  The discovery of therapeutics to treat genetically-driven pathologies relies
on identifying genes involved in the underlying disease mechanisms. Existing
approaches search over the billions of potential interventions to maximize the
expected influence on the target phenotype. However, to reduce the risk of
failure in future stages of trials, practical experiment design aims to find a
set of interventions that maximally change a target phenotype via diverse
mechanisms. We propose DiscoBAX, a sample-efficient method for maximizing the
rate of significant discoveries per experiment while simultaneously probing for
a wide range of diverse mechanisms during a genomic experiment campaign. We
provide theoretical guarantees of approximate optimality under standard
assumptions, and conduct a comprehensive experimental evaluation covering both
synthetic as well as real-world experimental design tasks. DiscoBAX outperforms
existing state-of-the-art methods for experimental design, selecting effective
and diverse perturbations in biological systems.
