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ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, additional part 1

Authors: Choudhury, Subham; Moret, Michael; Salvy, Pierre; Weilandt, Daniel; Hatzimanikatis, Vassily; Miskovic, Ljubisa;

Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, additional part 1

Abstract

Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al. The code to use with these data and reproduce the manuscript results is available at https://github.com/EPFL-LCSB/rekindle and https://gitlab.com/EPFL-LCSB/rekindle. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the REKINDLE code repositories for more details. Temporal evolution of perturbations in non-linear ordinary differential equations ode_solutions_physiology1.zip - contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation. The detailed instructions and the main body of the dataset is available here: https://zenodo.org/record/5803120

Keywords

kinetic parameters, E. coli, deep learning, nonlinearity, transfer learning, large-scale and genome-scale kinetic models, metabolism

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popularity
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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influence
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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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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