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.

翻译：在过去几年中，如何通过基因修饰细胞以最大化特定细胞表型的问题已成为药物开发的核心（例如，基因编辑的CAR-T、CAR-NK和CAR-NKT细胞已进入癌症临床试验）。由于成本和实验限制，穷尽所有可能的基因编辑（扰动）或其组合的搜索空间是不切实际的。本文提供了一个理论坚实的框架，用于在实验预算下以合并批次的方式迭代探索扰动空间，以最大化目标表型。受此应用领域的启发，我们研究了批次查询赌博机优化问题，并引入了乐观臂消除（$\mathrm{OAE}$）原则，该原则旨在查询（臂）与输出（奖励）之间存在不同函数关系时找到近似最优的臂。我们通过将$\mathrm{OAE}$与算法函数类别的Eluder维度相关联，分析了其收敛性质，并验证了在模拟问题、赌博机背景下广泛研究的公开数据集以及回归模型为深度神经网络的基因扰动数据集中，$\mathrm{OAE}$在寻找最优动作方面优于其他策略。在GeneDisco实验规划挑战中，$\mathrm{OAE}$在4个数据集的3个中超越了基准算法。