Machine Learning Unravels Sex-Specific Biomarkers for Atopic Dermatitis

Ana Duarte, Orlando Belo

2024

Abstract

The prevalence of atopic dermatitis is significantly higher in women than in men. Understanding the differences in the manifestation of the disease between males and females can contribute to more tailored and effective treatments. Our goal in this paper was to discover sex-specific biomarkers that can be used to differentiate between lesional and non-lesional skin in atopic dermatitis patients. Using transcriptomic datasets, we first identified the genes with the highest expression difference. Subsequently, several feature selection methods and machine learning models were employed to select the most relevant genes and identify potential candidates for sex-specific biomarkers. Based on backward feature elimination, we obtained a male-specific signature with 11 genes and a female-specific signature with 10 genes. Both candidate signatures were properly evaluated by an ensemble classifier using an independent test. The obtained AUC and accuracy values for the male signature were 0.839 and 0.7222, respectively, and 0.65 and 0.6667 for the female signature. Finally, we tested the male signature on female data and the female signature on male data. As expected, the analysed metrics decreased considerably in these scenarios. These results suggest that we have identified two promising sex-specific gene signatures, and support that sex affects the ability to distinguish lesions in patients with eczema.

Paper Citation

in Harvard Style

Duarte A. and Belo O. (2024). Machine Learning Unravels Sex-Specific Biomarkers for Atopic Dermatitis. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-716-0, SciTePress, pages 27-35. DOI: 10.5220/0012890700003838

in Bibtex Style

@conference{kdir24,
author={Ana Duarte and Orlando Belo},
title={Machine Learning Unravels Sex-Specific Biomarkers for Atopic Dermatitis},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2024},
pages={27-35},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012890700003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Machine Learning Unravels Sex-Specific Biomarkers for Atopic Dermatitis
SN - 978-989-758-716-0
AU - Duarte A.
AU - Belo O.
PY - 2024
SP - 27
EP - 35
DO - 10.5220/0012890700003838
PB - SciTePress