DiscoBAX：基因组实验设计中最优干预集的发现
DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design
AI总结：
针对基因组实验需发现多样且有效干预的问题，提出样本高效方法 DiscoBAX，在最大化显著发现率的同时探测多种机制，并具备近似最优性理论保证与优异实验表现。
摘要：
发现治疗遗传性疾病的疗法依赖于识别参与潜在疾病机制的基因。现有方法在数十亿种潜在干预中进行搜索，以最大化对目标表型的预期影响。然而，为降低未来试验阶段失败的风险，实际实验设计旨在找到一组通过多种不同机制最大程度改变目标表型的干预措施。我们提出 DiscoBAX，这是一种样本高效的方法，用于在基因组实验活动中最大化每次实验的重大发现率，同时探测广泛多样的机制。我们在标准假设下提供了近似最优性的理论保证，并对合成任务和真实世界实验设计任务进行了全面的实验评估。DiscoBAX 优于现有的最先进实验设计方法，能够在生物系统中选择有效且多样的扰动。
英文摘要：
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.
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