AI 中文总结
本研究提出可视化分析系统CellPrism，用于探索AI虚拟细胞模型的高维扰动空间，经案例与访谈验证，可助力药物发现中基因扰动策略的系统探索。
AI 中文摘要
基因扰动分析在药物发现中至关重要，它能让研究者探究对特定基因的干预如何影响细胞内的全局基因表达模式。近期，人工智能驱动的虚拟细胞模型取得进展，可在计算机上预测多种扰动策略下的基因表达结果，大幅减少对昂贵且耗时的生物实验的依赖。然而，由于扰动的组合性质及其产生的复杂细胞特异性基因表达响应，有效探索这些模型生成的高维扰动空间仍具挑战。本研究提出CellPrism，一款用于支持药物发现中基因扰动策略系统探索的可视化分析系统。具体而言，CellPrism整合基于聚类的概览以总结扰动结果、基于符号的表示法以紧凑编码不同细胞类型的基因表达模式，以及协调视图以实现对扰动效应的细粒度比较与解释。我们通过真实案例研究和专家访谈验证了CellPrism的有效性，该研究凸显了可视化分析在药物发现中连接虚拟细胞建模与专家驱动决策的价值。
英文摘要
Gene perturbation analysis plays a critical role in drug discovery by enabling researchers to investigate how interventions on specific genes influence global gene expression patterns within cells. Recent advances in artificial intelligence-driven virtual cell models have made it possible to predict gene expression outcomes for a wide range of perturbation strategies in silico, substantially reducing reliance on costly and time-consuming biological experiments. However, effectively exploring and interpreting the high-dimensional perturbation spaces produced by these models remains challenging because of the combinatorial nature of perturbations and the complex cell-specific gene expression responses they generate. In this work, we present CellPrism, a visual analytics system designed to support the systematic exploration of gene perturbation strategies for drug discovery. Specifically, CellPrism integrates clustering-based overviews to summarize perturbation outcomes, a glyph-based representation to compactly encode gene expression patterns across cell types, and coordinated views that enable fine-grained comparison and interpretation of perturbation effects. We demonstrate the effectiveness of CellPrism through a real-world case study and expert interviews. This work highlights the value of visual analytics in bridging virtual cell modeling with expert-driven decision making in drug discovery.
