Under both physiological and pathological conditions gene expression programs are shaped through the interplay of regulatory proteins and their gene targets, interactions between which form intricate gene regulatory networks (GRN). While the assessment of genome-wide expression for the complete set of genes at a given condition has become rather straight-forward and is performed routinely, we are still far from being able to infer the topology of gene regulation simply by analyzing its descendant expression profile. In this work we are trying to overcome the existing limitations for the inference and study of such regulatory networks. We are combining our approach with state-of-the-art gene set enrichment analyses in order to create a tool, called Regulatory Network Enrichment Analysis (RNEA) that will prioritize regulatory and functional characteristics of a genome-wide expression experiment.