112 citations · 226 across the 23 of their papers we have counts for
17 papers · 1 filter
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…
Balancing label resolution and computational cost in dynamical models of lipid metabolism
Paul Jonas Jost, Christoph Thiele, Jan Hasenauer
Lipid metabolism is a central biological process that is commonly studied using destructive mass-spectrometry experiments. A recently proposed strategy, uses multiple labels to ext…
Scalable branch-and-bound model selection with non-monotonic criteria including AIC, BIC and Mallows's
Jakob Vanhoefer, Antonia Körner, Domagoj Doresic +2
Model selection is a pivotal process in the quantitative sciences, where researchers must navigate between numerous candidate models of varying complexity. Traditional information…
Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models in Epidemiological Research
Vincent Wieland, Nils Wassmuth, Lorenzo Contento +2
Global pandemics, such as the recent COVID-19 crisis, highlight the need for stochastic epidemic models that can capture the randomness inherent in the spread of disease. Such mode…
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…
Compositional amortized inference for large-scale hierarchical Bayesian models
Jonas Arruda, Vikas Pandey, Catherine Sherry +4
Amortized Bayesian inference (ABI) with neural networks has emerged as a powerful simulation-based approach for estimating complex mechanistic models. However, extending ABI to hie…