Reference: Predicting cellular responses to perturbationacross diverse contexts with State.Arc正式发布首个虚拟细胞模型 State，这是一种用于预测细胞扰动反应的机器学习模型。该模型旨在克服现有计算方法在泛化不同细胞背景下扰动效应方面的局限性。State 包含两个主要组件：State Transition (ST) 模型，它学习扰动引起的细胞群体转变；以及 State Embedding (SE) 模型，它从大量观察性单细胞数据中生成富有表现力的细胞嵌入。研究结果表明，State 在预测基因表达变化、识别差异表达基因以及估计扰动效应大小方面，其性能优于现有基线模型。此外，State 能够对未曾观察到扰动的细胞背景进行 零样本预测，并识别细胞类型特异性反应，包括对细胞存活的影响。该研究还深入探讨了 ST 模型与 最优传输 (Optimal Transport) 理论的联系。该模型的应用可以大大的加速疾病研究和药物筛查。
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