GRASP: Adaptive Context with Regulatory Partner Supervision for GRN Inference

Supervised gene regulatory network inference from single cell transcriptomic data seeks to rank unobserved transcription factor-target links by combining expression profiles with partial interaction references. Graph-based approaches exploit such references to provide relational context for prediction. However, local message passing remains bounded by incomplete reference topology. Moreover, same gene contrastive alignment does not explicitly exploit training positive TF-target partners when defining positive comparisons. To address these issues, we propose a framework named GRASP, which couples adaptive context construction with partner-based contrastive supervision for GRN inference. Technically, GRASP uses expression guided edge retention to adapt local propagation to the measured cellular context, and further constructs a teacher induced higher-order view to provide context beyond recorded graph neighborhoods. To exploit relational supervision already available in training interactions, neighborhood contrastive learning augments same gene correspondence with fixed training partners as positives within and across the two views, while a role-specific decoder produces ordered TF-target scores. We additionally employ pair-disjoint evaluation to prevent held out gene-pair connectivity from entering the training graph. Across 42 evaluation settings, GRASP achieves the highest average AUPRC among eight evaluated baselines. Code is available at https://anonymous.4open.science/r/GRASP-302C/.