The study provides a comprehensive analysis of the human regulatory network, derived from ENCODE data, focusing on the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. Key findings include: 1. **Context-Specific Co-Association**: Transcription factors co-associate in a combinatorial and context-specific manner, with different combinations of factors binding near different targets. This context-specificity is evident in both gene-proximal and distal regulatory regions. 2. **Hierarchical Organization**: The transcription factor network is organized into a hierarchy, with top-level factors influencing expression more strongly and middle-level factors co-regulating targets to mitigate information-flow bottlenecks. Lower-level factors are more regulated by other factors. 3. **Network Motifs**: The network contains many enriched network motifs, such as feed-forward loops, which are crucial for noise buffering and maintaining stable gene expression. 4. **Allelic Effects**: Highly connected network components exhibit stronger allele-specific activity, but these elements are under weaker selection compared to non-allelic ones. This suggests that the actual allele-specific binding sites are under less selective constraint. 5. **Evolutionary Selection**: More connected network elements are under stronger negative selection, consistent with findings in model organisms. However, highly connected transcription factors are more likely to exhibit allele-specific binding. 6. **Cell-Type Differences**: Transcription factor co-association patterns vary across different cell types, with some factors showing marked differences in their partners between cell lines. 7. **Proximal and Distal Regulation**: There are distinct partner preferences at proximal and distal sites, with core promoter transcription factors tending to bind proximally and factors like JUND and JUNB showing preferential co-association with distal sites. 8. **Collaboration Between Hierarchy Levels**: Middle-level transcription factors tend to collaborate more with top-level and bottom-level factors, both inter-level and intra-level, in terms of co-association, physical interactions, and target-expression cooperativity. 9. **Network Dynamics**: The targets of lower-level transcription factors tend to change more between cell types, indicating their role in more specialized processes. 10. **Network Dynamics and Gene Expression**: Highly connected factors tend to be highly expressed, and their binding signal around targets is positively correlated with target expression levels. This study provides a detailed understanding of the human regulatory network, highlighting its hierarchical structure, context-specific co-association, and the role of network motifs in maintaining gene expression stability.The study provides a comprehensive analysis of the human regulatory network, derived from ENCODE data, focusing on the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. Key findings include: 1. **Context-Specific Co-Association**: Transcription factors co-associate in a combinatorial and context-specific manner, with different combinations of factors binding near different targets. This context-specificity is evident in both gene-proximal and distal regulatory regions. 2. **Hierarchical Organization**: The transcription factor network is organized into a hierarchy, with top-level factors influencing expression more strongly and middle-level factors co-regulating targets to mitigate information-flow bottlenecks. Lower-level factors are more regulated by other factors. 3. **Network Motifs**: The network contains many enriched network motifs, such as feed-forward loops, which are crucial for noise buffering and maintaining stable gene expression. 4. **Allelic Effects**: Highly connected network components exhibit stronger allele-specific activity, but these elements are under weaker selection compared to non-allelic ones. This suggests that the actual allele-specific binding sites are under less selective constraint. 5. **Evolutionary Selection**: More connected network elements are under stronger negative selection, consistent with findings in model organisms. However, highly connected transcription factors are more likely to exhibit allele-specific binding. 6. **Cell-Type Differences**: Transcription factor co-association patterns vary across different cell types, with some factors showing marked differences in their partners between cell lines. 7. **Proximal and Distal Regulation**: There are distinct partner preferences at proximal and distal sites, with core promoter transcription factors tending to bind proximally and factors like JUND and JUNB showing preferential co-association with distal sites. 8. **Collaboration Between Hierarchy Levels**: Middle-level transcription factors tend to collaborate more with top-level and bottom-level factors, both inter-level and intra-level, in terms of co-association, physical interactions, and target-expression cooperativity. 9. **Network Dynamics**: The targets of lower-level transcription factors tend to change more between cell types, indicating their role in more specialized processes. 10. **Network Dynamics and Gene Expression**: Highly connected factors tend to be highly expressed, and their binding signal around targets is positively correlated with target expression levels. This study provides a detailed understanding of the human regulatory network, highlighting its hierarchical structure, context-specific co-association, and the role of network motifs in maintaining gene expression stability.