pyPESTO: a modular and scalable tool for parameter estimation for dynamic models.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
01 Nov 2023
01 Nov 2023
Historique:
received:
03
05
2023
revised:
02
10
2023
accepted:
22
11
2023
medline:
4
12
2023
pubmed:
23
11
2023
entrez:
23
11
2023
Statut:
ppublish
Résumé
Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large and complex systems. pyPESTO is a modular framework for systematic parameter estimation, with scalable algorithms for optimization and uncertainty quantification. While tailored to ordinary differential equation problems, pyPESTO is broadly applicable to black-box parameter estimation problems. Besides own implementations, it provides a unified interface to various popular simulation and inference methods. pyPESTO is implemented in Python, open-source under a 3-Clause BSD license. Code and documentation are available on GitHub (https://github.com/icb-dcm/pypesto).
Identifiants
pubmed: 37995297
pii: 7443974
doi: 10.1093/bioinformatics/btad711
pmc: PMC10689677
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : German Research Foundation
Organisme : Germany's Excellence Strategy
ID : EXC 2047/1 390685813
Organisme : Human Frontier Science Program
Organisme : German Federal Ministry of Education and Research
Organisme : Joachim Herz Foundation
Informations de copyright
© The Author(s) 2023. Published by Oxford University Press.
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