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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2021
License: CC BY
Data sources: ZENODO
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Supplementary material to *Mini-batch optimization enables training of ODE models on large-scale datasets*

Authors: Stapor Paul; Leonard Schmiester; Christoph Wierling; Simon Merkt; Dilan Pathirana; Bodo Lange; Daniel Weindl; +1 Authors

Supplementary material to *Mini-batch optimization enables training of ODE models on large-scale datasets*

Abstract

This archive contains supplementary material to the revised version of the manuscript Mini-batch optimization enables training of ODE models on large-scale datasets This upload contains: Code for parameter estimation which we used to find our results Code for in-silico knockout study The biological models in SBML/PEtab format The artificial data created and used for a benchmark study The condensed results of the parameter estimation, as hdf5-files Figures of the preprint Code for generation of the figures Multiple readme files

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2151 - 390873048.

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Keywords

Mini-batch optimization, ODE modeling, optimization, parameter estimation, large-scale models, systems biology

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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