{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T12:14:58Z","timestamp":1756901698415,"version":"3.44.0"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T00:00:00Z","timestamp":1756339200000},"content-version":"vor","delay-in-days":58,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82273858","U23A20530"],"award-info":[{"award-number":["82273858","U23A20530"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>RNA interference (RNAi) is a technique for precisely silencing the expression of specific genes by means of small RNA molecules and is essential in functional genomics. Among the commonly used RNAi molecules, short hairpin RNAs (shRNAs) exhibit advantages over small interfering RNAs, including longer half-life, comparable silencing efficiency, fewer off-target effects, and greater safety. However, traditional screening of potent shRNAs is costly and time-consuming. Advances in big data and artificial intelligence have enabled computational methods to significantly accelerate shRNA design and prediction. In this study, we propose BBANsh, a new shRNA prediction model based on bidirectional encoder representation from transformers (BERT) and bilinear attention network (BAN). We comprehensively evaluate the performance of BBANsh against traditional feature-based models, various feature fusion methods, and existing shRNA prediction models. The BBANsh has achieved an area under the precision\u2013recall curve of 0.951 on five-cross validation and a prediction accuracy of 0.896 on a new external validation set, highlighting its superior predictive performance. Ablation experiments validate the significant contributions of BERT and BAN to model performance. The visualization of internal feature representations intuitively demonstrates the effectiveness of the feature fusion strategy of BBANsh. Furthermore, the attentional analysis reveals that nucleotides near the 5\u2032 end have the greatest impact on model predictions, highlighting sequence characteristics of potent shRNAs. Overall, BBANsh provides an efficient and reliable tool for shRNA prediction, which can offer valuable support for researchers in the precise selection and design of shRNA.<\/jats:p>","DOI":"10.1093\/bib\/bbaf443","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T12:21:49Z","timestamp":1756383709000},"source":"Crossref","is-referenced-by-count":0,"title":["BBANsh: a deep learning architecture based on BERT and bilinear attention networks to identify potent shRNA"],"prefix":"10.1093","volume":"26","author":[{"given":"Yuanting","family":"Chen","sequence":"first","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , 130 Meilong Road, Shanghai 200237 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , 130 Meilong Road, Shanghai 200237 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinxin","family":"Yu","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , 130 Meilong Road, Shanghai 200237 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihua","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , 130 Meilong Road, Shanghai 200237 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2340-1109","authenticated-orcid":false,"given":"Yun","family":"Tang","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , 130 Meilong Road, Shanghai 200237 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9648-844X","authenticated-orcid":false,"given":"Guixia","family":"Liu","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , 130 Meilong Road, Shanghai 200237 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,8,28]]},"reference":[{"key":"2025082808214143600_ref1","doi-asserted-by":"publisher","first-page":"442","DOI":"10.4161\/cc.4.3.1520","article-title":"siRNA induced transcriptional gene silencing in mammalian cells","volume":"4","author":"Kawasaki","year":"2005","journal-title":"Cell Cycle"},{"key":"2025082808214143600_ref2","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1038\/s41587-023-02105-y","article-title":"RNA interference in the era of nucleic acid therapeutics","volume":"42","author":"Jadhav","year":"2024","journal-title":"Nat Biotechnol"},{"key":"2025082808214143600_ref3","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s10555-017-9717-6","article-title":"RNA interference-based therapy and its delivery systems","volume":"37","author":"Chen","year":"2018","journal-title":"Cancer Metastasis Rev"},{"key":"2025082808214143600_ref4","doi-asserted-by":"publisher","first-page":"1409","DOI":"10.1126\/science.1103076","article-title":"Argonaute journeys into the heart of RISC","volume":"305","author":"Sontheimer","year":"2004","journal-title":"Science"},{"key":"2025082808214143600_ref5","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1016\/j.molcel.2007.04.016","article-title":"RNA helicase a interacts with RISC in human cells and functions in RISC loading","volume":"26","author":"Robb","year":"2007","journal-title":"Mol Cell"},{"key":"2025082808214143600_ref6","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.molcel.2004.12.002","article-title":"Transcription and processing of human microRNA precursors","volume":"16","author":"Cullen","year":"2004","journal-title":"Mol Cell"},{"key":"2025082808214143600_ref7","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1038\/s41573-019-0017-4","article-title":"The current state and future directions of RNAi-based therapeutics","volume":"18","author":"Setten","year":"2019","journal-title":"Nat Rev Drug Discov"},{"key":"2025082808214143600_ref8","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1186\/1471-2105-7-520","article-title":"An accurate and interpretable model for siRNA efficacy prediction","volume":"7","author":"Vert","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2025082808214143600_ref9","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1016\/j.molcel.2011.02.008","article-title":"Functional identification of optimized RNAi triggers using a massively parallel sensor assay","volume":"41","author":"Fellmann","year":"2011","journal-title":"Mol Cell"},{"key":"2025082808214143600_ref10","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1038\/nbt.3807","article-title":"Prediction of potent shRNAs with a sequential classification algorithm","volume":"35","author":"Pelossof","year":"2017","journal-title":"Nat Biotechnol"},{"key":"2025082808214143600_ref11","doi-asserted-by":"publisher","first-page":"bbac429","DOI":"10.1093\/bib\/bbac429","article-title":"ILGBMSH: An interpretable classification model for the shRNA target prediction with ensemble learning algorithm","volume":"23","author":"Zhao","year":"2022","journal-title":"Brief Bioinform"},{"year":"2024","author":"Park","key":"2025082808214143600_ref12","doi-asserted-by":"publisher","DOI":"10.1097\/j.jcrs.0000000000001713"},{"key":"2025082808214143600_ref13","doi-asserted-by":"publisher","first-page":"3504","DOI":"10.1109\/TASLP.2021.3124365","article-title":"Pre-training with whole word masking for chinese BERT","volume":"29","author":"Cui","year":"2021","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"year":"2019","author":"Devlin","key":"2025082808214143600_ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s12028-025-02332-y"},{"key":"2025082808214143600_ref15","doi-asserted-by":"publisher","first-page":"2102","DOI":"10.1093\/bioinformatics\/btac020","article-title":"ProteinBERT: A universal deep-learning model of protein sequence and function","volume":"38","author":"Brandes","year":"2022","journal-title":"Bioinformatics"},{"key":"2025082808214143600_ref16","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1038\/s42256-024-00823-9","article-title":"A 5\u2032 UTR language model for decoding untranslated regions of mRNA and function predictions","volume":"6","author":"Chu","year":"2024","journal-title":"Nat Mach Intell"},{"key":"2025082808214143600_ref17","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1038\/s42256-024-00836-4","article-title":"Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning","volume":"6","author":"Wang","year":"2024","journal-title":"Nat Mach Intell"},{"key":"2025082808214143600_ref18","doi-asserted-by":"publisher","first-page":"3205","DOI":"10.1109\/TCBB.2023.3283985","article-title":"Prediction of multiple types of RNA modifications via biological language model","volume":"20","author":"Zhang","year":"2023","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2025082808214143600_ref19","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.biomaterials.2017.05.032","article-title":"An optimized lentiviral vector system for conditional RNAi and efficient cloning of microRNA embedded short hairpin RNA libraries","volume":"139","author":"Adams","year":"2017","journal-title":"Biomaterials"},{"key":"2025082808214143600_ref20","doi-asserted-by":"publisher","first-page":"2112","DOI":"10.1093\/bioinformatics\/btab083","article-title":"DNABERT: Pre-trained bidirectional encoder representations from transformers model for DNA-language in genome","volume":"37","author":"Ji","year":"2021","journal-title":"Bioinformatics"},{"key":"2025082808214143600_ref21","doi-asserted-by":"crossref","first-page":"gkae1310","DOI":"10.1093\/nar\/gkae1310","article-title":"GENA-LM: A family of open-source foundational models for long DNA sequences","volume":"53","author":"Fishman","year":"2025","journal-title":"Nucleic Acids Res"},{"key":"2025082808214143600_ref22","doi-asserted-by":"publisher","first-page":"1571","DOI":"10.7754\/Clin.Lab.2025.250150","volume-title":"NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Kim","year":"2018"},{"key":"2025082808214143600_ref23","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1038\/s42256-022-00605-1","article-title":"Interpretable bilinear attention network with domain adaptation improves drug\u2013target prediction","volume":"5","author":"Bai","year":"2023","journal-title":"Nat Mach Intell"},{"key":"2025082808214143600_ref24","first-page":"bbae493","article-title":"BANDRP: A bilinear attention network for anti-cancer drug response prediction based on fingerprint and multi-omics","volume":"25","author":"Cao","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025082808214143600_ref25","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.neucom.2022.06.111","article-title":"Activation functions in deep learning: A comprehensive survey and benchmark","volume":"503","author":"Dubey","year":"2022","journal-title":"Neurocomputing"},{"year":"2017","author":"Kingma","key":"2025082808214143600_ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s00392-025-02708-2"},{"key":"2025082808214143600_ref27","doi-asserted-by":"publisher","first-page":"1270","DOI":"10.3934\/mbe.2024054","article-title":"The WuC-Adam algorithm based on joint improvement of warmup and cosine annealing algorithms","volume":"21","author":"Zhang","year":"2024","journal-title":"Math Biosci Eng"},{"key":"2025082808214143600_ref28","doi-asserted-by":"publisher","first-page":"e60","DOI":"10.1093\/nar\/gkab122","article-title":"iLearnPlus: A comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization","volume":"49","author":"Chen","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2025082808214143600_ref29","doi-asserted-by":"publisher","first-page":"W65","DOI":"10.1093\/nar\/gkv458","article-title":"Pse-in-one: A web server for generating various modes of pseudo components of DNA, RNA, and protein sequences","volume":"43","author":"Liu","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2025082808214143600_ref30","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1006\/jmbi.1996.0503","article-title":"Nucleosome DNA sequence pattern revealed by multiple alignment of experimentally mapped sequences","volume":"262","author":"Ioshikhes","year":"1996","journal-title":"J Mol Biol"},{"key":"2025082808214143600_ref31","doi-asserted-by":"publisher","first-page":"10938","DOI":"10.1039\/d5cp02409a","volume-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Wei","year":"2020"},{"year":"2023","author":"Liu","key":"2025082808214143600_ref32","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpca.5c04400"},{"year":"2023","author":"Shen","key":"2025082808214143600_ref33","doi-asserted-by":"publisher","DOI":"10.1039\/d5cc02777b"},{"year":"2017","author":"Lu","key":"2025082808214143600_ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.micron.2025.103893"},{"key":"2025082808214143600_ref35","doi-asserted-by":"publisher","first-page":"E3384","DOI":"10.1073\/pnas.1508821112","article-title":"Next-generation libraries for robust RNA interference-based genome-wide screens","volume":"112","author":"Kampmann","year":"2015","journal-title":"Proc Natl Acad Sci"},{"author":"Broad institute. TRC shRNA design","key":"2025082808214143600_ref36"},{"key":"2025082808214143600_ref37","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.cell.2008.02.034","article-title":"Sorting of small RNAs into arabidopsis argonaute complexes is directed by the 5\u2032 terminal nucleotide","volume":"133","author":"Mi","year":"2008","journal-title":"Cell"},{"key":"2025082808214143600_ref38","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1038\/s42256-024-00946-z","article-title":"An interpretable RNA foundation model for exploring functional RNA motifs in plants","volume":"6","author":"Yu","year":"2024","journal-title":"Nat Mach Intell"},{"key":"2025082808214143600_ref39","doi-asserted-by":"publisher","first-page":"e3","DOI":"10.1093\/nar\/gkad1031","article-title":"Multiple sequence alignment-based RNA language model and its application to structural inference","volume":"52","author":"Zhang","year":"2023","journal-title":"Nucleic Acids Res"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/4\/bbaf443\/64147848\/bbaf443.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/4\/bbaf443\/64147848\/bbaf443.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T12:21:50Z","timestamp":1756383710000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf443\/8242584"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,7,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf443","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"type":"print","value":"1467-5463"},{"type":"electronic","value":"1477-4054"}],"subject":[],"published-other":{"date-parts":[[2025,7]]},"published":{"date-parts":[[2025,7]]},"article-number":"bbaf443"}}