Statistical and machine learning methods for analysis of multiplex protein data from a novel proximity extension assay in patients with ST-elevation myocardial infarction

Abstract Using data from patients with ST-elevation myocardial infarction (STEMI), we explored how machine learning methods can be used for analysing multiplex protein data obtained from proximity extension assays. Blood samples were obtained from 48 STEMI-patients at admission and after three month...

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Autores principales:	Emil Maag, Archana Kulasingam, Erik Lerkevang Grove, Kamilla Sofie Pedersen, Steen Dalby Kristensen, Anne-Mette Hvas
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/78ceba10a74a4ba4906cb33c355737e9
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spelling	oai:doaj.org-article:78ceba10a74a4ba4906cb33c355737e92021-12-02T18:18:51ZStatistical and machine learning methods for analysis of multiplex protein data from a novel proximity extension assay in patients with ST-elevation myocardial infarction10.1038/s41598-021-93162-32045-2322https://doaj.org/article/78ceba10a74a4ba4906cb33c355737e92021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93162-3https://doaj.org/toc/2045-2322Abstract Using data from patients with ST-elevation myocardial infarction (STEMI), we explored how machine learning methods can be used for analysing multiplex protein data obtained from proximity extension assays. Blood samples were obtained from 48 STEMI-patients at admission and after three months. A subset of patients also had blood samples obtained at four and 12 h after admission. Multiplex protein data were obtained using a proximity extension assay. A random forest model was used to assess the predictive power and importance of biomarkers to distinguish between the acute and the stable phase. The similarity of response profiles was investigated using K-means clustering. Out of 92 proteins, 26 proteins were found to significantly distinguish the acute and the stable phase following STEMI. The five proteins tissue factor pathway inhibitor, azurocidin, spondin-1, myeloperoxidase and myoglobin were found to be highly important for differentiating between the acute and the stable phase. Four of these proteins shared response profiles over the four time-points. Machine learning methods can be used to identify and assess novel predictive biomarkers as showcased in the present study population of patients with STEMI.Emil MaagArchana KulasingamErik Lerkevang GroveKamilla Sofie PedersenSteen Dalby KristensenAnne-Mette HvasNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Emil Maag Archana Kulasingam Erik Lerkevang Grove Kamilla Sofie Pedersen Steen Dalby Kristensen Anne-Mette Hvas Statistical and machine learning methods for analysis of multiplex protein data from a novel proximity extension assay in patients with ST-elevation myocardial infarction
description	Abstract Using data from patients with ST-elevation myocardial infarction (STEMI), we explored how machine learning methods can be used for analysing multiplex protein data obtained from proximity extension assays. Blood samples were obtained from 48 STEMI-patients at admission and after three months. A subset of patients also had blood samples obtained at four and 12 h after admission. Multiplex protein data were obtained using a proximity extension assay. A random forest model was used to assess the predictive power and importance of biomarkers to distinguish between the acute and the stable phase. The similarity of response profiles was investigated using K-means clustering. Out of 92 proteins, 26 proteins were found to significantly distinguish the acute and the stable phase following STEMI. The five proteins tissue factor pathway inhibitor, azurocidin, spondin-1, myeloperoxidase and myoglobin were found to be highly important for differentiating between the acute and the stable phase. Four of these proteins shared response profiles over the four time-points. Machine learning methods can be used to identify and assess novel predictive biomarkers as showcased in the present study population of patients with STEMI.
format	article
author	Emil Maag Archana Kulasingam Erik Lerkevang Grove Kamilla Sofie Pedersen Steen Dalby Kristensen Anne-Mette Hvas
author_facet	Emil Maag Archana Kulasingam Erik Lerkevang Grove Kamilla Sofie Pedersen Steen Dalby Kristensen Anne-Mette Hvas
author_sort	Emil Maag
title	Statistical and machine learning methods for analysis of multiplex protein data from a novel proximity extension assay in patients with ST-elevation myocardial infarction
title_short	Statistical and machine learning methods for analysis of multiplex protein data from a novel proximity extension assay in patients with ST-elevation myocardial infarction
title_full	Statistical and machine learning methods for analysis of multiplex protein data from a novel proximity extension assay in patients with ST-elevation myocardial infarction
title_fullStr	Statistical and machine learning methods for analysis of multiplex protein data from a novel proximity extension assay in patients with ST-elevation myocardial infarction
title_full_unstemmed	Statistical and machine learning methods for analysis of multiplex protein data from a novel proximity extension assay in patients with ST-elevation myocardial infarction
title_sort	statistical and machine learning methods for analysis of multiplex protein data from a novel proximity extension assay in patients with st-elevation myocardial infarction
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/78ceba10a74a4ba4906cb33c355737e9
work_keys_str_mv	AT emilmaag statisticalandmachinelearningmethodsforanalysisofmultiplexproteindatafromanovelproximityextensionassayinpatientswithstelevationmyocardialinfarction AT archanakulasingam statisticalandmachinelearningmethodsforanalysisofmultiplexproteindatafromanovelproximityextensionassayinpatientswithstelevationmyocardialinfarction AT eriklerkevanggrove statisticalandmachinelearningmethodsforanalysisofmultiplexproteindatafromanovelproximityextensionassayinpatientswithstelevationmyocardialinfarction AT kamillasofiepedersen statisticalandmachinelearningmethodsforanalysisofmultiplexproteindatafromanovelproximityextensionassayinpatientswithstelevationmyocardialinfarction AT steendalbykristensen statisticalandmachinelearningmethodsforanalysisofmultiplexproteindatafromanovelproximityextensionassayinpatientswithstelevationmyocardialinfarction AT annemettehvas statisticalandmachinelearningmethodsforanalysisofmultiplexproteindatafromanovelproximityextensionassayinpatientswithstelevationmyocardialinfarction
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Statistical and machine learning methods for analysis of multiplex protein data from a novel proximity extension assay in patients with ST-elevation myocardial infarction

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