GenePert: Leveraging GenePT Embeddings for Gene Perturbation Prediction

Abstract

Predicting how perturbation of a target gene affects the expression of other genes is a critical component of understanding cell biology. This is a challenging prediction problem as the model must capture complex gene-gene relationships and the output is high-dimensional and sparse. To address this challenge, we present GenePert, a simple approach that leverages GenePT embeddings, which are derived using ChatGPT from text descriptions of individual genes, to predict gene expression changes due to perturbations via regularized regression models. Benchmarked on eight CRISPR perturbation screen datasets across multiple cell types and five different pretrained gene embedding models, GenePert consistently outperforms all the state-of-the-art prediction models measured in both Pearson correlation and mean squared error metrics. Even with limited training data, our model generalizes effectively, offering a scalable solution for predicting perturbation outcomes. These findings underscore the power of informative gene embeddings in predicting the outcomes of unseen genetic perturbation experiments in silico. GenePert is available at https://github.com/zou-group/GenePert.