理解细胞对基因干扰的反应对于众多生物医学应用至关重要，从确定与癌症相关的遗传互作用到开发再生医学的方法。然而，多基因干扰的可能组合数量的爆炸式增长严重限制了实验探究。在这里，我们提出了一种增强图形的基因激活和抑制模拟器（Graph-Enhanced Gene Activation and Repression Simulator，GEARS），该方法将深度学习与基因-基因关系的知识图相结合，利用来自干扰屏幕的单细胞RNA测序数据预测对于单个和多基因干扰的转录反应。GEARS能够预测从未进行实验干扰的基因组合的影响。与现有方法相比，GEARS在预测复杂干扰屏幕中的四个不同遗传互作亚型方面精度提高了40%，并且在识别最强的互作中表现出两倍于之前方法的表现。总体而言，GEARS能够预测多基因干扰的表型效应，并因此指导干扰实验的设计。© 2023. 作者。

Understanding cellular responses to genetic perturbation is central to numerous biomedical applications, from identifying genetic interactions involved in cancer to developing methods for regenerative medicine. However, the combinatorial explosion in the number of possible multigene perturbations severely limits experimental interrogation. Here, we present graph-enhanced gene activation and repression simulator (GEARS), a method that integrates deep learning with a knowledge graph of gene-gene relationships to predict transcriptional responses to both single and multigene perturbations using single-cell RNA-sequencing data from perturbational screens. GEARS is able to predict outcomes of perturbing combinations consisting of genes that were never experimentally perturbed. GEARS exhibited 40% higher precision than existing approaches in predicting four distinct genetic interaction subtypes in a combinatorial perturbation screen and identified the strongest interactions twice as well as prior approaches. Overall, GEARS can predict phenotypically distinct effects of multigene perturbations and thus guide the design of perturbational experiments.© 2023. The Author(s).