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VAMBN
Variational Autoencoders Modular Bayesian Networks (VAMBN) is a modelling approach to generate high quality, longitudinal & heterogeneous synthetic patient level data.
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Description
Variational Autoencoders Modular Bayesian Networks (VAMBN)
This repository hosts the code for VAMBN 2.0. Unlike its predecessor, this version features a PyTorch-based HI-VAE (see Nazabal et al.'s paper) and employs Snakemake to manage the workflow of Python and R scripts.
Documentation
For detailed information, visit the documentation page.
License
This software is licensed under the GNU General Public License (GPL) for non-commercial use. For commercial use, please contact Holger Fröhlich to obtain a commercial license.
Development & Bug tracking
This project is under active development. Expect changes and potential bugs. Please open an issue for any problems you encounter.
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Reference papers
- 1.Author(s): Lisa Kühnel, Julian Schneider, Ines Perrar, Tim Adams, Sobhan Moazemi, Fabian Prasser, Ute Nöthlings, Holger Fröhlich, Juliane FluckPublished in Scientific Reports by Springer Science and Business Media LLC in 202410.1038/s41598-024-62102-2
- 2.Author(s): Meemansa Sood, Robbert Harms, Maximilian Bügler, Ioannis Tarnanas, Neva Coello, Holger FröhlichPublished in Alzheimer's & Dementia by Wiley in 202110.1002/alz.053574
- 3.Author(s): Luise Gootjes-Dreesbach, Meemansa Sood, Akrishta Sahay, Martin Hofmann-Apitius, Holger FröhlichPublished in Frontiers in Big Data by Frontiers Media SA in 202010.3389/fdata.2020.00016
Contributors
Contact person
HF
Holger Fröhlich
Fraunhofer Institute for Scientific Computing and Intelligent Algorithms
0000-0002-5328-1243
Mail HolgerML
Manuel Lentzen
Author
Fraunhofer Institute for Scientific Computing and Intelligent Algorithms
TA
Tim Adams
Fraunhofer Institute for Scientific Computing and Intelligent Algorithms
HGN
Hwei Geok Ng
HF
Holger Fröhlich
Fraunhofer Institute for Scientific Computing and Intelligent Algorithms
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