{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T19:47:00Z","timestamp":1784317620986,"version":"3.55.0"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T00:00:00Z","timestamp":1748476800000},"content-version":"vor","delay-in-days":28,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Scientific Research Start-up Funds","award":["QD2021005N"],"award-info":[{"award-number":["QD2021005N"]}]},{"name":"Shenzhen Science and Technology Program","award":["WDZC20220819134430002"],"award-info":[{"award-number":["WDZC20220819134430002"]}]},{"name":"Shenzhen Universities Stable Funding Key Projects","award":["WDZC20200821104802001"],"award-info":[{"award-number":["WDZC20200821104802001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Identification of T cell receptor (TCR) specificities for antigens from large-scale single-cell or bulk TCR repertoire data plays a vital role in disease diagnosis and immunotherapy. In silico prediction models have emerged in recent years. However, the generalizability and transferability of current computational models remain significant hurdles in accurately predicting TCR\u2013pMHC binding specificity, primarily due to the limited availability of experimental data and the vast diversity of TCR sequences. In this paper, we propose a lightweight contrastive TCR\u2013pMHC learning with context-aware prompts, named LightCTL, to infer TCR\u2013pMHC binding specificity. For each TCR and peptide-MHC sequence, we utilize a TCR encoding module and a pMHC encoding module to transform them into latent representations. Specifically, we introduce a contrastive TCR\u2013pMHC learning paradigm to enhance the generalization ability of TCR\u2013pMHC binding specificity prediction by learning the matching relationship between TCR\u2013pMHC and MHC-peptide. We fuse the TCR and pMHC latent representations and employ a novel context-aware prompt module to consider the varying importance of different feature maps. Compared with existing methods, LightCTL substantially improves the accuracy of predicting TCR\u2013pMHC binding specificity. Moreover, comparative experiments across eight independent datasets demonstrate the generalization ability of LightCTL, showing superior performance for predicting unknown TCR\u2013pMHC pairs. Finally, we assess LightCTL's efficacy across different TCR sequence lengths and distinct unseen epitopes, as well as estimate cytomegalovirus-specific TCR diversity and clone frequency from peripheral TCR repertoire data. Overall, our findings highlight LightCTL as a versatile analytical method for advancing novel T-cell therapies and identifying novel biomarkers for disease diagnosis.<\/jats:p>","DOI":"10.1093\/bib\/bbaf246","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T13:34:40Z","timestamp":1748525680000},"source":"Crossref","is-referenced-by-count":4,"title":["LightCTL: lightweight contrastive TCR-pMHC specificity learning with context-aware prompt"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9512-3625","authenticated-orcid":false,"given":"Fei","family":"Ye","sequence":"first","affiliation":[{"name":"Institute of Biopharmaceutical and Health Engineering , Tsinghua ShenZhen International Graduate School, Tsinghua University, Lishui Road, Nanshan District, Shenzhen, Guangdong Province 518055,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mao","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Biopharmaceutical and Health Engineering , Tsinghua ShenZhen International Graduate School, Tsinghua University, Lishui Road, Nanshan District, Shenzhen, Guangdong Province 518055,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Biopharmaceutical and Health Engineering , Tsinghua ShenZhen International Graduate School, Tsinghua University, Lishui Road, Nanshan District, Shenzhen, Guangdong Province 518055,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruihao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Biopharmaceutical and Health Engineering , Tsinghua ShenZhen International Graduate School, Tsinghua University, Lishui Road, Nanshan District, Shenzhen, Guangdong Province 518055,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuqi","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Biopharmaceutical and Health Engineering , Tsinghua ShenZhen International Graduate School, Tsinghua University, Lishui Road, Nanshan District, Shenzhen, Guangdong Province 518055,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiuyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Biopharmaceutical and Health Engineering , Tsinghua ShenZhen International Graduate School, Tsinghua University, Lishui Road, Nanshan District, Shenzhen, Guangdong Province 518055,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanyang","family":"Han","sequence":"additional","affiliation":[{"name":"Institute of Biopharmaceutical and Health Engineering , Tsinghua ShenZhen International Graduate School, Tsinghua University, Lishui Road, Nanshan District, Shenzhen, Guangdong Province 518055,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lan","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute of Biopharmaceutical and Health Engineering , Tsinghua ShenZhen International Graduate School, Tsinghua University, Lishui Road, Nanshan District, Shenzhen, Guangdong Province 518055,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Biopharmaceutical and Health Engineering , Tsinghua ShenZhen International Graduate School, Tsinghua University, Lishui Road, Nanshan District, Shenzhen, Guangdong Province 518055,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"key":"2025052909343494800_ref1","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM55620.2022.9994875","article-title":"Pretraining transformers for TCR-pMHC binding prediction","volume-title":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Shang","year":"2022"},{"key":"2025052909343494800_ref2","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1111\/imr.12253","article-title":"Cd1 and mycobacterial lipids activate human T cells","volume":"264","author":"Van Rhijn","year":"2015","journal-title":"Immunol Rev"},{"key":"2025052909343494800_ref3","doi-asserted-by":"publisher","first-page":"16161","DOI":"10.1073\/pnas.1212755109","article-title":"Statistical inference of the generation probability of T-cell receptors from sequence repertoires","volume":"109","author":"Murugan","year":"2012","journal-title":"Proc Natl Acad Sci"},{"key":"2025052909343494800_ref4","doi-asserted-by":"publisher","first-page":"3211","DOI":"10.1038\/s41467-024-47461-8","article-title":"Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells","volume":"15","author":"Croce","year":"2024","journal-title":"Nat Commun"},{"key":"2025052909343494800_ref5","doi-asserted-by":"publisher","first-page":"1168","DOI":"10.1074\/jbc.M111.289488","article-title":"A single autoimmune T cell receptor recognizes more than a million different peptides","volume":"287","author":"Wooldridge","year":"2012","journal-title":"J Biol Chem"},{"key":"2025052909343494800_ref6","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1146\/annurev-immunol-032414-112334","article-title":"T cell antigen receptor recognition of antigen-presenting molecules","volume":"33","author":"Rossjohn","year":"2015","journal-title":"Annu Rev Immunol"},{"key":"2025052909343494800_ref7","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1126\/science.274.5284.94","article-title":"Phenotypic analysis of antigen-specific t lymphocytes","volume":"274","author":"Altman","year":"1996","journal-title":"Science"},{"key":"2025052909343494800_ref8","doi-asserted-by":"publisher","first-page":"1156","DOI":"10.1038\/nbt.4282","article-title":"High-throughput determination of the antigen specificities of T cell receptors in single cells","volume":"36","author":"Zhang","year":"2018","journal-title":"Nat Biotechnol"},{"key":"2025052909343494800_ref9","doi-asserted-by":"publisher","first-page":"1016","DOI":"10.1016\/j.cell.2019.07.009","article-title":"T-Scan: a genome-wide method for the systematic discovery of T cell epitopes","volume":"178","author":"Kula","year":"2019","journal-title":"Cell"},{"key":"2025052909343494800_ref10","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1038\/s41592-022-01578-0","article-title":"VDJdb in the pandemic era: a compendium of T cell receptors specific for SARS-CoV-2","volume":"19","author":"Goncharov","year":"2022","journal-title":"Nat Methods"},{"key":"2025052909343494800_ref11","doi-asserted-by":"publisher","first-page":"2924","DOI":"10.1093\/bioinformatics\/btx286","article-title":"McPAS-TCR: a manually curated catalogue of pathology-associated T cell receptor sequences","volume":"33","author":"Tickotsky","year":"2017","journal-title":"Bioinformatics"},{"key":"2025052909343494800_ref12","doi-asserted-by":"publisher","first-page":"D436","DOI":"10.1093\/nar\/gkae1092","article-title":"The immune epitope database (IEDB): 2024 update","volume":"53","author":"Vita","year":"2025","journal-title":"Nucleic Acids Res"},{"key":"2025052909343494800_ref13","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1093\/bioinformatics\/btz614","article-title":"PIRD: Pan immune repertoire database","volume":"36","author":"Zhang","year":"2020","journal-title":"Bioinformatics"},{"key":"2025052909343494800_ref14","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1038\/nature22976","article-title":"Identifying specificity groups in the T cell receptor repertoire","volume":"547","author":"Glanville","year":"2017","journal-title":"Nature"},{"key":"2025052909343494800_ref15","doi-asserted-by":"publisher","first-page":"e1008814","DOI":"10.1371\/journal.pcbi.1008814","article-title":"Predicting recognition between T cell receptors and epitopes with TCRGP","volume":"17","author":"Jokinen","year":"2021","journal-title":"PLoS Comput Biol"},{"key":"2025052909343494800_ref16","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/s00251-017-1023-5","article-title":"On the feasibility of mining cd8+ T cell receptor patterns underlying immunogenic peptide recognition","volume":"70","author":"De Neuter","year":"2018","journal-title":"Immunogenetics"},{"key":"2025052909343494800_ref17","doi-asserted-by":"publisher","first-page":"btad284","DOI":"10.1093\/bioinformatics\/btad284","article-title":"epiTCR: a highly sensitive predictor for TCR\u2013peptide binding","volume":"39","author":"Pham","year":"2023","journal-title":"Bioinformatics"},{"key":"2025052909343494800_ref18","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1038\/s41592-020-01020-3","article-title":"Mapping the functional landscape of T cell receptor repertoires by single-T cell transcriptomics","volume":"18","author":"Zhang","year":"2021","journal-title":"Nat Methods"},{"key":"2025052909343494800_ref19","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1038\/s43588-021-00076-1","article-title":"Rapid assessment of T-cell receptor specificity of the immune repertoire","volume":"1","author":"Lin","year":"2021","journal-title":"Nat Comput Sci"},{"key":"2025052909343494800_ref20","doi-asserted-by":"publisher","DOI":"10.1101\/2022.06.01.494331","article-title":"Automated protein-protein structure prediction of the T cell receptor-peptide major histocompatibility complex","author":"Rollins","year":"2022","journal-title":"bioRxiv"},{"key":"2025052909343494800_ref21","doi-asserted-by":"publisher","first-page":"e82813","DOI":"10.7554\/eLife.82813","article-title":"Structure-based prediction of T cell receptor: peptide-MHC interactions","volume":"12","author":"Bradley","year":"2023","journal-title":"eLife"},{"key":"2025052909343494800_ref22","doi-asserted-by":"publisher","first-page":"W396","DOI":"10.1093\/nar\/gky432","article-title":"TCRmodel: high resolution modeling of T cell receptors from sequence","volume":"46","author":"Gowthaman","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2025052909343494800_ref23","doi-asserted-by":"publisher","first-page":"W569","DOI":"10.1093\/nar\/gkad356","article-title":"TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning","volume":"51","author":"Yin","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2025052909343494800_ref24","article-title":"DeepAIR: a deep-learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis","volume":"9","author":"Zhao","journal-title":"Science Advances"},{"key":"2025052909343494800_ref25","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1038\/s42256-023-00634-4","article-title":"Characterizing the interaction conformation between T-cell receptors and epitopes with deep learning","volume":"5","author":"Peng","year":"2023","journal-title":"Nat Mach Intell"},{"key":"2025052909343494800_ref26","doi-asserted-by":"publisher","first-page":"433706","DOI":"10.1101\/433706","article-title":"NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks","author":"Jurtz","year":"2018","journal-title":"bioRxiv"},{"key":"2025052909343494800_ref27","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1038\/s42256-021-00383-2","article-title":"Deep learning-based prediction of the T cell receptor\u2013antigen binding specificity. Nature","volume":"3","author":"Tianshi","year":"2021","journal-title":"Mach Intell"},{"key":"2025052909343494800_ref28","doi-asserted-by":"publisher","first-page":"i237","DOI":"10.1093\/bioinformatics\/btab294","article-title":"TITAN: T-cell receptor specificity prediction with bimodal attention networks","volume":"37","author":"Weber","year":"2021","journal-title":"Bioinformatics"},{"key":"2025052909343494800_ref29","doi-asserted-by":"publisher","first-page":"bbab335","DOI":"10.1093\/bib\/bbab335","article-title":"DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor","volume":"22","author":"Xu","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025052909343494800_ref30","doi-asserted-by":"crossref","first-page":"942491","DOI":"10.3389\/fgene.2022.942491","article-title":"AttnTAP: a dual-input framework incorporating the attention mechanism for accurately predicting TCR-peptide binding","volume":"13","author":"Ying","year":"2022","journal-title":"Front Genet"},{"key":"2025052909343494800_ref31","doi-asserted-by":"publisher","first-page":"bbad202","DOI":"10.1093\/bib\/bbad202","article-title":"MITNet: a fusion transformer and convolutional neural network architecture approach for T-cell epitope prediction","volume":"24","author":"Darmawan","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025052909343494800_ref32","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.molimm.2023.03.010","article-title":"TPBTE: a model based on convolutional transformer for predicting the binding of TCR to epitope","volume":"157","author":"Jie","year":"2023","journal-title":"Mol Immunol"},{"key":"2025052909343494800_ref33","doi-asserted-by":"publisher","first-page":"000325","DOI":"10.1109\/SAMI58000.2023.10044509","article-title":"Analyzing immunomes using sequence embedding and network analysis","volume-title":"2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI)","author":"Motuzenko","year":"2023"},{"key":"2025052909343494800_ref34","doi-asserted-by":"publisher","first-page":"bbad191","DOI":"10.1093\/bib\/bbad191","article-title":"SC-AIR-BERT: a pre-trained single-cell model for predicting the antigen-binding specificity of the adaptive immune receptor","volume":"24","author":"Zhao","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025052909343494800_ref35","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac378","article-title":"Attention-aware contrastive learning for predicting T cell receptor\u2013antigen binding specificity","volume":"23","author":"Fang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025052909343494800_ref36","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1038\/s42256-023-00619-3","article-title":"Pan-peptide meta learning for T-cell receptor\u2013antigen binding recognition","volume":"5","author":"Gao","year":"2023","journal-title":"Nat Mach Intell"},{"key":"2025052909343494800_ref37","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM58861.2023.10385479","article-title":"Multimodal-AIR-BERT: a multimodal pre-trained model for antigen specificity prediction in adaptive immune receptors","volume-title":"2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Xiao"},{"key":"2025052909343494800_ref38","doi-asserted-by":"publisher","first-page":"e796","DOI":"10.1371\/journal.pone.0000796","article-title":"NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and-B locus protein of known sequence","volume":"2","author":"Nielsen","year":"2007","journal-title":"PloS One"},{"key":"2025052909343494800_ref39","doi-asserted-by":"publisher","first-page":"12704","DOI":"10.1073\/pnas.1809642115","article-title":"Precise tracking of vaccine-responding T cell clones reveals convergent and personalized response in identical twins","volume":"115","author":"Pogorelyy","year":"2018","journal-title":"Proc Natl Acad Sci"},{"key":"2025052909343494800_ref40","year":"2020"},{"key":"2025052909343494800_ref41","article-title":"A large-scale database of T-cell receptor beta (TCR$\\beta $) sequences and binding associations from natural and synthetic exposure to SARS-Cov-2","author":"Nolan","year":"2020","journal-title":"Res Sq"},{"key":"2025052909343494800_ref42","first-page":"eabk3070","article-title":"Allelic variation in class I HLA determines cd8+ T cell repertoire shape and cross-reactive memory responses to SARS-CoV-2","volume":"7","author":"Francis","year":"2022","journal-title":"Sci Immunol"},{"key":"2025052909343494800_ref43","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"2025052909343494800_ref44","doi-asserted-by":"crossref","first-page":"2907","DOI":"10.1109\/TCBB.2021.3098126","article-title":"MCA-Net: multi-feature coding and attention convolutional neural network for predicting lncRNA\u2013disease association","volume":"19","author":"Zhang","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2025052909343494800_ref45","article-title":"Attention is all you need","volume-title":"Advances in Neural Information Processing Systems 30 (NIPS 2017)","author":"Vaswani"},{"key":"2025052909343494800_ref46","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1038\/s42256-024-00901-y","article-title":"Sliding-attention transformer neural architecture for predicting T cell receptor\u2013antigen\u2013human leucocyte antigen binding","volume":"6","author":"Feng","year":"2024","journal-title":"Nat Mach Intell"},{"key":"2025052909343494800_ref47","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad436","article-title":"Accurate TCR-pMHC interaction prediction using a BERT-based transfer learning method","volume":"25","author":"Zhang","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025052909343494800_ref48","doi-asserted-by":"crossref","DOI":"10.1038\/s42256-024-00913-8","article-title":"Epitope-anchored contrastive transfer learning for paired cd8+ T cell receptor\u2013antigen recognition","volume":"6","author":"Zhang","year":"2024","journal-title":"Nat Mach Intell"},{"key":"2025052909343494800_ref49","doi-asserted-by":"publisher","first-page":"D406","DOI":"10.1093\/nar\/gkx971","article-title":"STCRDab: the structural T-cell receptor database","volume":"46","author":"Leem","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2025052909343494800_ref50","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/ng.3822","article-title":"Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire","volume":"49","author":"Emerson","year":"2017","journal-title":"Nat Genet"},{"key":"2025052909343494800_ref51","doi-asserted-by":"publisher","first-page":"108408","DOI":"10.1016\/j.compbiomed.2024.108408","article-title":"Sa-TTCA: an SVM-based approach for tumor T-cell antigen classification using features extracted from biological sequencing and natural language processing","volume":"174","author":"Tran","year":"2024","journal-title":"Comput Biol Med"},{"key":"2025052909343494800_ref52","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-024-02420-y","article-title":"A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy","author":"Huber","year":"2024","journal-title":"Nat Biotechnol"},{"key":"2025052909343494800_ref53","doi-asserted-by":"crossref","first-page":"bbae595","DOI":"10.1093\/bib\/bbae595","article-title":"CapHLA: a comprehensive tool to predict peptide presentation and binding to HLA class I and class II","volume":"26","author":"Chang","year":"2025","journal-title":"Brief Bioinform"},{"key":"2025052909343494800_ref54","doi-asserted-by":"publisher","DOI":"10.1038\/s42003-024-07292-1","article-title":"Geometric deep learning improves generalizability of MHC-bound peptide predictions","volume":"7","author":"Marzella","year":"2024","journal-title":"Commun Biol"},{"key":"2025052909343494800_ref55","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proceedings of the 38th International Conference on Machine Learning (ICML 2021).","author":"Radford"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/3\/bbaf246\/63398247\/bbaf246.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/3\/bbaf246\/63398247\/bbaf246.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T13:34:43Z","timestamp":1748525683000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf246\/8152768"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,1]]},"references-count":55,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,5,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf246","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,5]]},"published":{"date-parts":[[2025,5,1]]},"article-number":"bbaf246"}}