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High-throughput technologies like ChIA-PET, Hi-C, and their derivatives methods have greatly enhanced our knowledge of 3D chromatin architecture. However, the chromatin interaction mechanisms remain largely unexplored. Deep learning, with its powerful feature extraction and pattern recognition capabilities, offers a promising approach for integrating multi-omics data, to build accurate predictive models of chromatin interaction matrices. This review systematically summarizes recent advances in chromatin interaction matrix prediction models. By integrating DNA sequences and epigenetic signals, we investigate the latest developments in these methods. This article details various models, focusing on how one-dimensional (1D) information transforms into the 3D structure chromatin interactions, and how the integration of different deep learning modules specifically affects model accuracy. Additionally, we discuss the critical role of DNA sequence information and epigenetic markers in shaping 3D genome interaction patterns. Finally, this review addresses the challenges in predicting chromatin interaction matrices, in order to improve the precise mapping of chromatin interaction matrices and DNA sequence, and supporting the transformation and theoretical development of 3D genomics across biological systems.<\/jats:p>","DOI":"10.1093\/bib\/bbae651","type":"journal-article","created":{"date-parts":[[2024,12,21]],"date-time":"2024-12-21T22:50:24Z","timestamp":1734821424000},"source":"Crossref","is-referenced-by-count":21,"title":["A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6824-9505","authenticated-orcid":false,"given":"Yunlong","family":"Wang","sequence":"first","affiliation":[{"name":"Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences , No. 97 Buxin Road, Dapeng New District, Shenzhen 518120 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"Kong","sequence":"additional","affiliation":[{"name":"Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences , No. 97 Buxin Road, Dapeng New District, Shenzhen 518120 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University , No. 1 Shizishan Street, Hongshan District, Wuhan 430070 ,","place":["China"]},{"name":"Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center , No. 1 Shizishan Street, Hongshan District, Wuhan 430070 ,","place":["China"]},{"name":"College of Informatics, Huazhong Agricultural University , No. 1 Shizishan Street, Hongshan District, Wuhan 430070 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanfang","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS) , No. 2 West Yuanmingyuan Rd, Haidian District, Beijing 100193 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yubo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences , No. 97 Buxin Road, Dapeng New District, Shenzhen 518120 ,","place":["China"]},{"name":"Sequencing 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