Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning.

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.