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Bayesian modelling of time series data (BayModTS)-a FAIR workflow to process sparse and highly variable data.

Sebastian HöpflMohamed AlbadryUta DahmenKarl-Heinz HerrmannEva Marie KindlerMatthias KönigJürgen Rainer ReichenbachHans-Michael TautenhahnWeiwei WeiWan-Ting ZhaoNicole Erika Radde
Published in: Bioinformatics (Oxford, England) (2024)
The BayModTS codebase is available on GitHub at https://github.com/Systems-Theory-in-Systems-Biology/BayModTS. The repository contains a Python script for the executable BayModTS workflow and a widely applicable SBML (systems biology markup language) model for retarded transient functions. In addition, all examples from the paper are included in the repository. Data and code of the application examples are stored on DaRUS: https://doi.org/10.18419/darus-3876. The raw MRI ROI voxel data were uploaded to DaRUS: https://doi.org/10.18419/darus-3878. The steatosis metabolite data are published on FairdomHub: 10.15490/fairdomhub.1.study.1070.1.
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