T-Cell Receptor Repertoire in Autoimmune Diseases and Their Machine Learning-Based Prediction Analysis

Tongfei Shen; Miaozhe Huo; Shuaicheng Li

doi:10.53941/tai.2026.100006

Abstract

The T-cell receptor (TCR) is a fundamental component of the adaptive immune system, playing a crucial role in the development and progression of autoimmune diseases through its remarkable diversity and antigen specificity. Advances in high-throughput sequencing technologies and multi-omics data integration have revolutionized the ability to characterize TCR repertoires at unprecedented resolution. Coupled with emerging machine learning methodologies, these advances have opened new avenues for unraveling the complex immunopathology underlying autoimmune disorders. This review comprehensively summarizes current knowledge on the dynamic regulation of TCR repertoires in autoimmune diseases, highlighting key processes such as central tolerance failure, clonal expansion of autoreactive T cells, and regulatory T cell dysfunction, as well as the influences of genetic predisposition and immunosenescence on shaping TCR diversity. This review also provides a 3 that demonstrates how to analyze publicly available TCR repertoire datasets. We compare V and J gene usage profiles and CDR3 summary features across clinical labels to characterize between-group variation and to inform feature engineering for downstream machine learning models. Furthermore, we detail various machine learning-based diagnostic models that utilize gene usage patterns and CDR3 sequence features to accurately classify autoimmune disease status, alongside recent breakthroughs in predicting TCR-epitope binding specificity. These computational approaches not only enhance diagnostic precision but also provide mechanistic insights into immune recognition and autoreactivity. By integrating immunological principles with data-driven techniques, this work aims to offer a robust theoretical framework and practical guidance for future research in immunology and precision medicine. Ultimately, the convergence of TCR repertoire profiling and machine learning promises to drive innovative strategies for early diagnosis, personalized therapy, and improved clinical management of autoimmune diseases, enabling the transition to antigen-specific tolerogenic therapies.

References

1.
Pai, J.A.; Satpathy, A.T. High-throughput and single-cell T cell receptor sequencing technologies. Nat. Methods 2021, 18, 881–892.
2.
Chuang, H.C.; Li, R.; Huang, H.; et al. Single-cell sequencing of full-length transcripts and T-cell receptors with automated high-throughput smart-seq3. BMC Genom. 2024, 25, 1127.
3.
Zhang, Y.; Xu, Q.; Gao, Z.; et al. High-throughput screening for optimizing adoptive T cell therapies. Exp. Hematol. Oncol. 2024, 13, 113.
4.
Qu, H.Q.; Kao, C.; Hakonarson, H. Single-cell RNA sequencing technology landscape in 2023. Stem Cells 2024, 42, 1–12.
5.
Shah, K.; Al-Haidari, A.; Sun, J.; et al. T cell receptor (TCR) signaling in health and disease. Signal Transduct. Target. Ther. 2021, 6, 412.
6.
Pageon, S.V.; Tabarin, T.; Yamamoto, Y.; et al. Functional role of T-cell receptor nanoclusters in signal initiation and antigen discrimination. Proc. Natl. Acad. Sci. USA 2016, 113, E5454–E5463.
7.
Wang, Y.; Li, R.; Tong, R.; et al. Integrating single-cell RNA and T cell/B cell receptor sequencing with mass cytometry reveals dynamic trajectories of human peripheral immune cells from birth to old age. Nat. Immunol. 2025, 26, 308–322.
8.
Zhao, L.; Wang, Q.; Yang, C.; et al. Application of single-cell sequencing technology in research on colorectal cancer. J. Pers. Med. 2024, 14, 108.
9.
Huang, S.; Shi, W.; Li, S.; et al. Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis. Lab Chip 2024, 24, 2601–2621.
10.
Qin, R.; Zhang, Y.; Shi, J.; et al. TCR catch bonds nonlinearly control CD8 cooperation to shape T cell specificity. Cell Res. 2025, 35, 265–283.
11.
Gao, L.; Zhang, Y.; Ge, F.; et al. Structure-directed pan-specific T-cell receptor–peptide-major histocompatibility complex interaction prediction. J. Chem. Inf. Model. 2025, 65, 4674–4686.
12.
Baker, T.C. Improving Detection and Quantification of Major Histocompatibility Complex (MHC)-Presented Immunopeptides for Vaccine Development. Ph.D. Thesis, University of British Columbia, Vancouver, BC, Canada, 2024.
13.
Shi, Y. Comparative Analysis of TCR and TCR-pMHC Complex Structure Prediction Tools. Ph.D. Thesis, University of Tennessee, Knoxville, UK, 2024.
14.
de Wit, A.S.; Bianchi, F.; van den Bogaart, G. Antigen presentation of post-translationally modified peptides in major histocompatibility complexes. Immunol. Cell Biol. 2025, 103, 161–177.
15.
Barbosa, C.R.; Barton, J.; Shepherd, A.J.; et al. Mechanistic diversity in MHC class I antigen recognition. Biochem. J. 2021, 478, 4187–4202.
16.
Aran, A.; Garrigs, L.; Curigliano, G.; et al. Evaluation of the TCR repertoire as a predictive and prognostic biomarker in cancer: Diversity or clonality? Cancers 2022, 14, 1771.
17.
Joglekar, A.V.; Li, G. T cell antigen discovery. Nat. Methods 2021, 18, 873–880.
18.
Malviya, M.; Aretz, Z.E.; Molvi, Z.; et al. Challenges and solutions for therapeutic TCR-based agents. Immunol. Rev. 2023, 320, 58–82.
19.
Li, J.; Xiao, Z.; Wang, D.; et al. The screening, identification, design and clinical application of tumor-specific neoantigens for TCR-T cells. Mol. Cancer 2023, 22, 141.
20.
Sidhom, J.W.; Larman, H.B.; Pardoll, D.M.; et al. DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires. Nat. Commun. 2021, 12, 1605.
21.
Lu, T.; Zhang, Z.; Zhu, J.; et al. Deep learning-based prediction of the T cell receptor–antigen binding specificity. Nat. Mach. Intell. 2021, 3, 864–875.
22.
Pisetsky, D.S. Pathogenesis of autoimmune disease. Nat. Rev. Nephrol. 2023, 19, 509–524.
23.
Porsch, F.; Binder, C.J. Autoimmune diseases and atherosclerotic cardiovascular disease. Nat. Rev. Cardiol. 2024, 21, 780–807.
24.
Shah, H.; Liu, Z.; Guo, W.; et al. Immune-regulating extracellular vesicles: A new frontier in autoimmune disease therapy. Essays Biochem. 2025, 69, 161–168.
25.
Kumar, S.; Kaushik, D.; Sharma, S.K. Autoimmune Disorders: Types, Symptoms, and Risk Factors. In Artificial Intelligence and Autoimmune Diseases: Applications in the Diagnosis, Prognosis, and Therapeutics; Springer: Singapore, 2024; pp. 3–31.
26.
Song, R.; Jia, X.; Zhao, J.; et al. T cell receptor revision and immune repertoire changes in autoimmune diseases. Int. Rev. Immunol. 2022, 41, 517–533.
27.
He, J.; Shen, J.; Luo, W.; et al. Research progress on application of single-cell TCR/BCR sequencing technology to the tumor immune microenvironment, autoimmune diseases, and infectious diseases. Front. Immunol. 2022, 13, 969808.
28.
Field, M.A. Detecting pathogenic variants in autoimmune diseases using high-throughput sequencing. Immunol. Cell Biol. 2021, 99, 146–156.
29.
Jia, X.; Zhai, T.Y.; Wang, B.; et al. High-throughput T cell receptor sequencing reveals differential immune repertoires in autoimmune thyroid diseases. Mol. Cell. Endocrinol. 2022, 550, 111644.
30.
Binson, V.; Thomas, S.; Subramoniam, M.; et al. A review of machine learning algorithms for biomedical applications. Ann. Biomed. Eng. 2024, 52, 1159–1183.
31.
Strzelecki, M.; Badura, P. Machine learning for biomedical application. Appl. Sci. 2022, 12, 2022
32.
Huang, Z.; Shi, Y.; Cai, B.; et al. MALDI-TOF MS combined with magnetic beads for detecting serum protein biomarkers and establishment of boosting decision tree model for diagnosis of systemic lupus erythematosus. Rheumatology 2009, 48, 626–631.
33.
Chen, Y.; Huang, S.; Chen, T.; et al. Machine learning for prediction and risk stratification of lupus nephritis renal flare. Am. J. Nephrol. 2021, 52, 152–160.
34.
Kockelbergh, H. Machine Learning Approaches for Diagnosis of Autoimmune Disease with the T-Cell Receptor Repertoire. Ph.D. Thesis, University of Liverpool, Liverpool, UK, 2024.
35.
Yang, D.; Peng, X.; Zheng, S.; et al. Deep learning-based prediction of autoimmune diseases. Sci. Rep. 2025, 15, 4576.
36.
Danieli, M.G.; Brunetto, S.; Gammeri, L.; et al. Machine learning application in autoimmune diseases: State of art and future prospectives. Autoimmun. Rev. 2024, 23, 103496.
37.
Zhao, Q.; Jiang, Y.; Xiang, S.; et al. Engineered TCR-T cell immunotherapy in anticancer precision medicine: Pros and cons. Front. Immunol. 2021, 12, 658753.
38.
Lin, P.; Lin, Y.; Mai, Z.; et al. Targeting cancer with precision: Strategical insights into TCR-engineered T cell therapies. Theranostics 2025, 15, 300.
39.
Levi, R.; Louzoun, Y. Two step selection for bias in β chain VJ pairing. Front. Immunol. 2022, 13, 906217.
40.
Mitchell, A.M.; Michels, A.W. T cell receptor sequencing in autoimmunity. J. Life Sci. 2020, 2, 38.
41.
Foth, S.; Vlkel, S.; Bauersachs, D.; et al. T cell repertoire during ontogeny and characteristics in inflammatory disorders in adults and childhood. Front. Immunol. 2021, 11, 611573.
42.
Boehncke, W.H.; Brembilla, N.C. Autoreactive T-lymphocytes in inflammatory skin diseases. Front. Immunol. 2019, 10, 1198.
43.
Amoriello, R.; Mariottini, A.; Ballerini, C. Immunosenescence and autoimmunity: Exploiting the T-cell receptor repertoire to investigate the impact of aging on multiple sclerosis. Front. Immunol. 2021, 12, 799380.
44.
Liu, X.; Zhang, W.; Zhao, M.; et al. T cell receptor β repertoires as novel diagnostic markers for systemic lupus erythematosus and rheumatoid arthritis. Ann. Rheum. Dis. 2019, 78, 1070–1078.
45.
Sansom, S.N.; Shikama-Dorn, N.; Zhanybekova, S.; et al. Population and single-cell genomics reveal the Aire dependency, relief from polycomb silencing, and distribution of self-antigen expression in thymic epithelia. Genome Res. 2014, 24, 1918–1931.
46.
Yano, M.; Kuroda, N.; Han, H.; et al. Aire controls the differentiation program of thymic epithelial cells in the medulla for the establishment of self-tolerance. J. Exp. Med. 2008, 205, 2827–2838.
47.
Akiyama, T.; Shinzawa, M.; Qin, J.; et al. Regulations of gene expression in medullary thymic epithelial cells required for preventing the onset of autoimmune diseases. Front. Immunol. 2013, 4, 249.
48.
Wang, J.; Chitsaz, F.; Derbyshire, M.K.; et al. The conserved domain database in 2023. Nucleic Acids Res. 2023, 51, D384–D388.
49.
ElTanbouly, M.A.; Noelle, R.J. Rethinking peripheral T cell tolerance: Checkpoints across a T cell’s journey. Nat. Rev. Immunol. 2021, 21, 257–267.
50.
Hatano, H.; Ishigaki, K. Functional genetics to understand the etiology of autoimmunity. Genes 2023, 14, 572.
51.
Prinz, J.C. Immunogenic self-peptides-the great unknowns in autoimmunity: Identifying T-cell epitopes driving the autoimmune response in autoimmune diseases. Front. Immunol. 2023, 13, 1097871.
52.
Agapiou, M. Revisiting Development and Homeostasis of Thymic Regulatory T Cells in Type 1 Diabetes. Ph.D. Thesis, University of York, York, UK, 2017.
53.
Coder, B. Thymic Involution Perturbs Negative Selection and Leads to Chronic Inflammation. Ph.D. Thesis, University of North Texas Health Science Center at Fort Worth, Fort Worth, TX, USA, 2015.
54.
Bainter, W.; Lougaris, V.; Wallace, J.G.; et al. Combined immunodeficiency with autoimmunity caused by a homozygous missense mutation in inhibitor of nuclear factor κB kinase alpha (IKKα). Sci. Immunol. 2021, 6, eabf6723.
55.
Xue, Z.; Wu, L.; Tian, R.; et al. Integrative mapping of human CD8+ T cells in inflammation and cancer. Nat. Methods 2025, 22, 435–445.
56.
Rojas, M.; Acosta-Ampudia, Y.; Heuer, L.S.; et al. Antigen-specific T cells and autoimmunity. J. Autoimmun. 2024, 148, 103303.
57.
Marrero, I.; Aguilera, C.; Hamm, D.E.; et al. High-throughput sequencing reveals restricted TCR Vβ usage and public TCRβ clonotypes among pancreatic lymph node memory CD4+ T cells and their involvement in autoimmune diabetes. Mol. Immunol. 2016, 74, 82–95.
58.
Oh, S. The Effect of T Cell Receptor Specificity on CD4+ CD25+ Regulatory T Cell Function in an Autoimmune Setting. Ph.D. Thesis, University of Pennsylvania, Philadelphia, PA, USA, 2010.
59.
Layzell, S.J. The Role of IKK Dignalling in T Cells. Ph.D. Thesis, University College London, London, UK, 2022.
60.
Sogkas, G.; Atschekzei, F.; Adriawan, I.R.; et al. Cellular and molecular mechanisms breaking immune tolerance in inborn errors of immunity. Cell. Mol. Immunol. 2021, 18, 1122–1140.
61.
Sundaresan, B.; Shirafkan, F.; Ripperger, K.; et al. The role of viral infections in the onset of autoimmune diseases. Viruses 2023, 15, 782.
62.
Heimli, M. Characterization of Regulatory T Cells in Autoimmune Polyendocrine Syndrome Type I, a Model Disease for Autoimmunity. Master’s Thesis, The University of Bergen, Bergen, Norway, 2018.
63.
Shokeen, N.; Saini, C.; Sapra, L.; et al. Role of Regulatory T Lymphocytes in Health and Disease. In Systems and Synthetic Immunology; Springer: Singapore, 2020; pp. 201–243.
64.
Huang, F.; Sattler, S. Regulatory T Cell Deficiency in Systemic Autoimmune Disorders-Causal Relationship and Underlying Immunological Mechanisms; InTech: Rijeka, Croatia, 2011.
65.
Bayley, R. Altered Leukocyte Signalling Thresholds in Rheumatoid Arthritis through Changes in the Function of the Protein Tyrosine Phosphatase PTPN22/LYP. Ph.D. Thesis, University of Birmingham, Birmingham, UK, 2014.
66.
Li, Y.; Jiang, W.; Mellins, E.D. TCR-like antibodies targeting autoantigen-MHC complexes: A mini-review. Front. Immunol. 2022, 13, 968432.
67.
Pratigya, G. Deciphering the Link between PTPN22 and Autoimmunity. Ph.D. Thesis, University of Birmingham, Birmingham, UK, 2011.
68.
Macaulay, R. The Role of Immune Inhibitory Receptors in Age-Associated Immune Decline. Ph.D. Thesis, University College London, London, UK, 2011.
69.
Weber, A. T Cell Receptor Specificity Profiling: A Machine Learning Approach. Ph.D. Thesis, ETH Zurich, Zurich, Switzerland, 2023.
70.
Müller, L.; Pawelec, G.; Derhovanessian, E. The Immune System during Ageing. In Diet, Immunity and Inflammation; Elsevier: Amsterdam, The Netherlands, 2013, pp. 631–651.
71.
Naumova, E.N.; Naumov, Y.N.; Gorski, J. Measuring Immunological Age: From T Cell Repertoires to Populations. In Handbook of Immunosenescence; Springer: Berlin/Heidelberg, Germany, 2018, pp. 1–62.
72.
Attaf, M.; Huseby, E.; Sewell, A.K. αβ T cell receptors as predictors of health and disease. Cell. Mol. Immunol. 2015, 12, 391–399.
73.
Hou, X.; Chen, J.; Lu, C.; et al. The conserved T cell receptor repertoire observed in patients with systemic lupus erythematosus. Int. J. Clin. Exp. Med. 2017, 10, 2053–2065.
74.
Mitchell, A.M.; Baschal, E.E.; McDaniel, K.A.; et al. Temporal development of T cell receptor repertoires during childhood in health and disease. JCI Insight 2022, 7, e161885.
75.
Hou, X.; Wei, W.; Zhang, J.; et al. Characterisation of T and B cell receptor repertoire in patients with systemic lupus erythematosus. Clin. Exp. Rheumatol. 2023, 41, 2216–2223.
76.
Sui, W.; Hou, X.; Zou, G.; et al. Composition and variation analysis of the TCR β-chain CDR3 repertoire in systemic lupus erythematosus using high-throughput sequencing. Mol. Immunol. 2015, 67, 455–464.
77.
Ye, X.; Wang, Z.; Ye, Q.; et al. High-throughput sequencing-based analysis of T cell repertoire in lupus nephritis. Front. Immunol. 2020, 11, 1618.
78.
Garrido-Mesa, J.; Brown, M.A. Antigen-driven T cell responses in rheumatic diseases: Insights from T cell receptor repertoire studies. Nat. Rev. Rheumatol. 2025, 21, 157–173.
79.
Aterido, A.; López-Lasanta, M.; Blanco, F.; et al. Seven-chain adaptive immune receptor repertoire analysis in rheumatoid arthritis reveals novel features associated with disease and clinically relevant phenotypes. Genome Biol. 2024, 25, 68.
80.
Turcinov, S.; af Klint, E.; Van Schoubroeck, B.; et al. Diversity and clonality of T cell receptor repertoire and antigen specificities in small joints of early rheumatoid arthritis. Arthritis Rheumatol. 2023, 75, 673–684.
81.
Zhang, L.; Jiao, W.; Deng, H.; et al. High-throughput Treg cell receptor sequencing reveals differential immune repertoires in rheumatoid arthritis with kidney deficiency. PeerJ 2023, 11, e14837.
82.
Amoriello, R. T-Cell Response in Relapsing-Remitting Multiple Sclerosis: A Computational Approach to T-Cell Receptor Repertoire Diversity before and during Disease-Modifying Therapies. Ph.D. Thesis, University of Florence, Firenze, Italy, 2020.
83.
Dunlap, G.; Wagner, A.; Meednu, N.; et al. Clonal associations of lymphocyte subsets and functional states revealed by single cell. bioRxiv 2023, 2023.03.18.533282.
84.
Alves Sousa, A.P.; Johnson, K.R.; Ohayon, J.; et al. Comprehensive analysis of TCR-β repertoire in patients with neurological immune-mediated disorders. Sci. Rep. 2019, 9, 344.
85.
Hayashi, F.; Isobe, N.; Glanville, J.; et al. A new clustering method identifies multiple sclerosis-specific T-cell receptors. Ann. Clin. Transl. Neurol. 2021, 8, 163–176.
86.
Amoriello, R.; Chernigovskaya, M.; Greiff, V.; et al. TCR repertoire diversity in multiple sclerosis: High-dimensional bioinformatics analysis of sequences from brain, cerebrospinal fluid and peripheral blood. EBioMedicine 2021, 68, 103429.
87.
Valkiers, S.; Dams, A.; Kuznetsova, M.; et al. Linking myelin and Epstein-Barr virus specific immune responses in multiple sclerosis: Insights from integrated public T cell receptor repertoires. bioRxiv 2024, 2024-10.
88.
Massey, J. Extensive Reshaping of the T Cell Repertoire Following Autologous Haematopoietic Stem Cell Transplantation in Multiple Sclerosis. Ph.D. Thesis, UNSW Sydney, Sydney, Australia, 2021.
89.
Tong, Y.; Li, Z.; Zhang, H.; et al. T cell repertoire diversity is decreased in type 1 diabetes patients. Genom. Proteom. Bioinform. 2016, 14, 338–348.
90.
Eugster, A.; Lindner, A.; Catani, M.; et al. High diversity in the TCR repertoire of GAD65 autoantigen-specific human CD4+ T cells. J. Immunol. 2015, 194, 2531–2538.
91.
Savola, P.; Kelkka, T.; Rajala, H.; et al. Somatic mutations in clonally expanded cytotoxic T lymphocytes in patients with newly diagnosed rheumatoid arthritis. Nat. Commun. 2017, 8, 15869.
92.
Ramien, C.; Yusko, E.C.; Engler, J.B.; et al. T cell repertoire dynamics during pregnancy in multiple sclerosis. Cell Rep. 2019, 29, 810–815.
93.
Martinez Carmona, K.; Lothert, P.K.; Fedyshyn, B.; et al. Characterization of maternal and fetal immunity following in utero spina bifida repair and surgery-induced preterm birth. Prenat. Diagn. 2025, 45, 1816–1826.
94.
Vita, R.; Mahajan, S.; Overton, J.A.; et al. The immune epitope database (IEDB): 2018 update. Nucleic Acids Res. 2019, 47, D339–D343.
95.
Bagaev, D.V.; Vroomans, R.M.; Samir, J.; et al. VDJdb in 2019: Database extension, new analysis infrastructure and a T-cell receptor motif compendium. Nucleic Acids Res. 2020, 48, D1057–D1062.
96.
Tickotsky, N.; Sagiv, T.; Prilusky, J.; et al. McPAS-TCR: A manually curated catalogue of pathology-associated T cell receptor sequences. Bioinformatics 2017, 33, 2924–2929.
97.
Vander Heiden, J.A.; Marquez, S.; Marthandan, N.; et al. AIRR community standardized representations for annotated immune repertoires. Front. Immunol. 2018, 9, 2206.
98.
Corrie, B.D.; Marthandan, N.; Zimonja, B.; et al. iReceptor: A platform for querying and analyzing antibody/B-cell and T-cell receptor repertoire data across federated repositories. Immunol. Rev. 2018, 284, 24–41.
99.
Dibble, J.J.; Ferneyhough, B.; Roddis, M.; et al. Comparison of T-cell receptor diversity of people with myalgic encephalomyelitis versus controls. BMC Res. Notes 2024, 17, 17.
100.
Fowler, A.; FitzPatrick, M.; Shanmugarasa, A.; et al. An interpretable classification model using gluten-specific TCR sequences shows diagnostic potential in coeliac disease. Biomolecules 2023, 13, 1707.
101.
Ma, J.; Cui, C.; Tang, Y.; et al. Machine learning models developed and internally validated for predicting chronicity in pediatric immune thrombocytopenia. J. Thromb. Haemost. 2024, 22, 1167–1178.
102.
Shen, T.; Huo, M.; Nie, W.; et al. DeepTAPE: Enhancing systemic lupus erythematosus diagnosis with deep learning based on TCRβ CDR3 sequences. In Proceedings of the 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Lisboa, Portugal, 3–6 December 2024; pp. 1149–1154.
103.
He, J.; Liu, Z.; Tang, X. A deep learning model for predicting systemic lupus erythematosus-associated epitopes. BMC Med. Inform. Decis. Mak. 2025, 25, 230.
104.
Rawat, P.; Shapiro, M.R.; Peters, L.D.; et al. Identification of a type 1 diabetes-associated T cell receptor repertoire signature from the human peripheral blood. Sci. Adv. 2026, 12, eadx7448.
105.
Geirhos, R.; Jacobsen, J.H.; Michaelis, C.; et al. Shortcut learning in deep neural networks. Nat. Mach. Intell. 2020, 2, 665–673.
106.
Ostmeyer, J.; Christley, S.; Rounds, W.H.; et al. Statistical classifiers for diagnosing disease from immune repertoires: A case study using multiple sclerosis. BMC Bioinform. 2017, 18, 401.
107.
Demerdash, O.N.; Smith, J.C. TCR-H: Explainable machine learning prediction of T-cell receptor epitope binding on unseen datasets. Front. Immunol. 2024, 15, 1426173.
108.
Darmawan, J.T.; Leu, J.S.; Avian, C.; et al. MITNet: A fusion transformer and convolutional neural network architecture approach for T-cell epitope prediction. Brief. Bioinform. 2023, 24, bbad202.
109.
Wang, M.; Fan, W.; Wu, T.; et al. TPEPret: A deep learning model for characterizing T-cell receptors-antigen binding patterns. Bioinformatics 2025, 41, btaf022.
110.
Guo, S.; Wu, D.O. Game theoretical AI for precision medicine. Trans. Artif. Intell. 2025, 1, 170–196.
111.
Walsh, L.A.; Quail, D.F. Decoding the tumor microenvironment with spatial technologies. Nat. Immunol. 2023, 24, 1982–1993.
112.
Nagano, Y. Overcoming Data Bottlenecks in T Cell Receptor Specificity Prediction with Effective Machine Learning. Ph.D. Thesis, University College London, London, UK, 2024.
113.
Weber, A.; Plissier, A.; Martínez, M.R. T-cell receptor binding prediction: A machine learning revolution. ImmunoInformatics 2024, 15, 100040.
114.
Zeng, Y.; Gao, Y.; He, L.; et al. Smart delivery vehicles for cancer: Categories, unique roles and therapeutic strategies. Nanoscale Adv. 2024, 6, 4275–4308.
115.
Harris, J.C. Explainable Machine Learning in the Field of V(D)J Recombination. Ph.D. Thesis, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA, 2025.
116.
Tu, L.; Xu, A.; Lou, H.; et al. Unveiling fundamental principles: Visualizing T cell immunity with explainable artificial intelligence. Med. Plus 2025, 2, 100072.
117.
Tan, C.L.; Lindner, K.; Boschert, T.; et al. Prediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy. Nat. Biotechnol. 2025, 43, 134–142.
118.
Sun, H.; Han, X.; Du, Z.; et al. Machine learning for the identification of neoantigen-reactive CD8+ T cells in gastrointestinal cancer using single-cell sequencing. Br. J. Cancer 2024, 131, 387–402.
119.
Milighetti, M. Analysis of T Cell Receptor Sequence and Structure to Understand the Drivers of Antigen Specificity. Ph.D. Thesis, University College London, London, UK, 2023.
120.
Whalley, T. Novel Bioinformatics Tools for Epitope-Based Peptide Vaccine Design. Ph.D. Thesis, Cardiff University, Cardiff, UK, 2022.
121.
Qi, F.; Huang, Q.; Xuan, Y.; et al. A roadmap for T cell receptor-peptide-bound major histocompatibility complex binding prediction by machine learning: Glimpse and foresight. Brief. Bioinform. 2025, 26, bbaf327.
122.
Katayama, Y.; Kobayashi, T.J. Comparative study of repertoire classification methods reveals data efficiency of k-mer feature extraction. Front. Immunol. 2022, 13, 797640.
123.
Katayama, Y.; Yokota, R.; Akiyama, T.; et al. Machine learning approaches to TCR repertoire analysis. Front. Immunol. 2022, 13, 858057.
124.
Kidd, B.; Dudley, J. Systems Immunology. In Translational Immunology: Mechanisms and Pharmacologic Approaches; Elsevier: Amsterdam, The Netherlands, 2015; p. 1.
125.
Vivas, A.J.; Boumediene, S.; Tobón, G.J. Predicting autoimmune diseases: A comprehensive review of classic biomarkers and advances in artificial intelligence. Autoimmun. Rev. 2024, 23, 103611.
126.
Xu, X.; Li, J.; Zhu, Z.; et al. A comprehensive review on synergy of multi-modal data and AI technologies in medical diagnosis. Bioengineering 2024, 11, 219.
127.
Baulu, E.; Gardet, C.; Chuvin, N.; et al. TCR-engineered T cell therapy in solid tumors: State of the art and perspectives. Sci. Adv. 2023, 9, eadf3700.
128.
Zhang, J.; Wang, L. The emerging world of TCR-T cell trials against cancer: A systematic review. Technol. Cancer Res. Treat. 2019, 18, 1533033819831068.
129.
Dhusia, K.; Su, Z.; Wu, Y. A structural-based machine learning method to classify binding affinities between TCR and peptide-MHC complexes. Mol. Immunol. 2021, 139, 76–86.
130.
Deng, L.; Ly, C.; Abdollahi, S.; et al. Performance comparison of TCR-pMHC prediction tools reveals a strong data dependency. Front. Immunol. 2023, 14, 1128326.
131.
Dens, C.; Bittremieux, W.; Affaticati, F.; et al. Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interaction predictions. ImmunoInformatics 2023, 11, 100027.
132.
Parr, T.; Bhat, A.; Zeidman, P.; et al. Dynamic causal modelling of immune heterogeneity. Sci. Rep. 2021, 11, 11400.

Scilight Press

Author Information

Abstract

Keywords

References

About Scilight

Journals

Publishing Policies

Contact Us