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dc.contributor.authorStorås, Andrea
dc.contributor.authorFineide, Fredrik
dc.contributor.authorMagnø, Morten Schjerven
dc.contributor.authorThiede, Bernd
dc.contributor.authorChen, Xiangjun
dc.contributor.authorStrumke, Inga
dc.contributor.authorHalvorsen, Pål
dc.contributor.authorGaltung, Hilde
dc.contributor.authorJensen, Janicke Cecilie Liaaen
dc.contributor.authorUtheim, Tor Paaske
dc.contributor.authorRiegler, Michael Alexander
dc.date.accessioned2024-01-04T14:15:52Z
dc.date.available2024-01-04T14:15:52Z
dc.date.issued2023-12-22
dc.description.abstractMeibomian 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.en_US
dc.identifier.citationStorås, Fineide, Magnø, Thiede, Chen, Strumke, Halvorsen, Galtung, Jensen, Utheim, Riegler. Using machine learning model explanations to identify proteins related to severity of meibomian gland dysfunction. Scientific Reports. 2023en_US
dc.identifier.cristinIDFRIDAID 2217178
dc.identifier.doi10.1038/s41598-023-50342-7
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/10037/32327
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.relation.journalScientific Reports
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleUsing machine learning model explanations to identify proteins related to severity of meibomian gland dysfunctionen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)