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  4. Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migration
 
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Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migration

Citation Link: https://doi.org/10.15480/882.15325
Publikationstyp
Journal Article
Date Issued
2025-06-08
Sprache
English
Author(s)
Helmholz, Heike  
Resuli, Redon
Tacke, Marius  
Nourisa, Jalil  
Tomforde, Sven  
Aydin, Roland 
Machine Learning in Virtual Materials Design M-EXK5  
Willumeit-Römer, Regine  
Zeller-Plumhoff, Berit  
TORE-DOI
10.15480/882.15325
TORE-URI
https://hdl.handle.net/11420/56025
Journal
Computational and structural biotechnology journal  
Volume
27
Start Page
2711
End Page
2718
Citation
Computational and Structural Biotechnology Journal 27: 2711-2718 (2025)
Publisher DOI
10.1016/j.csbj.2025.06.023
Scopus ID
2-s2.0-105008693791
Publisher
Elsevier
Angiogenesis is one of the first stages in fracture healing and bone repair. Therefore, numerous studies evaluating the effect of Mg as a promising degradable, metallic biomaterial on the proliferation and function of endothelial cells have been performed. However, these studies lack methodological homogeneity and therefore differ in fundamental conclusions. Here, Mg-concentration-, donor- and cell age- dependent relations to primary human umbilical cord vein endothelial cells (HUVEC) proliferation and migration were investigated systematically. The generated data were utilized to develop regression models in order to assess and predict the cell response on Mg exposition in a concentration range of 2–20 mM Mg in cell culture medium extract. A concentration of > 2 mM already induced a detrimental effect in the sensitive primary HUVECs. Molecular data quantifying angiogenesis markers supported this finding. An increased migration capacity has been observed at a concentration of 10 mM Mg. We compared linear regression, random forests, support vector machines, neural networks and large language models for the prediction of HUVEC proliferation for a number of scenarios. Using these machine learning methods, we were able to predict the proliferation of HUVECs for missing Mg concentrations and for missing passages with mean absolute errors below 10 % and as low as 8.5 %, respectively. Due to strong differences between the cell behaviour of different donors, information for missing donors can be predicted with mean absolute errors of 15.7 % only. Support vector machines with linear kernel performed best on the tested data, but large language models also showed promising results.
Subjects
Cell proliferation | HUVEC | Large Language Model | Regression models
DDC Class
610: Medicine, Health
620.11: Engineering Materials
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
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