ReviewFrontiers in immunology2023
Quantitative approaches for decoding the specificity of the human T cell repertoire.
Review in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Predicting specificity of TCR-pMHC interactions using machine-learning and biophysical models.Cell systems · 2026Article
- Thymic selection of the T cell receptor repertoire is biased toward autoimmunity in females.eLife · 2026Article
- Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens.Nature communications · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Decoding TCR recognition via geometric deep learning of immunological fingerprints.Briefings in bioinformatics · 2026Article
- A biophysical framework for accurately identifying antigen single-amino acid escape variants and corresponding variant-specific compensatory TCR sequences.bioRxiv : the preprint server for biology · 2026Article
- Algorithm guided personalized T cell therapy: machine learning unlocks next generation TCR engineered immunotherapy.Pharmacological reports : PR · 2026Review
- Safety-first TCR-T: AI-guided specificity, base-editing to prevent mispairing, and prioritizing public neoantigens.Frontiers in immunology · 2026Article
- STCRpy: a software suite for T-cell receptor structure parsing, interaction profiling, and machine learning dataset preparation.Bioinformatics (Oxford, England) · 2025Article
- High-fidelity in silico generation and augmentation of TCR repertoire data using generative adversarial networks.Scientific reports · 2025Article
- T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity.Communications biology · 2025Article
- STAG-LLM: Predicting TCR-pHLA binding with protein language models and computationally generated 3D structures.Computational and structural biotechnology journal · 2025Article
- Integration of kinetic data into affinity-based models for improved T cell specificity prediction.Biophysical journal · 2024Article
- Limits on inferring T cell specificity from partial information.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Feature Selection Enhances Peptide Binding Predictions for TCR-Specific Interactions.bioRxiv : the preprint server for biology · 2024Article
- RACER-m leverages structural features for sparse T cell specificity prediction.Science advances · 2024Article
- Exosomal microRNAs in regulation of tumor cells resistance to apoptosis.Biochemistry and biophysics reports · 2024Review
- Feature selection enhances peptide binding predictions for TCR-specific interactions.Frontiers in immunology · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method's mathematical approach, predictive performance, and limitations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.