This study presents an analysis of the human regulatory network derived from ENCODE data, revealing the combinatorial and context-specific interactions of 119 transcription factors across 450 experiments. The regulatory network is organized into a hierarchy, integrating genomic data such as microRNA regulation, protein-protein interactions, and phosphorylation. Key findings include the context-specific co-association of transcription factors, with distinct combinations binding at specific genomic regions. The network is enriched with motifs like feed-forward loops, and highly connected elements show stronger allele-specific activity. The study also highlights the hierarchical organization of transcription factors, with top-level factors having greater regulatory influence and middle-level factors co-regulating targets to mitigate information flow bottlenecks. Distal and proximal regulatory regions show different binding patterns, and the network is enriched with motifs such as feed-forward loops. The study further reveals that highly connected network components are under stronger evolutionary selection and exhibit allele-specific activity. The analysis of allelic effects shows that transcription factors with higher allelicity tend to have more target genes and are more sensitive to maternal-paternal sequence variations. Selection pressures are also linked to regulatory in-degree and out-degree, with top-level transcription factors under stronger negative selection. The study provides insights into the organization of human regulatory networks and their implications for gene expression, disease, and evolutionary biology.This study presents an analysis of the human regulatory network derived from ENCODE data, revealing the combinatorial and context-specific interactions of 119 transcription factors across 450 experiments. The regulatory network is organized into a hierarchy, integrating genomic data such as microRNA regulation, protein-protein interactions, and phosphorylation. Key findings include the context-specific co-association of transcription factors, with distinct combinations binding at specific genomic regions. The network is enriched with motifs like feed-forward loops, and highly connected elements show stronger allele-specific activity. The study also highlights the hierarchical organization of transcription factors, with top-level factors having greater regulatory influence and middle-level factors co-regulating targets to mitigate information flow bottlenecks. Distal and proximal regulatory regions show different binding patterns, and the network is enriched with motifs such as feed-forward loops. The study further reveals that highly connected network components are under stronger evolutionary selection and exhibit allele-specific activity. The analysis of allelic effects shows that transcription factors with higher allelicity tend to have more target genes and are more sensitive to maternal-paternal sequence variations. Selection pressures are also linked to regulatory in-degree and out-degree, with top-level transcription factors under stronger negative selection. The study provides insights into the organization of human regulatory networks and their implications for gene expression, disease, and evolutionary biology.