{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li Z"],"funding":["China National Key R&D Program","Ministry of Science and Technology","National Natural Science Foundation of China","China National Key R&D Program"],"pagination":["13447-13468"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11602155"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["52(21)"],"pubmed_abstract":["Enhancers play a critical role in dynamically regulating spatial-temporal gene expression and establishing cell identity, underscoring the significance of designing them with specific properties for applications in biosynthetic engineering and gene therapy. Despite numerous high-throughput methods facilitating genome-wide enhancer identification, deciphering the sequence determinants of their activity remains challenging. Here, we present the DREAM (DNA cis-Regulatory Elements with controllable Activity design platforM) framework, a novel deep learning-based approach for synthetic enhancer design. Proficient in uncovering subtle and intricate patterns within extensive enhancer screening data, DREAM achieves cutting-edge sequence-based enhancer activity prediction and highlights critical se"],"journal":["Nucleic acids research"],"pubmed_title":["A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity."],"pmcid":["PMC11602155"],"funding_grant_id":["2021YFF1200500","20221250020","20181300988","32070595","20201300797"],"pubmed_authors":["Zhu Y","Cao J","Ma X","Liu Q","Liu R","Wang G","Wang H","Wang J","Zhang Q","Qin S","Shao C","Wang N","Liu Y","Yao Y","Ni JQ","Chen Y","Chen X","Xu L","Tang Z","Li J","De B","Bao Y","Zhu F","Sun T","Xie L","Chen C","Deng B","Zuo E","Li Z","Hong Y","Li T","Zhang Y","Peng B","Mei C","Liu B","Liu G"],"additional_accession":[]},"is_claimable":false,"name":"A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity.","description":"Enhancers play a critical role in dynamically regulating spatial-temporal gene expression and establishing cell identity, underscoring the significance of designing them with specific properties for applications in biosynthetic engineering and gene therapy. Despite numerous high-throughput methods facilitating genome-wide enhancer identification, deciphering the sequence determinants of their activity remains challenging. Here, we present the DREAM (DNA cis-Regulatory Elements with controllable Activity design platforM) framework, a novel deep learning-based approach for synthetic enhancer design. Proficient in uncovering subtle and intricate patterns within extensive enhancer screening data, DREAM achieves cutting-edge sequence-based enhancer activity prediction and highlights critical se","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Nov","modification":"2026-06-01T10:00:29.998Z","creation":"2025-04-04T00:43:56.86Z"},"accession":"S-EPMC11602155","cross_references":{"pubmed":["39420601"],"doi":["10.1093/nar/gkae912"]}}