Haihui Zhang

Combinatorial perturbation screens grow quadratically in pairs, so most combinations are never measured, yet combined transcriptional responses often depart from the sum of the two single effects. GrnPFN is pretrained on simulated screens generated from random gene-regulatory networks to predict that nonadditive residual directly, then incorporates measured pairs through in-context learning without retraining. Across five single-cell screens, with no measured pairs in the target screen it reached a pooled cosine similarity 0.049 higher than gradient boosting trained on other screens (97.5% CI, 0.038–0.060); as measured pairs accumulated, kernel ridge fitted to the same pairs caught up.

Combinatorial perturbation screens grow quadratically in pairs, so most combinations are never measured, yet combined transcriptional responses often depart from the sum of the two single effects. GrnPFN is pretrained on simulated screens generated from random gene-regulatory networks to predict that nonadditive residual directly, then incorporates measured pairs through in-context learning without retraining. Across five single-cell screens, with no measured pairs in the target screen it reached a pooled cosine similarity 0.049 higher than gradient boosting trained on other screens (97.5% CI, 0.038–0.060); as measured pairs accumulated, kernel ridge fitted to the same pairs caught up.