RT Journal
A1 Fu, Xiuhao
A1 Yang, Chao
A1 Cui, Chunyan
A1 Geng, Aoyun
A1 Sun, Yidi
A1 Zha, Chao
A1 Cui, Feifei
A1 Wei, Leyi
A1 Zou, Quan
A1 Gao, Xin
A1 Zhang, Zilong
T1 Incorporating valuable prior knowledge to improve deep learning prediction of genetic perturbation responses
JF Genome Research 
JO Genome Research 
YR 2026 
FD March 26 
DO 10.1101/gr.281523.125 
SP gr.281523.125 
UL http://genome.cshlp.org/content/early/2026/03/26/gr.281523.125.abstract 
AB Genetic perturbation response prediction plays a critical role in virtual cell research, yet the performance of current deep learning models still leaves room for improvement. In this study, we present a prior-guided response inference model (PRIM) that leverages a valuable priori knowledge of gene expression in control cells to model the effects of perturbations at the cellular level. This allows PRIM to predict the amount of change in gene expression after perturbation to better approximate the real situation. Compared with existing deep learning approaches, PRIM achieves superior performance across multiple datasets and notable advantages in predicting combinatorial perturbation responses. Moreover, it is more lightweight than current deep learning models and enables faster forward inference. Importantly, PRIM effectively captures nonadditive genetic interactions and shows the potential to uncover associations between combinatorial perturbations and new biologically meaningful phenotypes. These findings provide new insights into the application of deep learning for predicting cellular responses to genetic perturbations.