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Results for Neural Ordinary Differential Equations Inspired Parameterization of Kinetic Models

DOI:10.4121/3662eca5-7077-4ca3-8f66-d051e2c79cbe.v2
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/3662eca5-7077-4ca3-8f66-d051e2c79cbe

Datacite citation style

van Lent, Paul; Planken, L.R.(Léon); Bunkova, Olga; Thomas Abeel; Schmitz, Joep (2025): Results for Neural Ordinary Differential Equations Inspired Parameterization of Kinetic Models. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/3662eca5-7077-4ca3-8f66-d051e2c79cbe.v2
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Version 2 - 2025-03-31 (latest)
Version 1 - 2024-12-20

Results from the computational experiments on simulated and experimental time-series data as described in the paper Neural Ordinary Differential Equations Inspired

Parameterization of Kinetic Models. Details on the experiments can be found in the readme of https://github.com/AbeelLab/jaxkineticmodel

History

  • 2024-12-20 first online
  • 2025-03-31 published, posted

Publisher

4TU.ResearchData

Format

csv

Funding

Organizations

AI4b.io
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, The Delft Bioinformatics Lab
DSM-Firmenich

DATA

Files (1)