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Identifying enhancers is crucial for understanding genomic regulatory mechanisms, pinpointing key elements and investigating networks governing gene expression and disease-related mechanisms. Existing enhancer identification methods exhibit limitations, prompting the development of our novel multi-input deep learning framework, termed Enhancer-MDLF. Experimental results illustrate that Enhancer-MDLF outperforms the previous method, Enhancer-IF, across eight distinct human cell lines and exhibits superior performance on generic enhancer datasets and enhancer\u2013promoter datasets, affirming the robustness of Enhancer-MDLF. Additionally, we introduce transfer learning to provide an effective and potential solution to address the prediction challenges posed by enhancer specificity. Furthermore, we utilize model interpretation to identify transcription factor binding site motifs that may be associated with enhancer regions, with important implications for facilitating the study of enhancer regulatory mechanisms. The source code is openly accessible at https:\/\/github.com\/HaoWuLab-Bioinformatics\/Enhancer-MDLF.<\/jats:p>","DOI":"10.1093\/bib\/bbae083","type":"journal-article","created":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T13:02:08Z","timestamp":1710421328000},"source":"Crossref","is-referenced-by-count":23,"title":["Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4698-7786","authenticated-orcid":false,"given":"Yao","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Software, Shandong University , Jinan, 250100, Shandong , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8696-4983","authenticated-orcid":false,"given":"Pengyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&F University , Yangling, 712100, Shaanxi , 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