Issue |
ESAIM: ProcS
Volume 73, 2023
CEMRACS 2021 - Data Assimilation and Reduced Modeling for High Dimensional Problems
|
|
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Page(s) | 2 - 27 | |
DOI | https://doi.org/10.1051/proc/202373002 | |
Published online | 30 August 2023 |
Comparison of statistical, machine learning, and mathematical modelling methods to investigate the effect of ageing on dog’s cardiovascular system
1
General Pharmacology Group, Department of Drug Discovery Support, Boehringer Ingelheim Pharma GmbH & Co KG, Biberach an der Riss, Germany
2
Sorbonne Université and COMMEDIA team, Inria, Paris, France
3
NOTOCORD, an Instem company, Le Pecq, France
4
Laboratory of Physiopharmacology, University of Antwerp, Antwerp, Belgium
The aim of this work is to provide a preliminary comparison of different classes of methods to automatically detect the effect of ageing from in vivo data. The application which motivated this work is related to safety pharmacology, whose major goal is to determine, in a pre-clinical phase, whether a drug is potentially dangerous for the health. In particular, we are going to compare statistical, machine learning and mathematical modelling methods.
Résumé
L’objectif de ce travail est de fournir une comparaison préliminaire entre différents classes de méthodes pour la détection automatique de l’effet du viellissement sur le système cardiovasculaire, en exploitant des données in vivo. L’application qui a motivé ce travail est liée à la pharmacologie de sécurité, qui vise à établir, dans une phase pre-clinique, si un médicament est potentiellement dangereux pour la santé. En particulier, nous allons comparer des approches statistiques, d’apprentissage statistique et de modélisation mathématique.
© EDP Sciences, SMAI 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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