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Many enhancers regulate their target genes via long-distance interactions. High-throughput experiments like ChIA-PET have been developed to map such largely cell-type-specific interactions betweencis-regulatory elements genome-widely. In this study, we integrated multiple types of data in order to reveal the general hidden patterns embedded in the ChIA-PET data. We found characteristic distance features related to promoter–promoter, enhancer–enhancer and insulator–insulator interactions. Although a protein may have many binding sites along the genome, our hypothesis is that those sites that share certain open chromatin structure can accommodate relatively larger protein complex consisting of specific regulatory and ‘bridging’ factors, and may be more likely to form robust long-range deoxyribonucleic acid (DNA) loops. This hypothesis was validated in the estrogen receptor alpha (ERα) ChIA-PET data. An efficient classifier was built to predict ERα-associated long-range interactions solely from the related ChIP-seq data, hence linking distal ERα-dependent enhancers to their target genes. We further applied the classifier to generate additional novel interactions, which were undetected in the original ChIA-PET paper but were validated by other independent experiments. Our work provides a new insight into the long-range chromatin interactions through deeper and integrative ChIA-PET data analysis and demonstrates DNA looping predictability from ordinary ChIP-seq data.