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Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis.<\/jats:p>","DOI":"10.1093\/bib\/bbad442","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T08:09:04Z","timestamp":1701677344000},"source":"Crossref","is-referenced-by-count":9,"title":["Integrative approaches based on genomic techniques in the functional studies on enhancers"],"prefix":"10.1093","volume":"25","author":[{"given":"Qilin","family":"Wang","sequence":"first","affiliation":[{"name":"Beihang University School of Engineering Medicine, , Beijing 100191, China"},{"name":"Beihang University School of Biological Science and Medical Engineering, , Beijing 100191, China"}]},{"given":"Junyou","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beihang University School of Engineering Medicine, , Beijing 100191, China"},{"name":"Beihang University School of Biological Science and Medical Engineering, , Beijing 100191, China"}]},{"given":"Zhaoshuo","family":"Liu","sequence":"additional","affiliation":[{"name":"Beihang University School of Engineering Medicine, , Beijing 100191, China"},{"name":"Beihang University School of Biological Science and Medical Engineering, , Beijing 100191, China"}]},{"given":"Yingying","family":"Duan","sequence":"additional","affiliation":[{"name":"Beihang University School of Engineering Medicine, , Beijing 100191, China"},{"name":"Beihang University School of Biological Science and Medical Engineering, , Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9720-2631","authenticated-orcid":false,"given":"Chunyan","family":"Li","sequence":"additional","affiliation":[{"name":"Beihang University School of Engineering Medicine, , Beijing 100191, China"},{"name":"Beihang University School of Biological Science and Medical Engineering, , Beijing 100191, China"},{"name":"Beihang University Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), , Beijing 100191, China"},{"name":"Beihang University Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, , Beijing 100191, China"}]}],"member":"286","published-online":{"date-parts":[[2023,12,2]]},"reference":[{"key":"2023120408083000100_ref1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1242\/dev.035493","article-title":"Promoting developmental transcription","volume":"137","author":"Ohler","year":"2010","journal-title":"Development"},{"key":"2023120408083000100_ref2","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1002\/ame2.12032","article-title":"Enhancer and super-enhancer: positive regulators in gene transcription","volume":"1","author":"Peng","year":"2018","journal-title":"Animal Model Exp Med"},{"key":"2023120408083000100_ref3","doi-asserted-by":"crossref","first-page":"e2300044","DOI":"10.1002\/bies.202300044","article-title":"What is an 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Biol"},{"key":"2023120408083000100_ref88","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.mce.2013.06.021","article-title":"Hormone-regulated transcriptomes: lessons learned from estrogen signaling pathways in breast cancer cells","volume":"382","author":"Hah","year":"2014","journal-title":"Mol Cell Endocrinol"},{"key":"2023120408083000100_ref89","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.tig.2015.11.004","article-title":"Enhanced identification of transcriptional enhancers provides mechanistic insights into diseases","volume":"32","author":"Murakawa","year":"2016","journal-title":"Trends Genet"},{"key":"2023120408083000100_ref90","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1038\/nature13182","article-title":"A promoter-level mammalian expression atlas","volume":"507","author":"Consortium 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high-throughput reporter assays","volume":"2019","author":"Cai","year":"2019","journal-title":"Database (Oxford)"},{"key":"2023120408083000100_ref101","first-page":"D58","article-title":"EnhancerAtlas 2.0: an updated resource with enhancer annotation in 586 tissue\/cell types across nine species","volume":"48","author":"Gao","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2023120408083000100_ref102","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.1101\/gr.264606.120","article-title":"Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model","volume":"30","author":"Tang","year":"2020","journal-title":"Genome Res"},{"key":"2023120408083000100_ref103","doi-asserted-by":"crossref","first-page":"103798","DOI":"10.1016\/j.isci.2022.103798","article-title":"Machine learning for multi-omics data integration in 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Biol"},{"key":"2023120408083000100_ref123","doi-asserted-by":"crossref","first-page":"3232","DOI":"10.1093\/bioinformatics\/btz064","article-title":"Variational infinite heterogeneous mixture model for semi-supervised clustering of heart enhancers","volume":"35","author":"Mehdi","year":"2019","journal-title":"Bioinformatics"},{"key":"2023120408083000100_ref124","doi-asserted-by":"crossref","first-page":"E2191","DOI":"10.1073\/pnas.1320308111","article-title":"Global view of enhancer-promoter interactome in human cells","volume":"111","author":"He","year":"2014","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2023120408083000100_ref125","doi-asserted-by":"crossref","first-page":"30043","DOI":"10.1038\/srep30043","article-title":"PETModule: a motif module based approach for enhancer target gene prediction","volume":"6","author":"Zhao","year":"2016","journal-title":"Sci 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genome","volume":"37","author":"Ji","year":"2021","journal-title":"Bioinformatics"},{"key":"2023120408083000100_ref132","article-title":"iEnhancer-GAN: a deep learning framework in combination with word embedding and sequence generative adversarial net to identify enhancers and their strength","volume":"22","author":"Yang","year":"2021","journal-title":"Int J Mol Sci"},{"key":"2023120408083000100_ref133","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1089\/cmb.2021.0316","article-title":"Integrating long-range regulatory interactions to predict gene expression using graph convolutional networks","volume":"29","author":"Bigness","year":"2022","journal-title":"J Comput Biol"},{"key":"2023120408083000100_ref134","doi-asserted-by":"crossref","first-page":"7449","DOI":"10.1109\/TIP.2022.3223793","article-title":"GraphReg: dynamical point cloud registration with geometry-aware graph signal processing","volume":"31","author":"Zhao","year":"2022","journal-title":"IEEE Trans Image 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Bioinform"},{"key":"2023120408083000100_ref150","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1093\/bib\/bbz070","article-title":"Consistency and overfitting of multi-omics methods on experimental data","volume":"21","author":"McCabe","year":"2020","journal-title":"Brief Bioinform"},{"key":"2023120408083000100_ref151","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1145\/3236386.3241340","article-title":"The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery","volume":"16","author":"Lipton","year":"2018","journal-title":"Queue"},{"key":"2023120408083000100_ref152","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad286","article-title":"HEAP: a task adaptive-based explainable deep learning framework for enhancer activity prediction","volume":"24","author":"Liu","year":"2023","journal-title":"Brief 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