approaches, we aimed to decipher disease-related signature genes to find suitable sets of biomarkers. A total of 89 positive patch-test reaction biopsies against four contact allergens and two irritants were analyzed via microarray. Coexpression network analysis and Random Forest classification were used to discover potential biomarkers and selected biomarker models were validated in an independent patient group. Differential gene-expression analysis identified major gene-expression changes depending on the stimulus. Random Forest classification identified
Identifiants
pubmed: 33318199
pii: 2009192117
doi: 10.1073/pnas.2009192117
pmc: PMC7776829
doi:
Substances chimiques
Allergens
0
Biomarkers
0
Irritants
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
33474-33485Informations de copyright
Copyright © 2020 the Author(s). Published by PNAS.
Déclaration de conflit d'intérêts
The authors declare no competing interest.
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