Foundation Models Improve Perturbation Response Prediction
Cole, E.; Huizing, G.-J.; Addagudi, S.; Ho, N.; Hasanaj, E.; Kuijs, M.; Johnstone, T.; Carilli, M.; Davi, A.; Ellington, C.; Feinauer, C.; Li, P.; Menegaux, R.; Mohammadi, S.; Shao, Y.; Zhang, J.; Lundberg, E.; Song, L.; Bar-Joseph, Z.; Xing, E. P.
Show abstract
Predicting cellular responses to genetic or chemical perturbations has been 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 over 600 different models across various prediction tasks and evaluation metrics, demonstrating that while some foundation models fail to outperform simple baselines, others significantly improve predictions for both genetic and chemical perturbations. Furthermore, we developed and evaluated methods for integrating multiple foundation models for perturbation prediction. Our results show that with sufficient data, these models approach fundamental performance limits, confirming that foundation models can improve cellular response simulations. Code and Data: https://github.com/genbio-ai/foundation-models-perturbation
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- An adversarial scheme for integrating multi-modal data on protein function 96%
- Sequence-based prediction of protein-protein interactions: a structure-aware interpretable deep learning model 96%
- Belayer: Modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomics 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.