{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:45Z","timestamp":1772138025759,"version":"3.50.1"},"reference-count":64,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T00:00:00Z","timestamp":1730851200000},"content-version":"vor","delay-in-days":44,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004794","name":"Centre National de la Recherche Scientifique","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004794","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The accurate prediction of peptide-major histocompatibility complex (MHC) class I binding probabilities is a critical endeavor in immunoinformatics, with broad implications for vaccine development and immunotherapies. While recent deep neural network based approaches have showcased promise in peptide-MHC (pMHC) prediction, they have two shortcomings: (i) they rely on hand-crafted pseudo-sequence extraction, (ii) they do not generalize well to different datasets, which limits the practicality of these approaches. While existing methods rely on a 34 amino acid pseudo-sequence, our findings uncover the involvement of 147 positions in direct interactions between MHC and peptide. We further show that neural architectures can learn the intricacies of pMHC binding using even full sequences. To this end, we present PerceiverpMHC that is able to learn accurate representations on full-sequences by leveraging efficient transformer based architectures. Additionally, we propose IMGT\/RobustpMHC that harnesses the potential of unlabeled data in improving the robustness of pMHC binding predictions through a self-supervised learning strategy. We extensively evaluate RobustpMHC on eight different datasets and showcase an overall improvement of over 6% in binding prediction accuracy compared to state-of-the-art approaches. We compile CrystalIMGT, a crystallography-verified dataset presenting a challenge to existing approaches due to significantly different pMHC distributions. Finally, to mitigate this distribution gap, we further develop a transfer learning pipeline.<\/jats:p>","DOI":"10.1093\/bib\/bbae552","type":"journal-article","created":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T12:05:26Z","timestamp":1728993926000},"source":"Crossref","is-referenced-by-count":5,"title":["IMGT\/RobustpMHC: robust training for class-I MHC peptide binding prediction"],"prefix":"10.1093","volume":"25","author":[{"given":"Anjana","family":"Kushwaha","sequence":"first","affiliation":[{"name":"IMGT\u00ae , The International ImMunoGeneTics Information System\u00ae, Montpellier,","place":["France"]},{"name":"Institute of Human Genetics (IGH) , Montpellier,","place":["France"]},{"name":"University of Montpellier (UM) , Montpellier,","place":["France"]},{"name":"National Center for Scientific Research (CNRS) ,","place":["France"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrice","family":"Duroux","sequence":"additional","affiliation":[{"name":"IMGT\u00ae , The International ImMunoGeneTics Information System\u00ae, Montpellier,","place":["France"]},{"name":"Institute of Human Genetics (IGH) , Montpellier,","place":["France"]},{"name":"National Center for Scientific Research (CNRS) ,","place":["France"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V\u00e9ronique","family":"Giudicelli","sequence":"additional","affiliation":[{"name":"IMGT\u00ae , The International ImMunoGeneTics Information System\u00ae, Montpellier,","place":["France"]},{"name":"Institute of Human Genetics (IGH) , Montpellier,","place":["France"]},{"name":"University of Montpellier (UM) , Montpellier,","place":["France"]},{"name":"National Center for Scientific Research (CNRS) ,","place":["France"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konstantin","family":"Todorov","sequence":"additional","affiliation":[{"name":"LIRMM , Laboratoire d\u2019Informatique, de Robotique et de Micro\u00e9lectronique de Montpellier, Montpellier,","place":["France"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sofia","family":"Kossida","sequence":"additional","affiliation":[{"name":"IMGT\u00ae , The International ImMunoGeneTics Information System\u00ae, Montpellier,","place":["France"]},{"name":"Institute of Human Genetics (IGH) , Montpellier,","place":["France"]},{"name":"University of Montpellier (UM) , Montpellier,","place":["France"]},{"name":"National Center for Scientific Research (CNRS) ,","place":["France"]},{"name":"Institut Universitaire de France (IUF) , Paris,","place":["France"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"key":"2024110623173488200_ref1","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1146\/annurev.immunol.17.1.51","article-title":"Immunodominance in major histocompatibility complex class I\u2013restricted T lymphocyte responses","volume":"17","author":"Yewdell","year":"1999","journal-title":"Annu Rev Immunol"},{"key":"2024110623173488200_ref2","doi-asserted-by":"publisher","first-page":"110721","DOI":"10.1016\/j.intimp.2023.110721","article-title":"Improving the efficacy of peptide vaccines in cancer immunotherapy","volume":"123","author":"Zahedipour","year":"2023","journal-title":"Int Immunopharmacol"},{"key":"2024110623173488200_ref3","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1038\/s41591-021-01643-9","article-title":"A guide to immunotherapy for Covid-19","volume":"28","author":"van de Veerdonk","year":"2022","journal-title":"Nat Med"},{"key":"2024110623173488200_ref4","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1038\/334395a0","article-title":"T-cell antigen receptor genes and t-cell recognition","volume":"334","author":"Davis","year":"1988","journal-title":"Nature"},{"key":"2024110623173488200_ref5","doi-asserted-by":"publisher","first-page":"335","DOI":"10.3389\/fimmu.2015.00335","article-title":"Intracellular transport routes for MHC I and their relevance for antigen cross-presentation","volume":"6","author":"Adiko","year":"2015","journal-title":"Front Immunol"},{"key":"2024110623173488200_ref6","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1111\/j.1600-065X.1987.tb00521.x","article-title":"Antigen presentation pathways to class I and class II MHC-restricted T lymphocytes","volume":"98","author":"Braciale","year":"1987","journal-title":"Immunol Rev"},{"key":"2024110623173488200_ref7","volume-title":"Immunobiology: The Immune System in Health and Disease"},{"key":"2024110623173488200_ref8","doi-asserted-by":"publisher","journal-title":"Cold Spring Harbor Protocols 2011, pdb\u2013prot5637","DOI":"10.1111\/j.1744-313X.2008.00765.x"},{"key":"2024110623173488200_ref9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-8-238","article-title":"Prediction of MHC class II binding affinity using smm-align, a novel stabilization matrix alignment method","volume":"8","author":"Nielsen","year":"2007","journal-title":"BMC Bioinform"},{"key":"2024110623173488200_ref10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1472-6807-11-32","article-title":"Peptide binding prediction for the human class II MHC allele HLA-DP2: a molecular docking approach","volume":"11","author":"Patronov","year":"2011","journal-title":"BMC Struct Biol"},{"key":"2024110623173488200_ref11","doi-asserted-by":"crossref","first-page":"gix017","DOI":"10.1093\/gigascience\/gix017","article-title":"PSSMHCcpan: a novel PSSM-based software for predicting class I peptide-hla binding affinity","volume":"6","author":"Liu","year":"2017","journal-title":"Giga Sci"},{"key":"2024110623173488200_ref12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2013\/467852","article-title":"Evaluating the immunogenicity of protein drugs by applying in vitro MHC binding data and the immune epitope database and analysis resource","volume":"2013","author":"Paul","year":"2013","journal-title":"Clin Dev Immunol"},{"key":"2024110623173488200_ref13","doi-asserted-by":"publisher","first-page":"2047","DOI":"10.3389\/fimmu.2019.02047","article-title":"Structure based prediction of neoantigen immunogenicity","volume":"10","author":"Riley","year":"2019","journal-title":"Front Immunol"},{"key":"2024110623173488200_ref14","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.autrev.2011.02.003","article-title":"Predicting peptide binding to major histocompatibility complex molecules","volume":"10","author":"Liao","year":"2011","journal-title":"Autoimmun Rev"},{"key":"2024110623173488200_ref15","doi-asserted-by":"publisher","first-page":"e227","DOI":"10.1093\/bioinformatics\/btl255","article-title":"Learning MHC i\u2013peptide binding","volume":"22","author":"Jojic","year":"2006","journal-title":"Bioinformatics"},{"key":"2024110623173488200_ref16","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1110\/ps.0239403","article-title":"Reliable prediction of t-cell epitopes using neural networks with novel sequence representations","volume":"12","author":"Nielsen","year":"2003","journal-title":"Protein Sci"},{"key":"2024110623173488200_ref17","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":"2024110623173488200_ref18","volume-title":"IMGT\/3Dstructure-DB: Querying the IMGT Database for 3D Structures in Immunology and Immunoinformatics (IG or Antibodies, TR, MH, RPI, and FPIA), Cold Spring Harbor Protocols 2011, pdb\u2013prot5637","author":"Ehrenmann","year":"2011"},{"key":"2024110623173488200_ref19","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1093\/nar\/28.1.235","article-title":"The protein data bank","volume":"28","author":"Berman","year":"2000","journal-title":"Nucleic Acids Res"},{"key":"2024110623173488200_ref20","doi-asserted-by":"publisher","first-page":"W449","DOI":"10.1093\/nar\/gkaa379","article-title":"NetMHCpan-4.1 and netMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data","volume":"48","author":"Reynisson","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2024110623173488200_ref21","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1038\/s42256-022-00459-7","article-title":"A transformer-based model to predict peptide\u2013hla class I binding and optimize mutated peptides for vaccine design","volume":"4","author":"Chu","year":"2022","journal-title":"Nat Mach Intell"},{"key":"2024110623173488200_ref22","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1038\/s42003-023-04867-2","article-title":"Capsnet-MHC predicts peptide-MHC class I binding based on capsule neural networks","volume":"6","author":"Kalemati","year":"2023","journal-title":"Commun Biol"},{"key":"2024110623173488200_ref23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s42256-023-00694-6","article-title":"Deep neural networks predict class I major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicity","volume":"5","author":"Albert","year":"2023","journal-title":"Nat Mach Intell"},{"key":"2024110623173488200_ref24","first-page":"1","article-title":"DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I hla-peptide binding affinity prediction","volume":"9","author":"Liu","year":"2019","journal-title":"Sci Rep"},{"key":"2024110623173488200_ref25","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1002\/prot.26065","article-title":"Deep learning pan-specific model for interpretable MHC-I peptide binding prediction with improved attention mechanism, proteins","volume":"89","author":"Jin","year":"2021","journal-title":"Struct Funct Bioinform"},{"key":"2024110623173488200_ref26","article-title":"Perceiver IO: a general architecture for structured inputs & outputs","author":"Jaegle","year":"2021"},{"key":"2024110623173488200_ref27","doi-asserted-by":"crossref","first-page":"292","DOI":"10.3389\/fimmu.2017.00292","article-title":"Major histocompatibility complex (MHC) class I and MHC class II proteins: Conformational plasticity in antigen presentation","volume":"8","author":"Wieczorek","year":"2017","journal-title":"Front Immunol"},{"key":"2024110623173488200_ref28","doi-asserted-by":"publisher","first-page":"bbaa415","DOI":"10.1093\/bib\/bbaa415","article-title":"Anthem: a user customised tool for fast and accurate prediction of binding between peptides and hla class I molecules","volume":"22","author":"Mei","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024110623173488200_ref29","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1158\/2326-6066.CIR-18-0584","article-title":"Performance evaluation of MHC class-I binding prediction tools based on an experimentally validated MHC\u2013peptide binding data set","volume":"7","author":"Bonsack","year":"2019","journal-title":"Cancer Immunol Res"},{"key":"2024110623173488200_ref30","volume-title":"Immunoinformatics: Predicting Immunogenicity In Silico"},{"key":"2024110623173488200_ref31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-021-04155-y","article-title":"DeepNetBim: deep learning model for predicting hla-epitope interactions based on network analysis by harnessing binding and immunogenicity information","volume":"22","author":"Yang","year":"2021","journal-title":"BMC Bioinform"},{"key":"2024110623173488200_ref32","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1093\/bioinformatics\/btp137","article-title":"The pickpocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding","volume":"25","author":"Zhang","year":"2009","journal-title":"Bioinformatics"},{"key":"2024110623173488200_ref33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1745-7580-4-2","article-title":"Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries","volume":"4","author":"Sidney","year":"2008","journal-title":"Immunome Res"},{"key":"2024110623173488200_ref34","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/s00251-011-0579-8","article-title":"NetMHCcons: a consensus method for the major histocompatibility complex class I predictions","volume":"64","author":"Karosiene","year":"2012","journal-title":"Immunogenetics"},{"key":"2024110623173488200_ref35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-10-394","article-title":"Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior","volume":"10","author":"Kim","year":"2009","journal-title":"BMC Bioinform"},{"key":"2024110623173488200_ref36","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.4049\/jimmunol.1600582","article-title":"Pan-specific prediction of peptide\u2013MHC class I complex stability, a correlate of t cell immunogenicity","volume":"197","author":"Rasmussen","year":"2016","journal-title":"J Immunol"},{"key":"2024110623173488200_ref37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-6-132","article-title":"Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method","volume":"6","author":"Peters","year":"2005","journal-title":"BMC Bioinform"},{"key":"2024110623173488200_ref38","doi-asserted-by":"publisher","first-page":"4946","DOI":"10.1093\/bioinformatics\/btz427","article-title":"ACME: pan-specific peptide\u2013MHC class I binding prediction through attention-based deep neural networks","volume":"35","author":"Hu","year":"2019","journal-title":"Bioinformatics"},{"key":"2024110623173488200_ref39","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1016\/j.ijid.2020.07.016","article-title":"Mortality in Covid-19 disease patients: Correlating the association of major histocompatibility complex (MHC) with Severe Acute Respiratory Syndrome 2 (SARS-CoV-2) variants","volume":"98","author":"de Sousa","year":"2020","journal-title":"Int J Infect Dis"},{"key":"2024110623173488200_ref40","doi-asserted-by":"publisher","first-page":"D1262","DOI":"10.1093\/nar\/gkab1136","article-title":"IMGT\u00ae databases, related tools and web resources through three main axes of research and development","volume":"50","author":"Manso","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2024110623173488200_ref41","article-title":"The RCSB Protein Data Bank: integrative view of protein, gene and 3D structural information","volume":"44","author":"Rose","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2024110623173488200_ref42","doi-asserted-by":"publisher","first-page":"W502","DOI":"10.1093\/nar\/gkz452","article-title":"IEDB-AR: immune epitope database\u2013analysis resource in 2019","volume":"47","author":"Dhanda","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2024110623173488200_ref43","doi-asserted-by":"publisher","first-page":"13404","DOI":"10.1038\/ncomms13404","article-title":"Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry","volume":"7","author":"Bassani-Sternberg","year":"2016","journal-title":"Nat Commun"},{"key":"2024110623173488200_ref44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1198\/10618600152418584","article-title":"The art of data augmentation","volume":"10","author":"Van Dyk","year":"2001","journal-title":"J Comput Graph Stat"},{"key":"2024110623173488200_ref45","article-title":"Representation learning with contrastive predictive coding","author":"Oord","year":"2018"},{"key":"2024110623173488200_ref46","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"2024110623173488200_ref47","article-title":"Reformer: the efficient transformer","author":"Kitaev","year":"2020"},{"key":"2024110623173488200_ref48","doi-asserted-by":"publisher","first-page":"2140","DOI":"10.1093\/bioinformatics\/bti269","article-title":"EpiMHC: a curated database of MHC-binding peptides for customized computational vaccinology","volume":"21","author":"Reche","year":"2005","journal-title":"Bioinformatics"},{"key":"2024110623173488200_ref49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1756-0500-2-61","article-title":"MHCBN 4.0: a database of MHC\/tap binding peptides and t-cell epitopes","volume":"2","author":"Lata","year":"2009","journal-title":"BMC Res Notes"},{"key":"2024110623173488200_ref50","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s002510050595","article-title":"SYFPEITHI: database for MHC ligands and peptide motifs","volume":"50","author":"Rammensee","year":"1999","journal-title":"Immunogenetics"},{"key":"2024110623173488200_ref51","doi-asserted-by":"publisher","first-page":"235","DOI":"10.7326\/0003-4819-158-4-201302190-00003","article-title":"Genome-wide association study of spontaneous resolution of hepatitis C virus infection: data from multiple cohorts","volume":"158","author":"Duggal","year":"2013","journal-title":"Ann Intern Med"},{"key":"2024110623173488200_ref52","doi-asserted-by":"publisher","first-page":"w20248","DOI":"10.4414\/smw.2020.20248","article-title":"HLA studies in the context of coronavirus outbreaks","volume":"150","author":"Sanchez-Mazas","year":"2020","journal-title":"Swiss Med Wkly"},{"key":"2024110623173488200_ref53","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.1007\/s10875-021-01071-x","article-title":"MHC haplotyping of SARS-CoV-2 patients: HLA subtypes are not associated with the presence and severity of Covid-19 in the Israeli population","volume":"41","author":"Ben Shachar","year":"2021","journal-title":"J Clin Immunol"},{"key":"2024110623173488200_ref54","doi-asserted-by":"publisher","first-page":"644637","DOI":"10.3389\/fimmu.2021.644637","article-title":"NepDB: a database of t-cell experimentally-validated neoantigens and pan-cancer predicted neoepitopes for cancer immunotherapy","volume":"12","author":"Xia","year":"2021","journal-title":"Front Immunol"},{"key":"2024110623173488200_ref55","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.1093\/annonc\/mdy022","article-title":"Neopepsee: accurate genome-level prediction of neoantigens by harnessing sequence and amino acid immunogenicity information","volume":"29","author":"Kim","year":"2018","journal-title":"Ann Oncol"},{"key":"2024110623173488200_ref56","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1016\/j.cell.2020.09.015","article-title":"Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction","volume":"183","author":"Wells","year":"2020","journal-title":"Cell"},{"key":"2024110623173488200_ref57","doi-asserted-by":"publisher","first-page":"D1053","DOI":"10.1093\/nar\/gkac1011","article-title":"The IPD-IMGT\/HLA database","volume":"51","author":"Barker","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2024110623173488200_ref58","article-title":"BERT: pre-training of deep bidirectional transformers for language understanding","volume-title":"Proceedings of naacL-HLT","author":"Kenton"},{"key":"2024110623173488200_ref59","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR52688.2022.01553","article-title":"Masked autoencoders are scalable vision learners","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","author":"He"},{"key":"2024110623173488200_ref60","doi-asserted-by":"publisher","first-page":"13404","DOI":"10.1038\/ncomms13404","article-title":"Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry","volume":"7","author":"Bassani-Sternberg","year":"2016","journal-title":"Nat Commun"},{"key":"2024110623173488200_ref61","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.cels.2020.06.010","article-title":"MHCflurry 2.0: Improved pan-allele prediction of MHC class I-presented peptides by incorporating antigen processing","volume":"11","author":"O\u2019Donnell","year":"2020","journal-title":"Cell systems"},{"key":"2024110623173488200_ref62","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1158\/2326-6066.CIR-19-0464","article-title":"High-throughput prediction of MHC class I and II neoantigens with MHCnuggets","volume":"8","author":"Shao","year":"2020","journal-title":"Cancer Immunol Res"},{"key":"2024110623173488200_ref63","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1038\/s41587-019-0322-9","article-title":"A large peptidome dataset improves hla class I epitope prediction across most of the human population","volume":"38","author":"Sarkizova","year":"2020","journal-title":"Nat Biotechnol"},{"key":"2024110623173488200_ref64","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1016\/0022-2836(91)90193-A","article-title":"Amino acid substitution matrices from an information theoretic perspective","volume":"219","author":"Altschul","year":"1991","journal-title":"J Mol Biol"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/6\/bbae552\/60250384\/bbae552.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/6\/bbae552\/60250384\/bbae552.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T18:17:57Z","timestamp":1730917077000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae552\/7852976"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,23]]},"references-count":64,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,9,23]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae552","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.11.13.566840","asserted-by":"object"}]},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,11]]},"published":{"date-parts":[[2024,9,23]]},"article-number":"bbae552"}}