TY  - JOUR
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
Y1  - 2026/03/26 
JF  - Genome Research 
JO  - Genome Research 
DO  - 10.1101/gr.281523.125 
SP  - gr.281523.125 
UR  - http://genome.cshlp.org/content/early/2026/03/26/gr.281523.125.abstract 
N2  - 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. 
ER  -