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A Novel Signature for Distinguishing Non-lesional from Lesional Skin of Atopic Dermatitis Based on a Machine Learning Approach

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Artificial Intelligence Applications and Innovations (AIAI 2024)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 711))

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Abstract

Atopic dermatitis is a common inflammatory skin disease, characterized by great heterogeneity and complexity. Its underlying causes are not yet fully understood. As a result, current therapies do not always lead to satisfactory outcomes. Very few studies have addressed the potential use of transcriptomic data and machine learning algorithms in atopic dermatitis. In this paper, we present and detail the use of machine learning models over omics data for identifying potential biomarkers to use for distinguishing non-lesional from lesional skin samples in patients with atopic dermatitis. Particularly, we identified an optimal signature that includes eight genes – FUT3, STRIP2, SMPD3, ZNF285, BTC, SUSD2, HSD11B1 and FABP7 – and obtained an AUC of 0.839 and an accuracy of 86.42%. We performed some functional analyses and concluded that some potential biomarkers interfere with the same molecular mechanisms and may be involved in atopic dermatitis. We expected to provide new insights for a deeper comprehension of the mechanisms behind the manifestation of the disease.

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Notes

  1. 1.

    Https://www.ncbi.nlm.nih.gov/geo/

  2. 2.

    Https://maayanlab.cloud/enrichr/

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Acknowledgements

This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, and the PhD grant: 2022.12728.BD.

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Authors and Affiliations

  1. Algoritmi R&D Centre/LASI, University of Minho, Campus of Gualtar, 4710-057, Braga, Portugal

    Ana Duarte & Orlando Belo

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  1. Ana Duarte
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  2. Orlando Belo
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Correspondence to Ana Duarte .

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Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. University of Abertay, Dundee, UK

    John Macintyre

  4. Ionian University, Corfu, Greece

    Markos Avlonitis

  5. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

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Duarte, A., Belo, O. (2024). A Novel Signature for Distinguishing Non-lesional from Lesional Skin of Atopic Dermatitis Based on a Machine Learning Approach. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Avlonitis, M., Papaleonidas, A. (eds) Artificial Intelligence Applications and Innovations. AIAI 2024. IFIP Advances in Information and Communication Technology, vol 711. Springer, Cham. https://doi.org/10.1007/978-3-031-63211-2_1

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  • DOI: https://doi.org/10.1007/978-3-031-63211-2_1

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