Differential network analysis has emerged as a powerful tool for identifying condition-specific changes in molecular interactions, particularly in high-dimensional omics data. Traditional approaches, such as Fisher’s z-test and fixed-threshold correlation methods, often struggle with noise, confounding effects, and selecting an appropriate significance threshold. In this study, we present a novel framework that integrates confounder adjustment, bootstrapped correlation inference, and biologically informed threshold selection to improve the robustness of differential network analysis. We first validate our method using simulated data, comparing its performance against existing approaches. Our results demonstrate that our method better balances precision and recall, outperforming established benchmarks such as DiffCorr. We then apply our framework to a case study investigating sex-specific differences in protein interaction networks. Our results highlight the abilities of our approach to uncover meaningful molecular differential interactions in complex biological systems.