RELAY: Transferring Dense Response Contrasts to Distinguish Unseen Perturbations

Predicting the effects of unseen perturbations requires capturing gene-level response tendencies while distinguishing the effects of different perturbations on the same readout gene. On PerturbQA, a gene prior (each readout gene's majority training label) attains Macro-F1 of 0.8814 for differential expression (DE), and every language-model and expression-based predictor we evaluate falls below it. Input ablations identify retrieved outcomes as the source of a retrieval LLM's within-gene signal; descriptions of biological mechanisms contribute little. Yet labels cover only about 1% of measured perturbation–gene pairs. We introduce RELAY, which transfers dense measured responses without a language model. Kernel ridge regression over a biological knowledge kernel transfers responses from related training perturbations. Task-specific readouts then combine each gene's tendency with the transferred contrast to predict DE and direction of change (DIR), retaining the gene prior's global accuracy while distinguishing perturbations that act on the same readout gene. Across four PerturbQA cell lines, RELAY Pareto-dominates every evaluated baseline on both global and within-gene metrics. It reaches DE and DIR Macro-F1 of 0.8941 and 0.8722 and improves within-gene balanced accuracy over the strongest baseline by 7.48 and 6.53 points. RELAY also extends to a held-out cell line and to unseen compounds, retaining its advantage over the competing methods.