ArticleProceedings of the National Academy of Sciences of the United States of America2020
Machine-learning-driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Integrated Clinical Trial and Molecular Profiling Reveals Immune Drivers of Chronic Hand Eczema.Allergy · 2026Trial
- Metabolomics and machine learning approaches for diagnostic and prognostic biomarkers screening in sepsis.BMC anesthesiology · 2023Trial
- Distinct Skin Penetration and Immune Responses to Ionic and Nanoparticulate Cobalt in Allergic Contact Dermatitis.Allergy · 2026Article
- Occupational Contact Dermatitis in the Post-COVID Era: From Barrier Dysfunction and Microbiome Dysbiosis to Prevention and Precision Management.Journal of clinical medicine · 2026Review
- Artificial intelligence-enabled precision medicine for inflammatory skin diseases.The Journal of investigative dermatology · 2026Review
- Advances in the application of computational toxicology in cosmetic safety assessment.Archives of toxicology · 2026Review
- Characteristics of T-Cells Expressing IL-37 and Its Receptors in Inflammatory Bowel Disease.International journal of molecular sciences · 2026Article
- The role of artificial intelligence in burn assessment, complication diagnosis, and outcome prediction: a narrative review.Burns & trauma · 2026Review
- Skin-Integrated Soft Wearable XR Interfaces for Seamless and Realistic User Experience.Chemical reviews · 2025Review
- Article
- Artificial intelligence in allergy practice: Digital transformation and the future of clinical care.The World Allergy Organization journal · 2025Review
- Weak Sensitizers May Be Associated with CD80 Polymorphisms: Implications for Systemic Contact Dermatitis.JID innovations : skin science from molecules to population health · 2025Article
- Machine Learning Analysis of the Orbitofrontal Cortex Transcriptome of Human Opioid Users Identifies Shisa7 as a Translational Target Relevant for Heroin Seeking Leveraging a Male Rat Model.Biological psychiatry · 2025Article
- Artificial Intelligence in Patch Testing: Comprehensive Review of Current Applications and Future Prospects in Dermatology.JMIR dermatology · 2025Review
- Review
- Scratching the surface: biomarkers and neurobiomarkers for improved allergic contact dermatitis management.Frontiers in allergy · 2025Review
- Dual-stage optimizer for systematic overestimation adjustment applied to multi-objective genetic algorithms for biomarker selection.Briefings in bioinformatics · 2024Article
- Co-Expression Network and Machine Learning Analysis of Transcriptomics Data Identifies Distinct Gene Signatures and Pathways in Lesional and Non-Lesional Atopic Dermatitis.Journal of personalized medicine · 2024Article
- Itch and Pain Behaviors in Irritant Contact Dermatitis Produced by Sodium Lauryl Sulfate in Mice.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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