Foundation Models Improve Perturbation Response Prediction

Abstract

Predicting cellular responses to genetic or chemical perturbations is a long-standing goal in biology. Recent applications of foundation models to this task have yielded contradictory results regarding their superiority over simple baselines. We conducted an extensive analysis of the utility of pretrained embeddings (including but not limited to biological foundation models) for perturbation response prediction, covering over 600 embedding-task pairs. While some foundation models fail to outperform simple baselines, others yield consistent improvements. However, the strongest pretrained embeddings are those derived from prior knowledge. We also developed methods for integrating multiple pretrained embeddings to make better predictions. Our results confirm that pretrained embeddings can enable more accurate simulations of cellular behavior.