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There are mainly two approaches to predict chromatin loops: transcription factor (TF) binding-dependent approach and genomic variation-based approach. However, neither of these approaches provides an adequate understanding of gene regulation in human tissues. To address this issue, we developed a deep learning-based chromatin loop prediction model called Deep Learning-based Universal Chromatin Interaction Annotator (DeepLUCIA).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Although DeepLUCIA does not use TF binding profile data which previous TF binding-dependent methods critically rely on, its prediction accuracies are comparable to those of the previous TF binding-dependent methods. More importantly, DeepLUCIA enables the tissue-specific chromatin loop predictions from tissue-specific epigenomes that cannot be handled by genomic variation-based approach. We demonstrated the utility of the DeepLUCIA by predicting several novel target genes of SNPs identified in genome-wide association studies targeting Brugada syndrome, COVID-19 severity and age-related macular degeneration.<\/jats:p><jats:p>Availability and implementation<\/jats:p><jats:p>DeepLUCIA is freely available at https:\/\/github.com\/bcbl-kaist\/DeepLUCIA.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac373","type":"journal-article","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:43:36Z","timestamp":1654044216000},"page":"3501-3512","source":"Crossref","is-referenced-by-count":12,"title":["DeepLUCIA: predicting tissue-specific chromatin loops using Deep Learning-based Universal Chromatin Interaction Annotator"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2311-5794","authenticated-orcid":false,"given":"Dongchan","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Bio and Brain Engineering, KAIST , Daejeon 34141, Republic of Korea"}]},{"given":"Taesu","family":"Chung","sequence":"additional","affiliation":[{"name":"Biotechnology & Healthcare Examination Division, Convergence Technology Examination Bureau, KIPO , Daejeon 35208, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5916-6799","authenticated-orcid":false,"given":"Dongsup","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Bio and Brain Engineering, KAIST , Daejeon 34141, Republic of Korea"}]}],"member":"286","published-online":{"date-parts":[[2022,5,31]]},"reference":[{"key":"2023041405364641100_","doi-asserted-by":"crossref","first-page":"5217","DOI":"10.1038\/s41598-018-23276-8","article-title":"Three-dimensional epigenome statistical model: genome-wide chromatin looping prediction","volume":"8","author":"Al Bkhetan","year":"2018","journal-title":"Sci. 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