提出新方法精准发现基因扰动中的高响应样本。
Many Needles in a Haystack: Active Hit Discovery for Perturbation Experiments
- 用后验概率直接评估扰动是否超阈值，优化实验选择
- 在真实免疫学数据上最高提升6.4%的命中率
- 适合资源有限但需广泛筛选有效基因扰动的研究
高通量基因扰动实验可并行测试多个遗传干预，但实验预算仍受限。核心目标是命中发现：识别尽可能多的表型效应超过预设阈值的扰动。纯探索策略统计效率低，浪费预算于低价值区域。贝叶斯优化虽具理论优势，但聚焦单一全局最优，过度开发主导模式而忽略其他高价值区域。本文将命中发现形式化为序列实验设计问题，提出概率命中（Probability-of-Hit）采集函数，依据候选扰动后验概率超阈值的程度进行排序。证明该方法渐近最优，并在合成基准和真实生物免疫学数据集上展示优异性能，包括在Schmidt IL-2数据集上最高达6.4%的性能提升。
原文摘要 · Abstract (English)
High-throughput gene perturbation experiments can test several genetic interventions in parallel, yet experimental budgets remain limited. A central goal is hit discovery: identifying as many perturbations as possible whose phenotypic effect exceeds a predefined threshold. Pure exploration strategies are statistically inefficient, wasting budget on low-value regions. Bayesian optimization methods offer a principled alternative but target a single global optimum, over-exploiting dominant modes while neglecting other high-value regions. We formalize hit discovery as a sequential experimental design problem and propose Probability-of-Hit, an acquisition function that directly targets threshold exceedance by ranking candidates according to their posterior probability of being a hit. We prove asymptotic optimality of this approach and demonstrate strong empirical performance on both synthetic benchmarks and real biological immunology datasets, including up to 6.4% improvement over baselines on the Schmidt IL-2 dataset.
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