Traditional methods for obtaining cellular responses after perturbation are usually labor-intensive and costly, especially when working with multiple different experimental conditions. Therefore, accurate prediction of cellular responses to perturbations is of great importance in computational biology. Existing methodologies, such as graph-based approaches, vector arithmetic, and neural networks, either mix perturbation-related variances with cell-type-specific patterns or implicitly distinguish them within black-box models.

中文翻译：

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scPerb：通过基于样式迁移的变分自动编码器预测单单元扰动


扰动后获得细胞反应的传统方法通常是劳动密集型且成本高昂的，尤其是在处理多种不同的实验条件时。因此，准确预测细胞对扰动的反应在计算生物学中非常重要。现有的方法，例如基于图的方法、向量算术和神经网络，要么将与扰动相关的方差与单元类型特定的模式混合，要么在黑盒模型中隐式区分它们。