The problem of how to genetically modify cells in order to maximize a certain
cellular phenotype has taken center stage in drug development over the last few
years (with, for example, genetically edited CAR-T, CAR-NK, and CAR-NKT cells
entering cancer clinical trials). Exhausting the search space for all possible
genetic edits (perturbations) or combinations thereof is infeasible due to cost
and experimental limitations. This work provides a theoretically sound
framework for iteratively exploring the space of perturbations in pooled
batches in order to maximize a target phenotype under an experimental budget.
Inspired by this application domain, we study the problem of batch query bandit
optimization and introduce the Optimistic Arm Elimination ($\mathrm{OAE}$)
principle designed to find an almost optimal arm under different functional
relationships between the queries (arms) and the outputs (rewards). We analyze
the convergence properties of $\mathrm{OAE}$ by relating it to the Eluder
dimension of the algorithm's function class and validate that $\mathrm{OAE}$
outperforms other strategies in finding optimal actions in experiments on
simulated problems, public datasets well-studied in bandit contexts, and in
genetic perturbation datasets when the regression model is a deep neural
network. OAE also outperforms the benchmark algorithms in 3 of 4 datasets in
the GeneDisco experimental planning challenge.

本文提出了一种理论上可行的方法来探索批量扰动以最大化目标基因表型，介绍了一个称为优先臂淘汰的约束优化技术，并发现在模拟和真实基因扰动数据的实验中，优先臂淘汰算法明显优于其他算法。