标题： BioBO：用于扰动设计的生物信息贝叶斯优化 显示英文标题

标题： BioBO: Biology-informed Bayesian Optimization for Perturbation Design

摘要： 高效设计基因组扰动实验对于加速药物发现和治疗靶点识别至关重要，但由于潜在遗传相互作用的广阔搜索空间和实验限制，对人类基因组进行全面扰动仍然不可行。贝叶斯优化（BO）已成为选择信息性干预措施的强大框架，但现有方法往往未能充分利用特定领域的生物先验知识。我们提出了生物学感知的贝叶斯优化（BioBO），一种将贝叶斯优化与多模态基因嵌入和富集分析相结合的方法，富集分析是生物学中用于基因优先排序的常用工具，以增强代理建模和获取策略。BioBO在一个原则性的框架中结合了基于生物学的先验和获取函数，这使得搜索偏向于有希望的基因，同时保持探索不确定区域的能力。通过在已建立的公共基准和数据集上的实验，我们证明BioBO的标记效率提高了25-40%，并且始终优于传统BO，能够更有效地识别表现最佳的扰动。此外，通过引入富集分析，BioBO为所选扰动提供了通路级别的解释，提供了将设计与生物上连贯的调控回路联系起来的机制可解释性。 显示英文摘要

摘要： Efficient design of genomic perturbation experiments is crucial for accelerating drug discovery and therapeutic target identification, yet exhaustive perturbation of the human genome remains infeasible due to the vast search space of potential genetic interactions and experimental constraints. Bayesian optimization (BO) has emerged as a powerful framework for selecting informative interventions, but existing approaches often fail to exploit domain-specific biological prior knowledge. We propose Biology-Informed Bayesian Optimization (BioBO), a method that integrates Bayesian optimization with multimodal gene embeddings and enrichment analysis, a widely used tool for gene prioritization in biology, to enhance surrogate modeling and acquisition strategies. BioBO combines biologically grounded priors with acquisition functions in a principled framework, which biases the search toward promising genes while maintaining the ability to explore uncertain regions. Through experiments on established public benchmarks and datasets, we demonstrate that BioBO improves labeling efficiency by 25-40%, and consistently outperforms conventional BO by identifying top-performing perturbations more effectively. Moreover, by incorporating enrichment analysis, BioBO yields pathway-level explanations for selected perturbations, offering mechanistic interpretability that links designs to biologically coherent regulatory circuits.