Machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis

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. Empl...

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Veröffentlicht in:	Proceedings of the National Academy of Sciences - PNAS 2020-12, Vol.117 (52), p.33474-33485
Hauptverfasser:	Fortino, Vittorio, Wisgrill, Lukas, Werner, Paulina, Suomela, Sari, Linder, Nina, Jalonen, Erja, Suomalainen, Alina, Marwah, Veer, Kero, Mia, Pesonen, Maria, Lundin, Johan, Lauerma, Antti, Aalto-Korte, Kristiina, Greco, Dario, Alenius, Harri, Fyhrquist, Nanna
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Algorithms Allergens allergic contact dermatitis artificial intelligence Biological Sciences biomarker Biomarkers - metabolism Databases, Genetic Dermatitis, Allergic Contact - diagnosis Dermatitis, Allergic Contact - genetics Dermatitis, Irritant - diagnosis Dermatitis, Irritant - genetics Diagnosis, Differential Female Gene Expression Regulation Gene Regulatory Networks Humans irritant contact dermatitis Irritants Leukocytes - metabolism Machine Learning Male Medicin och hälsovetenskap Patch Tests Reproducibility of Results Severity of Illness Index Skin - pathology Transcriptome - genetics
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