Optimal Experimental Design Methods

The paper introduces a tool to optimally allocate samples to batches to balance covariates and reduce technical variability impacts.

This paper uses Bayesian optimization to accelerate cell culture media development, achieving better results with fewer experiments in two applications.

This paper introduces PARSEC, a model-based design of experiments framework that improves parameter estimation by using parameter sensitivity clustering and Bayesian computation.

This paper introduces a constraint programming method for designing microplate layouts to reduce bias and batch effects, improving accuracy in dose-response experiments and drug screening.

NLoed provides a Python package for nonlinear optimal experimental design in systems biology, supporting diverse models and data types with efficient computation.

This paper applies reinforcement learning to optimal experimental design in biology, showing it outperforms existing methods in bacterial growth parameter inference under parametric uncertainty.

This study compares human intuition with a computational tool for selecting optimal time points in experiments, finding that the tool reduces bias and improves information capture.