Published June 14, 2021 | Version v2
Dataset Open

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

Authors/Creators

  • 1. Institute of Computational Biology, Helmholtz Zentrum München, Germany
  • 2. Alacris Theranostics GmbH, Berlin, Germany
  • 3. Universität Bonn, Faculty of Mathematics and Natural Sciences, Germany

Description

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

Notes

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

Files

Files (9.4 GB)

Name Size Download all
md5:ddcf8edcdde1126bdcb97affa3f91c86
9.4 GB Download

Additional details

Funding

European Commission
CanPathPro - Generation of the CanPath prototype - a platform for predictive cancer pathway modeling 686282