Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
ISSN (print) / ISBN
1367-4803
Zeitschrift
Bioinformatics
Quellenangaben
Band: 39,
Heft: 11,
Artikelnummer: btad711
Verlag
Oxford University Press
Verlagsort
Oxford
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Computational Biology (ICB)
Förderungen
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