{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T11:04:18Z","timestamp":1789643058054,"version":"4.0.0"},"reference-count":185,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:p>The immune system leverages B and T cells to recognize specific molecular patterns, known as epitopes, on pathogens and cancer cells to effectively combat infections and diseases. The critical success of immunotherapies in cancer treatment and COVID-19 vaccine development has established precise epitope identification as a central and rapidly growing priority in therapeutic design. Because traditional wet-lab based identification of B- and T-cell epitopes is expensive and timeconsuming, our systematic literature search (2015\u20132026) identified 148 Artificial Intelligence (AI) based linear B-cell, conformational B-cell, and T-cell epitope prediction models within the stated search scope. However, the true potential of these models remains unclear due to fragmented evaluation practices, with models rarely tested across a comprehensive range of datasets, insufficient comparison with existing predictors, systematic under-utilization of available public databases, and other methodological inconsistencies across five different stages of the predictive pipeline. Moreover, the 7 existing review papers fail to adequately highlight these research gaps or to support the development of robust AI models. This review paper consolidates the computational landscape of B-cell and T-cell epitope recognition and introduces a unified taxonomy that organises the field into twelve prediction tasks: linear and conformational B-cell prediction together with ten T-cell subtasks (T1\u2013T10) that span the antigen-recognition cascade from human leukocyte antigen (HLA) typing to vaccine design. It analyses these twelve tasks across five major stages of a shared predictive pipeline. Within this taxonomy, the recognition sub-tasks (T1\u2013T7) apply to the epitopes of any antigen, whereas neoantigen identification (T8) and tumor T-cell antigen (TTCA) classification (T9) are oncology-specific translational applications and multi-epitope vaccine design (T10) spans infectious-disease and cancer targets. It systematically examines 155 studies published from 2015 to 2026 to perform comprehensive categorization of 43 dedicated epitope\/immunology databases, 144 benchmark datasets, 272 representation learning approaches, 148 classifiers, 54 evaluation and optimization approaches, and 148 predictive models accessibility status across all twelve prediction tasks. Additionally, it highlights persistent challenges across each stage of the predictive pipeline and offers key directions for improvement. This comprehensive analysis provides actionable recommendations for developing more robust, generalizable epitope predictors, and it ultimately accelerates the translation of computational predictions into effective immunotherapies against diverse diseases.<\/jats:p>","DOI":"10.3389\/fimmu.2026.1873135","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T10:46:46Z","timestamp":1789642006000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Computational identification of B- and T-cell epitopes: a unified task taxonomy and review of databases, datasets, predictive pipelines, and gaps"],"prefix":"10.3389","volume":"17","author":[{"given":"Muhammad Ali","family":"Ibrahim","sequence":"first","affiliation":[{"name":"Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau","place":["Kaiserslautern, Germany"]},{"name":"Smart Data & Knowledge Services, German Research Center for Artificial Intelligence","place":["Kaiserslautern, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arooj","family":"Zaib","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau","place":["Kaiserslautern, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tayyaba","family":"Asif","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau","place":["Kaiserslautern, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Dengel","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau","place":["Kaiserslautern, Germany"]},{"name":"Smart Data & Knowledge Services, German Research Center for Artificial Intelligence","place":["Kaiserslautern, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens","family":"Posma","sequence":"additional","affiliation":[{"name":"Center for Thrombosis and Hemostasis, University Medical Center of the Johannes Gutenberg University","place":["Mainz, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wolfram","family":"Ruf","sequence":"additional","affiliation":[{"name":"Center for Thrombosis and Hemostasis, University Medical Center of the Johannes Gutenberg University","place":["Mainz, Germany"]},{"name":"Department of Immunology and Microbiology, Scripps Research","place":["La Jolla, CA, United States"]},{"name":"German Center for Cardiovascular Research (DZHK), Partner Site Rhein-Main","place":["Mainz, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Nabeel","family":"Asim","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau","place":["Kaiserslautern, Germany"]},{"name":"Smart Data & Knowledge Services, German Research Center for Artificial Intelligence","place":["Kaiserslautern, Germany"]},{"name":"Graduate School of Informatics, Osaka Metropolitan University","place":["Sakai, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1007\/s41870-023-01294-8","article-title":"Ghpcso: gaussian distribution based hybrid particle cat swarm optimization for linear b-cell epitope prediction","volume":"15","author":"Angaitkar","year":"2023","journal-title":"Int J Inf Technol"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108083","article-title":"A method for predicting linear and conformational b-cell epitopes in an antigen from its primary sequence","volume":"170","author":"Kumar","year":"2024","journal-title":"Comput Biol Med"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab423","article-title":"Epitope3d: a machine learning method for conformational b-cell epitope prediction","volume":"23","author":"da Silva","year":"2022","journal-title":"Briefings Bioinf"},{"key":"B4","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/s13205-023-03716-7","article-title":"Dl-tcnn: deep learning-based temporal convolutional neural network for prediction of conformational b-cell epitopes","volume":"13","author":"Angaitkar","year":"2023","journal-title":"3 Biotech"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2023.107874","article-title":"Deepbce: evaluation of deep learning models for identification of immunogenic b-cell epitopes","volume":"104","author":"Attique","year":"2023","journal-title":"Comput Biol Chem"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.21203\/rs.3.rs-2709196\/v1","article-title":"Epicluster: end-to-end deep learning model for b cell epitope prediction designed to capture epitope clustering property","author":"Choi","year":"2023","journal-title":"bioRxiv"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2023.1117027","article-title":"Deeplbcepred: a bi-lstm and multi-scale cnn-based deep learning method for predicting linear b-cell epitopes","volume":"14","author":"Qi","year":"2023","journal-title":"Front Microbiol"},{"key":"B8","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/s12539-023-00549-z","article-title":"Lbce-xgb: a xgboost model for predicting linear b-cell epitopes based on bert embeddings","volume":"15","author":"Liu","year":"2023","journal-title":"Interdiscip Sci-Comput Life Sci"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad114","article-title":"Epitope1d: accurate taxonomy-aware b-cell linear epitope prediction","volume":"24","author":"da Silva","year":"2023","journal-title":"Briefings Bioinf"},{"key":"B10","doi-asserted-by":"publisher","first-page":"2464","DOI":"10.1038\/s41598-024-53028-w","article-title":"Prediction of linear b-cell epitopes based on protein sequence features and bert embeddings","volume":"14","author":"Liu","year":"2024","journal-title":"Sci Rep"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.3390\/pathogens11020146","article-title":"Machine learning techniques for the prediction of b-cell and t-cell epitopes as potential vaccine targets with a specific focus on sars-cov-2 pathogen: a review","volume":"11","author":"Bukhari","year":"2022","journal-title":"Pathogens"},{"key":"B12","doi-asserted-by":"publisher","first-page":"890943","DOI":"10.1101\/2021.07.13.452188","article-title":"A structure-based b-cell epitope prediction model through combing local and global features","volume":"13","author":"Lu","year":"2022","journal-title":"Front Immunol"},{"key":"B13","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-43427-3_29","article-title":"Beetle: a framework for linear b-cell epitope prediction and classification","author":"Yuan","year":"2023"},{"key":"B14","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1007\/s42979-023-02091-7","article-title":"Fuzzy deep learning model for prediction of conformational epitope","volume":"4","author":"Singh","year":"2023","journal-title":"SN Comput Sci"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkae386","article-title":"Sema 2.0: web-platform for b-cell conformational epitopes prediction using artificial intelligence","volume":"52","author":"Ivanisenko","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1980","article-title":"Physicochemical properties-based hybrid machine learning technique for the prediction of sars-cov-2 t-cell epitopes as vaccine targets","volume":"10","author":"Bukhari","year":"2024","journal-title":"PeerJ Comput Sci"},{"key":"B17","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1186\/s12859-026-06492-2","article-title":"Transfer learning for t-cell response prediction","volume":"27","author":"Stadelmaier","year":"2024","journal-title":"arXiv preprint arXiv:2403.12117"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1101\/2023.05.10.540189","article-title":"Predicting t cell receptor functionality against mutant epitopes","volume":"4","author":"Drost","year":"2024","journal-title":"Cell Genomics"},{"key":"B19","doi-asserted-by":"publisher","first-page":"100040","DOI":"10.1016\/j.immuno.2024.100040","article-title":"T-cell receptor binding prediction: a machine learning revolution","volume":"15","author":"Weber","year":"2024","journal-title":"ImmunoInformatics"},{"key":"B20","doi-asserted-by":"publisher","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":"B21","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1186\/s13073-023-01225-z","article-title":"A robust deep learning workflow to predict cd8+ t-cell epitopes","volume":"15","author":"Lee","year":"2023","journal-title":"Genome Med"},{"key":"B22","first-page":"194","article-title":"Tcr-bert: learning the grammar of t-cell receptors for flexible antigen-binding analyses","volume-title":"Machine Learning in Computational Biology (MLCB)","author":"Wu","year":"2024"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.54097\/wy28c490","article-title":"Biology-based ai predicts t-cell receptor antigen binding specificity","volume":"10","author":"Shen","year":"2024","journal-title":"Acad J Sci Technol"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1002\/pro.4841","article-title":"Tepcam: prediction of t-cell receptor\u2013epitope binding specificity via interpretable deep learning","volume":"33","author":"Chen","year":"2024","journal-title":"Protein Sci"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac546","article-title":"Dna-mp: a generalized dna modifications predictor for multiple species based on powerful sequence encoding method","volume":"24","author":"Nabeel Asim","year":"2023","journal-title":"Briefings Bioinf"},{"key":"B26","doi-asserted-by":"publisher","first-page":"153","DOI":"10.21203\/rs.3.rs-5043775\/v1","article-title":"Transitioning from wet lab to artificial intelligence: a systematic review of ai predictors in crispr","volume":"23","author":"Abbasi","year":"2025","journal-title":"J Transl Med"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0270275","article-title":"Lgca-vhppi: a local-global residue context aware viral-host protein-protein interaction predictor","volume":"17","author":"Asim","year":"2022","journal-title":"PloS One"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/2680160","article-title":"Fundamentals and methods for t-and b-cell epitope prediction","volume":"2017","author":"Sanchez-Trincado","year":"2017","journal-title":"J Immunol Res"},{"key":"B29","doi-asserted-by":"crossref","DOI":"10.1007\/978-981-15-3350-1_4","article-title":"Bioinformatics tools for epitope prediction","volume-title":"Systems and Synthetic Immunology","author":"Jaiswal","year":"2020"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2024.1463931","article-title":"Integrating machine learning to advance epitope mapping","volume":"15","author":"Grewal","year":"2024","journal-title":"Front Immunol"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jctc.3c00513","article-title":"Computational methods in immunology and vaccinology: design and development of antibodies and immunogens","volume":"19","author":"Guarra","year":"2023","journal-title":"J Chem Theory Comput"},{"key":"B32","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1038\/s41541-025-01258-y","article-title":"Ai-driven epitope prediction: a systematic review, comparative analysis, and practical guide for vaccine development","volume":"10","author":"Villanueva-Flores","year":"2025","journal-title":"NPJ Vaccines"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1038\/s41541-023-00795-8","article-title":"Development and use of machine learning algorithms in vaccine target selection","volume":"9","author":"Bravi","year":"2024","journal-title":"NPJ Vaccines"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa773","article-title":"Epidope: a deep neural network for linear b-cell epitope prediction","volume":"37","author":"Collatz","year":"2021","journal-title":"Bioinformatics"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1101\/2022.02.27.481241","article-title":"Epibertope: a sequence-based pre-trained bert model improves linear and structural epitope prediction by learning long-distance protein interactions effectively","author":"Park","year":"2022","journal-title":"bioRxiv"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2024.1322712","article-title":"Discotope-3.0: improved b-cell epitope prediction using inverse folding latent representations","volume":"15","author":"H\u00f8ie","year":"2024","journal-title":"Front Immunol"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad187","article-title":"Identifying b-cell epitopes using alphafold2 predicted structures and pretrained language model","volume":"39","author":"Zeng","year":"2023","journal-title":"Bioinformatics"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1101\/2022.07.11.499418","article-title":"Bepipred-3.0: improved b-cell epitope prediction using protein language models","volume":"31","author":"Clifford","year":"2022","journal-title":"Protein Sci"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2506.13395","article-title":"Cbtope2: an improved method for predicting of conformational b-cell epitopes in an antigen from its primary sequence","author":"Pandey","year":"2025","journal-title":"arXiv preprint arXiv:2506.13395"},{"key":"B40","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1038\/s41467-021-21541-5","article-title":"Accurate imputation of human leukocyte antigens with CookHLA","volume":"12","author":"Cook","year":"2021","journal-title":"Nat Commun"},{"key":"B41","doi-asserted-by":"publisher","first-page":"1639","DOI":"10.1038\/s41467-021-21975-x","article-title":"A deep learning method for HLA imputation and trans-ethnic MHC finemapping of type 1 diabetes (DEEP-HLA)","volume":"12","author":"Naito","year":"2021","journal-title":"Nat Commun"},{"key":"B42","article-title":"Efficient HLA imputation from sequential SNPs data by transformer","volume-title":"J Hum Genet","author":"Tanaka","year":"2022"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.1002\/humu.23230","article-title":"HLA-HD: an accurate HLA typing algorithm for next-generation sequencing data","volume":"38","author":"Kawaguchi","year":"2017","journal-title":"Hum Mutat"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1101\/266197","article-title":"HISAT-genotype: next generation genomic analysis platform on a personal computer","author":"Kim","year":"2018","journal-title":"bioRxiv"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.1007\/s00251-010-0441-4","article-title":"NetCTLpan: pan-specific MHC class I pathway epitope predictions","volume":"62","author":"Stranzl","year":"2010","journal-title":"Immunogenetics"},{"key":"B46","doi-asserted-by":"publisher","first-page":"13126","DOI":"10.1038\/s41598-021-92632-y","article-title":"NetCleave: an open-source algorithm for predicting C-terminal antigen processing for MHC-I and MHC-II","volume":"11","author":"Amengual-Rigo","year":"2021","journal-title":"Sci Rep"},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2209.07527","article-title":"Improved proteasomal cleavage prediction with positiveunlabeled learning","author":"Dorigatti","year":"2022","journal-title":"arXiv preprint arXiv:2209.07527"},{"key":"B48","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2606.02462","article-title":"APLSuite: an integrated suite for CD4+ T cell epitope prediction via antigen processing likelihood","author":"Li","year":"2026","journal-title":"arXiv"},{"key":"B49","doi-asserted-by":"publisher","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":"B50","doi-asserted-by":"publisher","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 Syst"},{"key":"B51","doi-asserted-by":"publisher","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":"B52","doi-asserted-by":"publisher","first-page":"696","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":"B53","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab216","article-title":"Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution (BVLSTM-MHC)","volume":"22","author":"Jiang","year":"2021","journal-title":"Briefings Bioinf"},{"key":"B54","doi-asserted-by":"publisher","DOI":"10.64898\/2025.12.01.691752","article-title":"DPCMHC: efficient prediction of MHC-peptide binding affinity by deep learning based on dual-padding convolution","author":"Lu","year":"2025","journal-title":"bioRxiv"},{"key":"B55","doi-asserted-by":"publisher","first-page":"10749","DOI":"10.1038\/s41598-022-14526-x","article-title":"3pHLA-score improves structure-based peptide-HLA binding affinity prediction","volume":"12","author":"Conev","year":"2022","journal-title":"Sci Rep"},{"key":"B56","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.cels.2022.12.002","article-title":"Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes","volume":"14","author":"Gfeller","year":"2023","journal-title":"Cell Syst"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-024-00971-y","article-title":"Deep learning enhances the prediction of HLA class I-presented CD8+ T cell epitopes in foreign pathogens","volume":"7","author":"Wohlwend","year":"2025","journal-title":"Nat Mach Intell"},{"key":"B58","doi-asserted-by":"publisher","DOI":"10.1074\/mcp.tir119.001658","article-title":"NNAlign_MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T-cell epitope predictions","volume":"18","author":"Alvarez","year":"2019","journal-title":"Mol Cell Proteomics"},{"key":"B59","doi-asserted-by":"publisher","DOI":"10.1016\/j.immuni.2023.03.009","article-title":"Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes (MixMHC2pred-2.0)","volume":"56","author":"Racle","year":"2023","journal-title":"Immunity"},{"key":"B60","doi-asserted-by":"publisher","DOI":"10.1109\/tcbb.2021.3074927","article-title":"DeepSeqPanII: an interpretable recurrent neural network model with attention mechanism for peptide-HLA class II binding prediction","volume":"19","author":"Liu","year":"2022","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"B61","doi-asserted-by":"crossref","DOI":"10.21203\/rs.3.rs-2048064\/v1","article-title":"MTL4MHC2: MHC class II binding prediction using multi-task learning from small training data","volume-title":"Research Square","author":"Ikkyu","year":"2022"},{"key":"B62","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab422","article-title":"BERTMHC: improved MHC\u2013peptide class II interaction prediction with transformer and multiple instance learning","volume":"37","author":"Cheng","year":"2021","journal-title":"Bioinformatics"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2021.100194","article-title":"Prediction of neo-epitope immunogenicity reveals TCR recognition determinants and provides insight into immunoediting (PRIME)","volume":"2","author":"Schmidt","year":"2021","journal-title":"Cell Rep Med"},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab160","article-title":"DeepImmuno: deep learningempowered prediction and generation of immunogenic peptides for T-cell immunity","volume":"22","author":"Li","year":"2021","journal-title":"Briefings Bioinf"},{"key":"B65","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2019.00827","article-title":"Quantitative prediction of the landscape of T cell epitope immunogenicity in sequence space (Repitope)","volume":"10","author":"Ogishi","year":"2019","journal-title":"Front Immunol"},{"key":"B66","article-title":"ImmunoStruct: a multimodal neural network framework for immunogenicity prediction from peptide-MHC sequence, structure, and biochemical properties","volume-title":"Research Square","author":"Givechian","year":"2025"},{"key":"B67","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2019.02559","article-title":"DeepHLApan: a deep learning approach for neoantigen prediction considering both HLA-peptide binding and immunogenicity","volume":"10","author":"Wu","year":"2019","journal-title":"Front Immunol"},{"key":"B68","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.1038\/s42003-021-02610-3","article-title":"NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCR\u03b1 and \u03b2 sequence data","volume":"4","author":"Montemurro","year":"2021","journal-title":"Commun Biol"},{"key":"B69","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.1055151","article-title":"NetTCR-2.1: lessons and guidance on how to develop models for TCR specificity predictions","volume":"13","author":"Montemurro","year":"2022","journal-title":"Front Immunol"},{"key":"B70","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2021.664514","article-title":"Contribution of T cell receptor alpha and beta CDR3, MHC typing, V and J genes to peptide binding prediction","volume":"12","author":"Springer","year":"2021","journal-title":"Front Immunol"},{"key":"B71","doi-asserted-by":"publisher","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":"B72","doi-asserted-by":"publisher","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":"B73","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2316401121","article-title":"TULIP: a transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes","volume":"121","author":"Meynard-Piganeau","year":"2024","journal-title":"Proc Natl Acad Sci"},{"key":"B74","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2023.11.037","article-title":"TCR-ESM: employing protein language embeddings to predict TCRpeptide-MHC binding","volume":"23","author":"Yadav","year":"2024","journal-title":"Comput Struct Biotechnol J"},{"key":"B75","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 (MixTCRpred)","volume":"15","author":"Croce","year":"2024","journal-title":"Nat Commun"},{"key":"B76","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-025-01002-0","article-title":"A unified deep framework for peptide-major histocompatibility complex-T cell receptor binding prediction (UniPMT)","volume":"7","author":"Zhao","year":"2025","journal-title":"Nat Mach Intell"},{"key":"B77","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-024-00973-w","article-title":"A unified cross-attention model for predicting antigen binding specificity to both HLA and TCR molecules (UnifyImmun)","volume":"7","author":"Yu","year":"2025","journal-title":"Nat Mach Intell"},{"key":"B78","doi-asserted-by":"publisher","DOI":"10.1101\/2023.04.25.538237","article-title":"STAPLER: efficient learning of TCRpeptide specificity prediction from full-length TCR-peptide data","author":"Kwee","year":"2023","journal-title":"bioRxiv"},{"key":"B79","doi-asserted-by":"publisher","DOI":"10.1101\/2023.04.12.536635","article-title":"Context-aware amino acid embedding advances analysis of TCR-epitope interactions","author":"Zhang","year":"2023","journal-title":"bioRxiv"},{"key":"B80","doi-asserted-by":"publisher","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":"B81","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2025.1616328","article-title":"NetTCR-struc: a structure-driven approach for prediction of TCR-pmhc interactions","volume":"16","author":"Deleuran","year":"2025","journal-title":"Front Immunol"},{"key":"B82","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2024.1346784","article-title":"TCRcost: a deep learning model utilizing TCR 3D structure for enhanced prediction of TCR-peptide-MHC binding","volume":"15","author":"Li","year":"2024","journal-title":"Front Genet"},{"key":"B83","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1101\/2022.11.04.514231","article-title":"ImmuneBuilder: deep-learning models for predicting the structures of immune proteins","volume":"6","author":"Abanades","year":"2023","journal-title":"Commun Biol"},{"key":"B84","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","article-title":"Accurate structure prediction of biomolecular interactions with AlphaFold 3","volume":"630","author":"Abramson","year":"2024","journal-title":"Nature"},{"key":"B85","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-019-0679-x","article-title":"pTuneos: prioritizing tumor neoantigens from next-generation sequencing data","volume":"11","author":"Zhou","year":"2019","journal-title":"Genome Med"},{"key":"B86","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s13073-025-01592-9","article-title":"NeoGuider: neoepitope prediction using advanced feature engineering","volume":"18","author":"Zhao","year":"2024","journal-title":"Genome Medicine"},{"key":"B87","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1186\/s12859-020-03869-9","article-title":"TruNeo: an integrated pipeline improves personalized true tumor neoantigen identification","volume":"21","author":"Tang","year":"2020","journal-title":"BMC Bioinf"},{"key":"B88","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2026.1722117","article-title":"CNNeoPP: a deep learning pipeline for personalized neoantigen prediction and liquid biopsy applications","volume":"17","author":"Cai","year":"2025","journal-title":"medRxiv"},{"key":"B89","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1186\/s12859-019-2876-4","article-title":"NeoPredPipe: high-throughput neoantigen prediction and recognition potential pipeline","volume":"20","author":"Schenck","year":"2019","journal-title":"BMC Bioinf"},{"key":"B90","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab344","article-title":"NeoFox: annotating neoantigen candidates with neoantigen features","volume":"37","author":"Lang","year":"2021","journal-title":"Bioinformatics"},{"key":"B91","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1186\/s13073-024-01384-7","article-title":"pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection","volume":"16","author":"Xia","year":"2024","journal-title":"Genome Med"},{"key":"B92","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1186\/s12967-021-03084-x","article-title":"ittca-rf: a random forest predictor for tumor t cell antigens","volume":"19","author":"Jiao","year":"2021","journal-title":"J Transl Med"},{"key":"B93","doi-asserted-by":"publisher","DOI":"10.1007\/s00251-022-01258-5","article-title":"ittca-mff: identifying tumor t cell antigens based on multiple feature fusion","volume":"74","author":"Zou","year":"2022","journal-title":"Immunogenetics"},{"key":"B94","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.107941","article-title":"ittca-mvl: A multi-view learning model based on physicochemical information and sequence statistical information for tumor t cell antigens identification","volume":"170","author":"Zhao","year":"2024","journal-title":"Comput Biol Med"},{"key":"B95","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106368","article-title":"Psrttca: A new approach for improving the prediction and characterization of tumor t cell antigens using propensity score representation learning","volume":"152","author":"Charoenkwan","year":"2023","journal-title":"Comput Biol Med"},{"key":"B96","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1186\/s12859-023-05421-x","article-title":"Stackttca: A stacking ensemble learning-based framework for accurate and high-throughput identification of tumor t cell antigens","volume":"24","author":"Charoenkwan","year":"2023","journal-title":"BMC Bioinf"},{"key":"B97","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0307176","article-title":"Encap: Computational prediction of tumor t cell antigens with ensemble classifiers and diverse sequence features","volume":"19","author":"Yu","year":"2024","journal-title":"PloS One"},{"key":"B98","doi-asserted-by":"publisher","DOI":"10.1109\/access.2024.3481244","article-title":"Sequence-based intelligent model for identification of tumor t cell antigens using fusion features","volume":"12","author":"Bibi","year":"2024","journal-title":"IEEE Access"},{"key":"B99","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2025.108630","article-title":"Lynet: Computational identification of tumor t cell antigens using convolutional and recurrent neural networks","volume":"120","author":"Lv","year":"2026","journal-title":"Comput Biol Chem"},{"key":"B100","doi-asserted-by":"publisher","first-page":"3238","DOI":"10.1038\/s41598-021-81749-9","article-title":"An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study","volume":"11","author":"Yang","year":"2021","journal-title":"Sci Rep"},{"key":"B101","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2020.01581","article-title":"COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning","volume":"11","author":"Ong","year":"2020","journal-title":"Front Immunol"},{"key":"B102","doi-asserted-by":"publisher","DOI":"10.3389\/fcimb.2025.1536156","article-title":"SHASI-ML: a machine learning-based approach for immunogenicity prediction in salmonella vaccine development","volume":"15","author":"Spiga","year":"2025","journal-title":"Front Cell Infect Microbiol"},{"key":"B103","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1038\/s41467-025-67778-2","article-title":"Integrating protein language and geometric deep learning models for enhanced vaccine antigen prediction","volume":"16","author":"Zai","year":"2025","journal-title":"Nat Commun"},{"key":"B104","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003266","article-title":"Properties of MHC class I presented peptides that enhance immunogenicity","volume":"9","author":"Calis","year":"2013","journal-title":"PloS Comput Biol"},{"key":"B105","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkq944","article-title":"Protegen: a web-based protective antigen database and analysis system","volume":"39","author":"Yang","year":"2011","journal-title":"Nucleic Acids Res"},{"key":"B106","doi-asserted-by":"publisher","DOI":"10.1515\/pac-2020-1107","article-title":"Data curation to improve the pattern recognition performance of b-cell epitope prediction by support vector machine","volume":"93","author":"Lim","year":"2021","journal-title":"Pure Appl Chem"},{"key":"B107","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13040-020-00211-0","article-title":"Deep learning methods improve linear b-cell epitope prediction","volume":"13","author":"Liu","year":"2020","journal-title":"Biodata Min"},{"key":"B108","doi-asserted-by":"publisher","DOI":"10.1016\/j.gpb.2022.11.009","article-title":"Netbce: an interpretable deep neural network for accurate prediction of linear b-cell epitopes","volume":"20","author":"Xu","year":"2022","journal-title":"Genomics Proteomics Bioinf"},{"key":"B109","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab536","article-title":"Organism-specific training improves performance of linear b-cell epitope prediction","volume":"37","author":"Ashford","year":"2021","journal-title":"Bioinformatics"},{"key":"B110","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.108280","article-title":"A novel ensemble fuzzy classification model in sars-cov-2 b-cell epitope identification for development of protein-based vaccine","volume":"116","author":"Ozger","year":"2022","journal-title":"Appl Soft Comput"},{"key":"B111","doi-asserted-by":"publisher","DOI":"10.52783\/jes.837","article-title":"Gaussian-based dilated 1-d cnn for classifying b-cell epitopes in zika and dengue protein sequences","volume":"20","author":"Deshmukh","year":"2024","journal-title":"J Electrical Syst"},{"key":"B112","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac281","article-title":"A framework for predicting variable-length epitopes of human-adapted viruses using machine learning methods","volume":"23","author":"Yin","year":"2022","journal-title":"Briefings Bioinf"},{"key":"B113","doi-asserted-by":"publisher","first-page":"14593","DOI":"10.1038\/s41598-023-41179-1","article-title":"Inferring linear-b cell epitopes using 2-step metaheuristic variant-feature selection using genetic algorithm","volume":"13","author":"Angaitkar","year":"2023","journal-title":"Sci Rep"},{"key":"B114","doi-asserted-by":"publisher","DOI":"10.1007\/s41870-022-00951-8","article-title":"Linear b-cell epitopes prediction using bagging based proposed ensemble model","volume":"14","author":"Gupta","year":"2022","journal-title":"Int J Inf Technol"},{"key":"B115","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac356","article-title":"A comparative analysis of amino acid encoding schemes for the prediction of flexible length linear b-cell epitopes","volume":"23","author":"Sahu","year":"2022","journal-title":"Briefings Bioinf"},{"key":"B116","doi-asserted-by":"crossref","DOI":"10.1109\/TOCS53301.2021.9689029","article-title":"Prediction of linear b-cell epitopes using manifold adaptive experimental design and random forest algorithm","author":"Yang","year":"2021"},{"key":"B117","doi-asserted-by":"publisher","first-page":"13739","DOI":"10.1038\/s41598-022-18021-1","article-title":"Prediction of b cell epitopes in proteins using a novel sequence similarity-based method","volume":"12","author":"Ras-Carmona","year":"2022","journal-title":"Sci Rep"},{"key":"B118","doi-asserted-by":"publisher","DOI":"10.3390\/v13081531","article-title":"Nigpred: class-specific antibody prediction for linear b-cell epitopes based on heterogeneous features and machine-learning approaches","volume":"13","author":"Tung","year":"2021","journal-title":"Viruses"},{"key":"B119","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac567","article-title":"Critical review of conformational b-cell epitope prediction methods","volume":"24","author":"Cia","year":"2023","journal-title":"Briefings Bioinf"},{"key":"B120","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.960985","article-title":"Sema: Antigen b-cell conformational epitope prediction using deep transfer learning","volume":"13","author":"Shashkova","year":"2022","journal-title":"Front Immunol"},{"key":"B121","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.275","article-title":"Enhancement of conformational b-cell epitope prediction using clusmote","volume":"6","author":"Solihah","year":"2020","journal-title":"PeerJ Comput Sci"},{"key":"B122","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2024.06.005","article-title":"Deep learning of antibody epitopes using positional permutation vectors","volume":"23","author":"Vardaxis","year":"2024","journal-title":"Comput Struct Biotechnol J"},{"key":"B123","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2024.05.023","article-title":"Wuren: Whole-modal union representation for epitope prediction","volume":"23","author":"Wang","year":"2024","journal-title":"Comput Struct Biotechnol J"},{"key":"B124","doi-asserted-by":"publisher","DOI":"10.1016\/j.immuno.2023.100024","article-title":"Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report","volume":"9","author":"Meysman","year":"2023","journal-title":"ImmunoInformatics"},{"key":"B125","article-title":"Machine learning dataset and benchmark for accurate T cell receptor-phla binding prediction (Hi-TpH)","volume-title":"Research Square","author":"Feng","year":"2025"},{"key":"B126","doi-asserted-by":"publisher","first-page":"82","DOI":"10.37349\/ei.2023.00091","article-title":"Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients","volume":"3","author":"Carri","year":"2023","journal-title":"Explor Immunol"},{"key":"B127","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf030","article-title":"A comprehensive benchmarking for evaluating TCR embeddings in modeling TCR-epitope interactions","volume":"26","author":"Feng","year":"2025","journal-title":"Briefings Bioinf"},{"key":"B128","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1186\/s12859-026-06423-1","article-title":"TransBindpMHCI: a transformer-based model for pan-specific MHC-I peptide binding prediction","volume":"27","author":"Xu","year":"2026","journal-title":"BMC Bioinf"},{"key":"B129","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2210.12991","article-title":"What cleaves? Is proteasomal cleavage prediction reaching a ceiling","author":"Ziegler","year":"2022","journal-title":"arXiv"},{"key":"B130","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00383-2","article-title":"Deep learning-based prediction of the T cell receptor\u2013antigen binding specificity","volume":"3","author":"Lu","year":"2021","journal-title":"Nat Mach Intell"},{"key":"B131","doi-asserted-by":"publisher","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":"Briefings Bioinf"},{"key":"B132","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.1038\/s41467-021-21879-w","article-title":"DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires","volume":"12","author":"Sidhom","year":"2021","journal-title":"Nat Commun"},{"key":"B133","doi-asserted-by":"publisher","DOI":"10.1158\/2326-6066.cir-19-0401","article-title":"pvactools: a computational toolkit to identify and visualize cancer neoantigens","volume":"8","author":"Hundal","year":"2020","journal-title":"Cancer Immunol Res"},{"key":"B134","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2019.107103","article-title":"Ttagp 1.0: A computational tool for the specific prediction of tumor t cell antigens","volume":"83","author":"Lissabet","year":"2019","journal-title":"Comput Biol Chem"},{"key":"B135","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2604.10531","article-title":"Pepbenchmark: A standardized benchmark for peptide machine learning","author":"Zhang","year":"2026","journal-title":"arXiv preprint arXiv:2604.10531"},{"key":"B136","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2021.107452","article-title":"Tap 1.0: A robust immunoinformatic tool for the prediction of tumor t-cell antigens based on aaindex properties","volume":"91","author":"Herrera-Bravo","year":"2021","journal-title":"Comput Biol Chem"},{"key":"B137","doi-asserted-by":"publisher","DOI":"10.1016\/j.ab.2020.113747","article-title":"ittca-hybrid: Improved and robust identification of tumor t cell antigens by utilizing hybrid feature representation","volume":"599","author":"Charoenkwan","year":"2020","journal-title":"Anal Biochem"},{"key":"B138","doi-asserted-by":"publisher","DOI":"10.3390\/app14104034","article-title":"Application of machine learning algorithms for prediction of tumor t-cell immunogens","volume":"14","author":"Sotirov","year":"2024","journal-title":"Appl Sci"},{"key":"B139","doi-asserted-by":"publisher","DOI":"10.1016\/j.biosystems.2024.105177","article-title":"An integrative machine learning model for the identification of tumor t-cell antigens","volume":"237","author":"Hassan","year":"2024","journal-title":"BioSystems"},{"key":"B140","doi-asserted-by":"publisher","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":"B141","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad135","article-title":"Unidl4biopep: a universal deep-learning architecture for binary classification in peptide bioactivity","volume":"24","author":"Du","year":"2023","journal-title":"Briefings Bioinf"},{"key":"B142","doi-asserted-by":"crossref","DOI":"10.21203\/rs.3.rs-2188420\/v1","article-title":"A machine learning model that identifies neoantigen-reactive CD8+ T cells in human gastrointestinal cancer","volume-title":"Research Square","author":"Shi","year":"2022"},{"key":"B143","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1038\/s42003-024-06380-6","article-title":"HeteroTCR: a heterogeneous graph neural network-based method for predicting peptide-TCR interaction","volume":"7","author":"Zhang","year":"2024","journal-title":"Commun Biol"},{"key":"B144","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2209.09155","article-title":"HLA predictions from long sequence read alignments, streamed directly into HLAminer","author":"Warren","year":"2022","journal-title":"arXiv"},{"key":"B145","doi-asserted-by":"publisher","DOI":"10.1111\/tan.15222","article-title":"Improving HLA typing imputation accuracy and eplet identification with local next-generation sequencing","volume":"103","author":"Lhotte","year":"2024","journal-title":"HLA"},{"key":"B146","doi-asserted-by":"publisher","DOI":"10.1101\/239236","article-title":"DeepMHC: Deep convolutional neural networks for high-performance peptideMHC binding affinity prediction","author":"Hu","year":"2017","journal-title":"bioRxiv"},{"key":"B147","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1186\/s12859-017-1997-x","article-title":"Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction","volume":"18","author":"Han","year":"2017","journal-title":"BMC Bioinf"},{"key":"B148","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btm611","article-title":"Efficient peptide-MHC-I binding prediction for alleles with few known binders (MultiTask SVM)","volume":"24","author":"Jacob","year":"2008","journal-title":"Bioinformatics"},{"key":"B149","doi-asserted-by":"crossref","DOI":"10.21203\/rs.3.rs-7699332\/v1","article-title":"DeepNeoAGNet: enhancing cancer immunotherapy with general heterogeneous sequence learning for precise neoantigen prediction and immunogenicity assessment","volume-title":"Research Square","author":"Liu","year":"2025"},{"key":"B150","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2018.01410","article-title":"Trans-allelic model for prediction of peptide:MHC-II interactions","volume":"9","author":"Degoot","year":"2018","journal-title":"Front Immunol"},{"key":"B151","doi-asserted-by":"publisher","DOI":"10.1007\/s00251-015-0873-y","article-title":"Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification","volume":"67","author":"Andreatta","year":"2015","journal-title":"Immunogenetics"},{"key":"B152","doi-asserted-by":"publisher","DOI":"10.1007\/s00251-019-01122-z","article-title":"Improved peptide-MHC class II interaction prediction through integration of eluted ligand and peptide affinity data","volume":"71","author":"Garde","year":"2019","journal-title":"Immunogenetics"},{"key":"B153","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1186\/s12864-023-09900-6","article-title":"Structure-aware deep model for MHC-II peptide binding affinity prediction","volume":"25","author":"Yu","year":"2024","journal-title":"BMC Genomics"},{"key":"B154","doi-asserted-by":"publisher","DOI":"10.1101\/539338","article-title":"Deep motif deconvolution of HLA-II peptidomes for robust class II epitope predictions","volume":"37","author":"Racle","year":"2019","journal-title":"Nat Biotechnol"},{"key":"B155","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2025.1681396","article-title":"NeoTImmuML: a machine learning-based prediction model for human tumor neoantigen immunogenicity","volume":"16","author":"Shao","year":"2025","journal-title":"Front Immunol"},{"key":"B156","doi-asserted-by":"crossref","DOI":"10.21203\/rs.3.rs-104972\/v1","article-title":"Predicting HLA-I peptide immunogenicity with deep learning and molecular dynamics","volume-title":"Research Square","author":"Weber","year":"2020"},{"key":"B157","doi-asserted-by":"publisher","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":"B158","doi-asserted-by":"publisher","first-page":"1966","DOI":"10.1101\/2025.07.23.666355","article-title":"NeoPrecis: enhancing immunotherapy response prediction through integration of qualified immunogenicity and clonality-aware neoantigen landscapes","volume":"17","author":"Lee","year":"2026","journal-title":"Nat Commun"},{"key":"B159","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1208\/s12248-024-01003-8","article-title":"Evaluating the immunogenicity risk of protein therapeutics by augmenting T cell epitope prediction with clinical factors","volume":"27","author":"Swanson","year":"2025","journal-title":"AAPS J"},{"key":"B160","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2023.1094236","article-title":"Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis","volume":"14","author":"Carri","year":"2023","journal-title":"Front Immunol"},{"key":"B161","doi-asserted-by":"publisher","DOI":"10.15252\/msb.20199416","article-title":"Predicting antigen specificity of single T cells based on TCR CDR3 regions (TcellMatch)","volume":"16","author":"Fischer","year":"2020","journal-title":"Mol Syst Biol"},{"key":"B162","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.893247","article-title":"ATM-TCR: TCR-epitope binding affinity prediction using a multi-head self-attention model","volume":"13","author":"Cai","year":"2022","journal-title":"Front Immunol"},{"key":"B163","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00619-3","article-title":"Pan-peptide meta learning for T-cell receptor-antigen binding recognition (PanPep)","volume":"5","author":"Gao","year":"2023","journal-title":"Nat Mach Intell"},{"key":"B164","article-title":"Active learning framework for cost-effective TCR-epitope binding affinity prediction (ActiveTCR)","author":"Zhang","year":"2023"},{"key":"B165","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2024.1426173","article-title":"TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasets","volume":"15","author":"Tatikonda","year":"2024","journal-title":"Front Immunol"},{"key":"B166","article-title":"TCR-EML: explainable model layers for TCR-pmhc prediction","author":"Li","year":"2025","journal-title":"arXiv"},{"key":"B167","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2020.01803","article-title":"Prediction of specific TCR-peptide binding from large dictionaries of TCR-peptide pairs (ERGO)","volume":"11","author":"Springer","year":"2020","journal-title":"Front Immunol"},{"key":"B168","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2501.01768","article-title":"Remodeling peptide-MHC-TCR triad binding as sequence fusion for immunogenicity prediction","author":"Ma","year":"2025","journal-title":"arXiv"},{"key":"B169","doi-asserted-by":"publisher","first-page":"000170","DOI":"10.23880\/aabsc-16000170","article-title":"ASNP: a personalized alternative splicing neoantigen discovery pipeline","volume":"4","author":"Mao","year":"2021","journal-title":"Ann Advanced Biomed Sci"},{"key":"B170","doi-asserted-by":"publisher","DOI":"10.1038\/nature18945","article-title":"Neoantigen landscape dynamics during human melanoma-T cell interactions","volume":"536","author":"Verdegaal","year":"2016","journal-title":"Nature"},{"key":"B171","doi-asserted-by":"publisher","DOI":"10.1038\/nature24473","article-title":"A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy","volume":"551","author":"Luksza","year":"2017","journal-title":"Nature"},{"key":"B172","doi-asserted-by":"publisher","DOI":"10.1038\/nature24462","article-title":"Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer","volume":"551","author":"Balachandran","year":"2017","journal-title":"Nature"},{"key":"B173","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbae084","article-title":"Single-residue linear and conformational b cell epitopes prediction using random and esm-2 based projections","volume":"25","author":"Israeli","year":"2024","journal-title":"Briefings Bioinf"},{"key":"B174","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2111.07137","article-title":"Interpreting BERT architecture predictions for peptide presentation by MHC class I proteins","author":"Gasser","year":"2021","journal-title":"arXiv"},{"key":"B175","doi-asserted-by":"publisher","DOI":"10.3389\/fbinf.2023.1207380","article-title":"Improved prediction of MHC-peptide binding using protein language models","volume":"3","author":"Hashemi","year":"2023","journal-title":"Front Bioinf"},{"key":"B176","doi-asserted-by":"publisher","first-page":"10752","DOI":"10.1038\/s41467-024-54887-7","article-title":"Towards designing improved cancer immunotherapy targets with a peptideMHC-I presentation model, HLApollo","volume":"15","author":"Thrift","year":"2024","journal-title":"Nat Commun"},{"key":"B177","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1186\/s12859-026-06407-1","article-title":"Multimodal learning on heterogeneous subgraphs and LLMs representation for MHC-peptide binding affinity prediction","volume":"27","author":"Li","year":"2026","journal-title":"BMC Bioinf"},{"key":"B178","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2512.14011","article-title":"Accelerating MHC-II epitope discovery via multi-scale prediction in antigen presentation","author":"Wan","year":"2025","journal-title":"arXiv"},{"key":"B179","doi-asserted-by":"publisher","DOI":"10.1101\/2023.12.18.572133","article-title":"Hla-epicheck: A b-cell epitope prediction tool for hla proteins using molecular dynamics simulation data","author":"Amaya-Ramirez","year":"2023","journal-title":"bioRxiv"},{"key":"B180","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab467","article-title":"Epitopevec: linear epitope prediction using deep protein sequence embeddings","volume":"37","author":"Bahai","year":"2021","journal-title":"Bioinformatics"},{"key":"B181","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac035","article-title":"Lbcepred: a machine learning model to predict linear b-cell epitopes","volume":"23","author":"Alghamdi","year":"2022","journal-title":"Briefings Bioinf"},{"key":"B182","doi-asserted-by":"publisher","DOI":"10.1109\/tcbb.2023.3311444","article-title":"Family-specific training improves linear b cell epitope prediction for emerging viruses","volume":"20","author":"Liu","year":"2023","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"B183","doi-asserted-by":"publisher","DOI":"10.3390\/cells10102744","article-title":"Bceps: A web server to predict linear b cell epitopes with enhanced immunogenicity and cross-reactivity","volume":"10","author":"Ras-Carmona","year":"2021","journal-title":"Cells"},{"key":"B184","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1016\/j.gpb.2019.04.004","article-title":"ilbe for computational identification of linear b-cell epitopes by integrating sequence and evolutionary features","volume":"18","author":"Hasan","year":"2020","journal-title":"Genomics Proteomics Bioinf"},{"key":"B185","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab321","article-title":"Serendip-ce: sequence-based interface prediction for conformational epitopes","volume":"37","author":"Hou","year":"2021","journal-title":"Bioinformatics"}],"container-title":["Frontiers in Immunology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2026.1873135\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T10:46:51Z","timestamp":1789642011000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2026.1873135\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,17]]},"references-count":185,"alternative-id":["10.3389\/fimmu.2026.1873135"],"URL":"https:\/\/doi.org\/10.3389\/fimmu.2026.1873135","relation":{},"ISSN":["1664-3224"],"issn-type":[{"value":"1664-3224","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,17]]},"article-number":"1873135"}}