Using machine learning model explanations to identify proteins related to severity of meibomian gland dysfunction

Meibomian gland dysfunction is the most common cause of dry eye disease and leads to significantly reduced quality of life and social burdens. Because meibomian gland dysfunction results in impaired function of the tear film lipid layer, studying the expression of tear proteins might increase the un...

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Vydáno v:Scientific reports Ročník 13; číslo 1; s. 22946 - 13
Hlavní autoři: Storås, Andrea M., Fineide, Fredrik, Magnø, Morten, Thiede, Bernd, Chen, Xiangjun, Strümke, Inga, Halvorsen, Pål, Galtung, Hilde, Jensen, Janicke L., Utheim, Tor P., Riegler, Michael A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 22.12.2023
Nature Publishing Group
Nature Portfolio
Témata:
631/114/1305
631/114/663
639/705/117
692/308/53
692/53/2421
692/699/3161/3163
Eye diseases
Glands
Humanities and Social Sciences
Learning algorithms
Machine learning
multidisciplinary
Proteins
Proteomics
Quality of life
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
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Shrnutí:Meibomian gland dysfunction is the most common cause of dry eye disease and leads to significantly reduced quality of life and social burdens. Because meibomian gland dysfunction results in impaired function of the tear film lipid layer, studying the expression of tear proteins might increase the understanding of the etiology of the condition. Machine learning is able to detect patterns in complex data. This study applied machine learning to classify levels of meibomian gland dysfunction from tear proteins. The aim was to investigate proteomic changes between groups with different severity levels of meibomian gland dysfunction, as opposed to only separating patients with and without this condition. An established feature importance method was used to identify the most important proteins for the resulting models. Moreover, a new method that can take the uncertainty of the models into account when creating explanations was proposed. By examining the identified proteins, potential biomarkers for meibomian gland dysfunction were discovered. The overall findings are largely confirmatory, indicating that the presented machine learning approaches are promising for detecting clinically relevant proteins. While this study provides valuable insights into proteomic changes associated with varying severity levels of meibomian gland dysfunction, it should be noted that it was conducted without a healthy control group. Future research could benefit from including such a comparison to further validate and extend the findings presented here.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-50342-7