Yannik Schälte

This paper presents a framework for integrating single-cell and bulk data using nonlinear mixed-effect models and tailored parameter estimation, improving parameter identifiability and prediction accuracy.

This paper addresses missing data in amortized simulation-based neural posterior estimation by proposing methods to encode and handle missing values, showing improved robustness in inference.

FitMultiCell provides a scalable pipeline for modeling, simulating, and parameterizing multi-scale multi-cellular processes using Morpheus and pyABC.

The paper introduces a machine learning approach to non-linear mixed-effects modeling using neural density estimation, demonstrating its flexibility and scalability in cell biology and pharmacology.

This paper improves approximate Bayesian computation by combining adaptive scale normalization with regression-based summary statistics and sensitivity weights, enhancing accuracy and efficiency on heterogeneous data.

This paper introduces robust distance functions for ABC that handle outliers, showing improved performance on corrupted data while maintaining comparable results on clean data.

This study benchmarks numerical integration methods for ODE models in systems biology, finding that most ODEs are stiff and providing guidelines for algorithm and hyperparameter selection.

The paper introduces an efficient algorithm for ABC that accounts for measurement noise, improving parameter estimation accuracy in dynamical systems with noisy data.

The paper introduces a hierarchical method for efficiently parameterizing large-scale dynamic models using relative measurements, improving optimizer performance and enabling unbiased weighting of heterogeneous data.