{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T19:00:11Z","timestamp":1780945211047,"version":"3.54.1"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"vor","delay-in-days":38,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR-Cas9) is a revolutionary genome editing technology derived from a bacterial adaptive immune system that uses a single guide RNA (sgRNA) to direct the Cas9 enzyme to specific DNA sequences for precise genetic modifications. Its ease of use and efficiency has accelerated advancements in genetic research and therapeutic development. However, unintended cleavage at off-target sites remains a significant concern, limiting the safety and broader applicability of CRISPR-based editing. Accurate computational prediction of off-target locations is therefore essential to mitigate potential risks and improve experimental design. In this study, we introduce CRISPR multi-branch transformer fusion (CRISPR-MBTF), a novel deep learning-based framework employing a multi-branch Transformer architecture combined with an attention-based fusion mechanism to model the intricate biological context influencing CRISPR activity. By capturing subtle sequence patterns and contextual dependencies, our model achieves enhanced predictive performance compared to existing approaches. Additionally, interpretability analyses uncover biologically meaningful patterns and highlight influential sequence regions, offering valuable insights into the determinants of CRISPR specificity. This work presents a robust and interpretable tool to support the design of safer and more effective genome editing strategies.<\/jats:p>","DOI":"10.1093\/bib\/bbag216","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T11:44:29Z","timestamp":1780487069000},"source":"Crossref","is-referenced-by-count":0,"title":["CRISPR-MBTF: a multi-branch transformer fusion framework for CRISPR-Cas9 off-target prediction"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9892-0140","authenticated-orcid":false,"given":"Ali","family":"Jahangiri-Sisakht","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, University of Zanjan , Zanjan 45371-38791 ,","place":["Iran"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2872-0937","authenticated-orcid":false,"given":"Leila","family":"Safari","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, University of Zanjan , Zanjan 45371-38791 ,","place":["Iran"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7818-6794","authenticated-orcid":false,"given":"Roghayyeh","family":"Alipanahi","sequence":"additional","affiliation":[{"name":"Faculty of Pharmacy, Tabriz University of Medical Sciences , Golgasht Street, Tabriz, 5166\/15731,","place":["Iran"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,6,6]]},"reference":[{"key":"2026060814544826900_ref1","doi-asserted-by":"publisher","first-page":"e184","DOI":"10.52225\/narra.v3i2.184","article-title":"Application of CRISPR-Cas9 genome editing technology in various fields: a review","volume":"3","author":"Ansori","year":"2023","journal-title":"Narra J"},{"key":"2026060814544826900_ref2","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1038\/s41392-023-01309-7","article-title":"CRISPR\/Cas9 therapeutics: Progress and prospects","volume":"8","author":"Li","year":"2023","journal-title":"Signal Transduct Target Ther"},{"key":"2026060814544826900_ref3","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1126\/science.1225829","article-title":"A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity","volume":"337","author":"Jinek","year":"2012","journal-title":"science"},{"key":"2026060814544826900_ref4","doi-asserted-by":"publisher","first-page":"1143157","DOI":"10.3389\/fbioe.2023.1143157","article-title":"Off-target effects in CRISPR\/Cas9 gene editing","volume":"11","author":"Guo","year":"2023","journal-title":"Front Bioeng Biotechnol"},{"key":"2026060814544826900_ref5","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s41596-020-00431-y","article-title":"Tools for experimental and computational analyses of off-target editing by programmable nucleases","volume":"16","author":"Bao","year":"2021","journal-title":"Nat Protoc"},{"key":"2026060814544826900_ref6","doi-asserted-by":"publisher","first-page":"1339189","DOI":"10.3389\/fbioe.2023.1339189","article-title":"Beyond the promise: evaluating and mitigating off-target effects in CRISPR gene editing for safer therapeutics","volume":"11","author":"Lopes","year":"2024","journal-title":"Front Bioeng Biotechnol"},{"key":"2026060814544826900_ref7","doi-asserted-by":"publisher","first-page":"bbad530","DOI":"10.1093\/bib\/bbad530","article-title":"CRISPR-DIPOFF: an interpretable deep learning approach for CRISPR Cas-9 off-target prediction","volume":"25","author":"Toufikuzzaman","year":"2024","journal-title":"Brief Bioinform"},{"key":"2026060814544826900_ref8","doi-asserted-by":"publisher","first-page":"bbad131","DOI":"10.1093\/bib\/bbad131","article-title":"Using traditional machine learning and deep learning methods for on-and off-target prediction in CRISPR\/Cas9: a review","volume":"24","author":"Sherkatghanad","year":"2023","journal-title":"Brief Bioinform"},{"key":"2026060814544826900_ref9","doi-asserted-by":"publisher","first-page":"vbae184","DOI":"10.1093\/bioadv\/vbae184","article-title":"Predicting CRISPR-Cas9 off-target effects in human primary cells using bidirectional LSTM with BERT embedding. 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