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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| Vydané v: | Scientific reports Ročník 13; číslo 1; s. 22946 - 13 |
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| Hlavní autori: | , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
Nature Publishing Group UK
22.12.2023
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2045-2322, 2045-2322 |
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| 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. |
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| AbstractList | 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. 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. 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.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. |
| ArticleNumber | 22946 |
| Author | Jensen, Janicke L. Thiede, Bernd Strümke, Inga Storås, Andrea M. Chen, Xiangjun Magnø, Morten Utheim, Tor P. Riegler, Michael A. Fineide, Fredrik Halvorsen, Pål Galtung, Hilde |
| Author_xml | – sequence: 1 givenname: Andrea M. surname: Storås fullname: Storås, Andrea M. email: andrea@simula.no organization: Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Department of Computer Science, OsloMet - Oslo Metropolitan University – sequence: 2 givenname: Fredrik surname: Fineide fullname: Fineide, Fredrik organization: Department of Computer Science, OsloMet - Oslo Metropolitan University, The Norwegian Dry Eye Clinic – sequence: 3 givenname: Morten surname: Magnø fullname: Magnø, Morten organization: Department of Ophthalmology, Sørlandet Hospital Arendal, Department of Plastic and Reconstructive Surgery, Oslo University Hospital – sequence: 4 givenname: Bernd surname: Thiede fullname: Thiede, Bernd organization: Department of Biosciences, University of Oslo – sequence: 5 givenname: Xiangjun surname: Chen fullname: Chen, Xiangjun organization: Department of Ophthalmology, Sørlandet Hospital Arendal, Department of Medical Biochemistry, Oslo University Hospital, Department of Ophthalmology, Vestre Viken Hospital Trust – sequence: 6 givenname: Inga surname: Strümke fullname: Strümke, Inga organization: Department of Computer Science, Norwegian University of Science and Technology – sequence: 7 givenname: Pål surname: Halvorsen fullname: Halvorsen, Pål organization: Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Department of Computer Science, OsloMet - Oslo Metropolitan University – sequence: 8 givenname: Hilde surname: Galtung fullname: Galtung, Hilde organization: Institute of Oral Biology, University of Oslo – sequence: 9 givenname: Janicke L. surname: Jensen fullname: Jensen, Janicke L. organization: Department of Oral Surgery and Oral Medicine, University of Oslo – sequence: 10 givenname: Tor P. surname: Utheim fullname: Utheim, Tor P. organization: Department of Computer Science, OsloMet - Oslo Metropolitan University, The Norwegian Dry Eye Clinic, Department of Ophthalmology, Sørlandet Hospital Arendal, Department of Medical Biochemistry, Oslo University Hospital, Department of Ophthalmology, Oslo University Hospital – sequence: 11 givenname: Michael A. surname: Riegler fullname: Riegler, Michael A. organization: Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Department of Computer Science, OsloMet - Oslo Metropolitan University, Department of Computer Science, UiT The Arctic University of Norway |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38135766$$D View this record in MEDLINE/PubMed |
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| Title | Using machine learning model explanations to identify proteins related to severity of meibomian gland dysfunction |
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