The doi 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

van Lent, Paul; Planken, L.R.(Léon); Bunkova, Olga; Thomas Abeel; Schmitz, Joep (2024): Results for Neural Ordinary Differential Equations Inspired Parameterization of Kinetic Models. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/3662eca5-7077-4ca3-8f66-d051e2c79cbe.v1

Dataset

• Other Biological Sciences
• Biological Sciences

bioinformatics, chemical kinetics, kinetic-models, machine learning, systems-biology

CC BY 4.0

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by Paul van Lent , L.R.(Léon) Planken , Olga Bunkova , Thomas Abeel , Joep Schmitz

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

• 2024-12-20 first online, published, posted

4TU.ResearchData

csv

• https://github.com/AbeelLab/jaxkineticmodel/tree/main

• AI4b.io [more info...]

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