CoReGRN: a context-aware post-processing framework for gene regulatory network inference revealing regulatory...

Accurate inference of gene regulatory networks (GRNs) from single-cell gene expression data is challenging due to noise, data sparsity, and variability in gene-gene associations across cells. We propose CoReGRN (Contextual Refinement of Gene Regulatory Networks), a nonparametric, context-aware post-processing framework that refines inferred GRNs by reweighting candidate regulatory interactions using Mutual Information based association strength and local network context. The method uses empirical cumulative distribution function (ECDF) based scores to assess how unusual each interaction is relative to the connectivity patterns of the genes it connects. Then combines this contextual information with the original edge confidence scores. We evaluate the CoReGRN framework on gold-standard datasets from the BEELINE benchmark suite across multiple state-of-the-art GRN inference algorithms. The results show consistent performance improvements, with average absolute gains of 0.097 in AUROC, 0.130 in AUPR, 0.133 in MCC, and 0.095 in F1-Score. We further apply the framework to a single-cell HIV-Leishmaniasis dataset, where the refined networks support the analysis of disease-specific regulatory hubs and interactions. Comparison with existing biological knowledge identifies both known and potentially novel regulatory relationships across HIV infection, HIV-Leishmaniasis, and HIV-Visceral Leishmaniasis conditions. The analysis includes hub gene identification, interaction network analysis, and literature based validation using GeneMANIA.