Generalization to Unseen Perturbations via Multiple Biological Priors

Predicting cellular responses to genetic and chemical perturbations can reduce the cost of experimental screening. A key challenge is generalization to unseen perturbations, since perturbation identities alone do not specify how their responses relate to those observed during training. However, biological knowledge from existing research provides multiple priors that can connect observed and unseen perturbations through shared molecular and functional properties. We thus formulate unseen perturbation prediction as a generalization problem over multiple biological priors, and introduce a corresponding hypothesis space that accommodates different priors and predictor architectures. Using this methodology, we design a two-stage learning algorithm, connected to late fusion, stacked generalization, and boosting, that first trains a response predictor for each prior and then integrates their predictions in response space through a shared integration function. Across two chemical and two genetic perturbation datasets spanning three cell lines, our approach outperforms single-prior predictors and representation-level fusion methods across various metrics, providing an effective way to combine multiple priors.