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pyPESTO: A modular and scalable tool for parameter estimation for dynamic models.
Bioinformatics 39:btad711 (2023)
SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: 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).
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Publication type
Article: Journal article
Document type
Scientific Article
ISSN (print) / ISBN
1367-4803
Journal
Bioinformatics
Quellenangaben
Volume: 39,
Issue: 11,
Article Number: btad711
Publisher
Oxford University Press
Publishing Place
Oxford
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
Grants
Joachim Herz Foundation
German Federal Ministry of Education and Research
Human Frontier Science Program
German Research Foundation
German Federal Ministry of Education and Research
Human Frontier Science Program
German Research Foundation