3 papers
q-bio.QM2026
PEtab SciML: an exchange format for specifying and training dynamic scientific machine learning models
Sebastian Persson, Branwen Snelling, Maren Philipps +5
Summary: Dynamic scientific machine learning (SciML) models that combine mechanistic ordinary differential equations (ODEs) with machine learning (ML) components have applications…
q-bio.QM2025
PEtab-GUI: A graphical user interface to create, edit and inspect PEtab parameter estimation problems
Paul Jonas Jost, Frank T Bergmann, Daniel Weindl +1
Motivation: Parameter estimation is a cornerstone of data-driven modeling in systems biology. Yet, constructing such problems in a reproducible and accessible manner remains challe…
q-bio.QM2022
BioSimulators: a central registry of simulation engines and services for recommending specific tools
Bilal Shaikh, Lucian P. Smith, Dan Vasilescu +68
Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, becau…