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other·ophthalmology, dry eye, biomedical research, research methods, clinical trial·PMC10746717

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

Scientific Reports · 11 authors, 13 centres

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TL;DR

This study used machine learning models and SHAP explanations to identify tear proteins associated with the severity of meibomian gland dysfunction (MGD) in human patients. The central result is that several proteins, including S100-A8, PRP4, and lysozyme C, were identified as important features for classifying MGD levels, with some showing statistically significant differences between severity groups.

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The study analyzed tear film protein quantifications from human patients with meibomian gland dysfunction (MGD) of varying severity. Using machine learning models and SHAP (Shapley Additive Explanations), researchers identified proteins important for classifying MGD levels. Among the top-ranked features, several proteins are known to be altered in dry eye disease (DED), including S100-A8 (upregulated), PRP4 (downregulated), and lysozyme C (downregulated). Statistical analysis showed significantly different levels of S100-A8 between MGD levels 2 and 3 (p < 0.05), with a borderline difference between levels 2 and 4 (p = 0.052). No significant differences were found for PRP4 or lysozyme C between MGD groups. The discussion highlights that the ML models identified patterns beyond single protein alterations, and discusses the potential roles of other top-ranked proteins like prostaglandin reductase 1, psoriasin, serotransferrin, ADH7, and various immunoglobulins in MGD and DED pathogenesis.

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other

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ophthalmology

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AI pipeline

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ORIGINAL PAPER

PMC10746717

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

Scientific Reports

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ABSTRACT

Abstract 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. Subject terms: Machine learning, Protein analysis, Diagnostic markers, Computer science, Biomarkers, Corneal diseases

CITATION EXPORT

Andrea M Storås, Fredrik Fineide, Morten Magnø et al.. Using machine learning model explanations to identify proteins related to severity of meibomian gland dysfunction. Scientific Reports. (2023). https://doi.org/10.1038/s41598-023-50342-7

AUTHORS

11 · 13 CENTRES

Andrea M Storås, Fredrik Fineide, Morten Magnø, Bernd Thiede, Xiangjun Chen, Inga Strümke, Pål Halvorsen, Hilde Galtung, Janicke L Jensen, Tor P Utheim, Michael A Riegler

1Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway
2Department of Computer Science, OsloMet - Oslo Metropolitan University, Oslo, Norway
3The Norwegian Dry Eye Clinic, Oslo, Bergen, Norway
4Department of Ophthalmology, Sørlandet Hospital Arendal, Arendal, Norway
5Department of Plastic and Reconstructive Surgery, Oslo University Hospital, Oslo, Norway
6Department of Biosciences, University of Oslo, Oslo, Norway
7Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway
8Department of Ophthalmology, Vestre Viken Hospital Trust, Drammen, Norway
9Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
10Institute of Oral Biology, University of Oslo, Oslo, Norway
11Department of Oral Surgery and Oral Medicine, University of Oslo, Oslo, Norway
12Department of Ophthalmology, Oslo University Hospital, Oslo, Norway
13Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway