GENEBRIDGE: HOW TO BRIDGE GENE SPACES FOR TRANSDUCTIVE ZERO-SHOT GRN INFERENCE

Gene regulatory network (GRN) inference faces a fundamental precision–generality trade-off: supervised methods achieve high accuracy on labeled cell types but cannot generalize to unseen ones, whereas unsupervised and foundation model-based approaches apply broadly at the cost of predictive accuracy. We propose GeneBridge, a zero-shot framework that transfers regulatory knowledge across cell types through a unified semantic gene graph. The central challenge is gene space misalignment: different cell types express different gene subsets, making expression-based representations non-transferable. GeneBridge addresses this by constructing a transferable semantic graph from foundation model embeddings, where hybrid node representations—contextual for expressed genes, frozen pretrained for absent ones—allow all genes to coexist in a shared semantic space. Experiments on seven BEELINE datasets under strict leave-one-out evaluation show that GeneBridge consistently outperforms existing methods by up to 3.1×. Ablation studies further demonstrate that semantic gene selection is foundational: replacing it with standard HVG filtering reduces Top-100 precision to near zero. Together, these results demonstrate that semantic graph learning provides a principled mechanism for transferring regulatory knowledge across heterogeneous biological contexts.