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Open AccessArticle

Prediction Power on Cardiovascular Disease of Neuroimmune Guidance Cues Expression by Peripheral Blood Monocytes Determined by Machine-Learning Methods

1
Einthoven Laboratory for Vascular and Regenerative Medicine, Department of Internal Medicine, Leiden University Medical Center, Albinusdreef, 22333 ZA Leiden, The Netherlands
2
Department of Cardiology, University Medical Center Utrecht, Heidelberglaan, 1003584 CX Utrecht, The Netherlands
3
Department of Cardiology, Leiden University Medical Center, Albinusdreef, 22333 ZA Leiden, The Netherlands
4
Department of Cardiology, Catharina Hospital, Michelangelolaan, 25623 EJ Eindhoven, The Netherlands
5
Department of Cardiology, Maastricht University Medical Center, P. Debyelaan, 256202 AZ Maastricht, The Netherlands
6
Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), University of Maastricht, Universiteitssingel, 506229 ER Maastricht, The Netherlands
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(17), 6364; https://doi.org/10.3390/ijms21176364
Received: 5 August 2020 / Revised: 25 August 2020 / Accepted: 26 August 2020 / Published: 2 September 2020
Atherosclerosis is the underlying pathology in a major part of cardiovascular disease, the leading cause of mortality in developed countries. The infiltration of monocytes into the vessel walls of large arteries is a key denominator of atherogenesis, making monocytes accountable for the development of atherosclerosis. With the development of high-throughput transcriptome profiling platforms and cytometric methods for circulating cells, it is now feasible to study in-depth the predicted functional change of circulating monocytes reflected by changes of gene expression in certain pathways and correlate the changes to disease outcome. Neuroimmune guidance cues comprise a group of circulating- and cell membrane-associated signaling proteins that are progressively involved in monocyte functions. Here, we employed the CIRCULATING CELLS study cohort to classify cardiovascular disease patients and healthy individuals in relation to their expression of neuroimmune guidance cues in circulating monocytes. To cope with the complexity of human datasets featured by noisy data, nonlinearity and multidimensionality, we assessed various machine-learning methods. Of these, the linear discriminant analysis, Naïve Bayesian model and stochastic gradient boost model yielded perfect or near-perfect sensibility and specificity and revealed that expression levels of the neuroimmune guidance cues SEMA6B, SEMA6D and EPHA2 in circulating monocytes were of predictive values for cardiovascular disease outcome. View Full-Text
Keywords: cardiovascular diseases; monocytes; machine-learning methods; neuroimmune guidance cues cardiovascular diseases; monocytes; machine-learning methods; neuroimmune guidance cues
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MDPI and ACS Style

Zhang, H.; Bredewold, E.O.W.; Vreeken, D.; Duijs, J..M.G.J.; de Boer, H.C.; Kraaijeveld, A.O.; Jukema, J.W.; Pijls, N.H.; Waltenberger, J.; Biessen, E.A.L.; van der Veer, E.P.; van Zonneveld, A.J.; van Gils, J.M. Prediction Power on Cardiovascular Disease of Neuroimmune Guidance Cues Expression by Peripheral Blood Monocytes Determined by Machine-Learning Methods. Int. J. Mol. Sci. 2020, 21, 6364.

AMA Style

Zhang H, Bredewold EOW, Vreeken D, Duijs JMGJ, de Boer HC, Kraaijeveld AO, Jukema JW, Pijls NH, Waltenberger J, Biessen EAL, van der Veer EP, van Zonneveld AJ, van Gils JM. Prediction Power on Cardiovascular Disease of Neuroimmune Guidance Cues Expression by Peripheral Blood Monocytes Determined by Machine-Learning Methods. International Journal of Molecular Sciences. 2020; 21(17):6364.

Chicago/Turabian Style

Zhang, Huayu; Bredewold, Edwin O.W.; Vreeken, Dianne; Duijs, Jacques. M.G.J.; de Boer, Hetty C.; Kraaijeveld, Adriaan O.; Jukema, J. W.; Pijls, Nico H.; Waltenberger, Johannes; Biessen, Erik A.L.; van der Veer, Eric P.; van Zonneveld, Anton J.; van Gils, Janine M. 2020. "Prediction Power on Cardiovascular Disease of Neuroimmune Guidance Cues Expression by Peripheral Blood Monocytes Determined by Machine-Learning Methods" Int. J. Mol. Sci. 21, no. 17: 6364.

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