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Home > Discriminative Machine Learning Analysis for Skin Microbiome: Observing Biomarkers in Patients with Seborrheic Dermatitis
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Research Articie September 06,2022
Discriminative Machine Learning Analysis for Skin Microbiome: Observing Biomarkers in Patients with Seborrheic Dermatitis
H.E.C. van der Wall 1 ,  R.J. Doll 2 ,  G.J.P. van Westen 3 ,  T. Niemeyer-van der Kolk 4 ,  G. Feiss 5 ,  H. Pinckaers 6 ,  M.B.A. van Doorn 7 ,  T. Nijsten 8 ,  M.G.H. Sanders 9 ,  A.F. Cohen 10 ,  J. Burggraaf 11 ,  R. Rissmann 12 ,  L.M. Pardo 13 hide author's information
Keywords: Microbiome, Artificial intelligence, Machine learning, Seborrheic dermatitis, Data science
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Abstract


In recent years the skin microbiome has taken center stage as drug target and as disease biomarker. Computational analyses of microbiome sequencing data from patients with skin diseases, for example seborrheic dermatitis, can be performed to identify discriminative biomarkers in the microbiome profile. The aim of the present study was twofold, namely to employ machine learning to predict disease from the microbiome dataset, and to identify discriminative biomarkers in the microbiome of patients with seborrheic dermatitis versus healthy controls using machine learning techniques. The population consisted of 97 patients with seborrheic dermatitis and 763 healthy controls. Skin swabs were taken from naso-labial fold (lesional skin: n = 22; non-lesional skin: n = 75, controls: n = 763). Using an extra trees machine learning model, differences between the skin microbiome of patients with seborrheic dermatitis versus healthy controls were characterized. Subsequently, the most important microorganisms for discrimination were determined by feature analysis and SHapley Additive exPlanations (SHAP) values...