Gene-level structure of prior knowledge and perturbation responses informs target-level predictability in prior-informed models

Background: Predictive models of transcriptional responses to unseen genetic perturbations increasingly incorporate biological prior knowledge. However, it remains unclear how well prior knowledge reflects each target's perturbation-response relationships and how closely prior-derived measures track target-level predictive performance. Here, we develop a gene-similarity framework to quantify agreement among eight sources of biological prior knowledge and between prior knowledge and co-function or co-regulation relationships derived from six Perturb-seq datasets. Results: In genome-wide and essential-gene screens, prior agreement accounted for 30-47% of the variance in co-function agreement and 35-43% in co-regulation agreement. Agreement with Perturb-seq responses was higher for essential genes, lower for transcription factor and signaling genes, and varied with gene expression characteristics and cellular context. We compared prior agreement and prior-derived train-test similarity with measures based on the observed test response in tracking target-level predictive performance. Across three prior-informed models, predictions were more accurate for targets whose observed perturbation-response relationships better matched prior knowledge and that were more similar to training perturbations. Notably, prior agreement and prior-derived train-test similarity, calculated without the test response, tracked relative differences in target-level predictive performance. The strength of these associations varied across models and datasets and, for train-test similarity, across prior sources. Conclusion: Our framework reveals gene- and context-level heterogeneity in how biological knowledge aligns with perturbation responses, and provides an a priori basis for assessing relative perturbation predictability before perturbation responses are measured.