Learning Faithful Mechanism Subgraphs from Partially Trusted Biological Priors for Genetic Perturbation Prediction

Predicting cellular responses to genetic perturbations is a central problem in virtual cell modeling. However, current methods face two key limitations: they typically treat biological priors as fixed ground truth, which are inherently incomplete, noisy, and only partially trustworthy; and they lack explicit mechanistic explanations for their predictions, instead relying on post hoc analyses rather than explicit, testable mediator entities. As a result, it remains unclear whether a model relies on biological mechanisms or merely exploits statistical correlations. These limitations motivate a broader machine learning question: how can we learn from partially trusted prior knowledge while ensuring that the learned explanation faithfully reflects the mechanism underlying the prediction? To address this question in genetic perturbation modeling, we propose **PerturbDigger**, a graph learning framework for perturbation prediction and mechanistic explanation. PerturbDigger formulates biological prior knowledge as a partially trusted prior graph, performs context-specific structural calibration to improve graph reliability, and learns perturbation responses with a biologically-guided graph transformer regularized by faithfulness constraints on mechanistic subgraphs. Extensive experiments show that PerturbDigger improves genetic perturbation prediction, generalizes better to unseen cellular contexts, and produces mechanistic subgraphs at both the sample and perturbation levels. Additional analyses show that the learned subgraphs are biologically meaningful, faithful, and stable, suggesting a promising step toward more interpretable and mechanism-aware virtual cell models.