Optimal Experimental Design Methods

Improving the efficiency of single-driver ecological experiments with Bayesian optimal experimental design

This paper develops Bayesian optimal experimental designs to improve efficiency in ecological experiments by reducing the number of measurements needed for accurate parameter estimation.

Design-of-Experiments for Nonlinear, Multivariate Biology: Rethinking Experimental Design through Perturb-seq

The paper evaluates experimental design in Perturb-seq, finding that correlated designs are robust in systems with signal amplification and introduces a metric to predict optimal designs.

Minimizing inferential bias in the theory and design of nutritional experiments through the application of the equilateral mixture triangle

The paper introduces the equilateral mixture triangle as a geometric framework to reduce inferential bias in nutritional experiments by improving the representation of nutrient interactions.

Bridging biology and statistics with hybrid Bayesian experimental design for drug dose-response assays

This paper introduces a Bayesian framework for optimizing drug dose-response assays to improve information efficiency and reduce trial-and-error in cancer research.

A generalised method for experiment design and model selection in the Bayesian framework

This paper introduces a Bayesian Optimal Experiment Design method for parameter estimation and model selection, demonstrating its effectiveness in systems biology models.

Efficient online-group-screening designs for agent identification

The paper introduces an online-group-screening method to efficiently identify causal agents in high-throughput experiments, reducing the number of required tests by up to 80% in worst-case scenarios.

STPGA: Selection of training populations with a genetic algorithm

The paper introduces STPGA, a genetic algorithm for optimal subset selection, applied to genomic prediction and other statistical problems.