ArticleMolecular systems biology2020
Predicting antigen specificity of single T cells based on TCR CDR3 regions.
Article in Molecular systems biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 1 of them a synthesis that pooled it.
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Who cites it
65 citing papers in PubMed, 1 synthesis or guideline pooled it, 119 citations in OpenAlex.
- Transformers meets neoantigen detection: a systematic literature review.Journal of integrative bioinformatics · 2024Pooled it
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- Machine Learning for TCR Repertoire Epitope Annotation and Pattern Discovery.Immunological reviews · 2026Review
- Transfer learning for T-cell response prediction.BMC bioinformatics · 2026Article
- Revised Adaptive Immune Receptor Data in the Immune Epitope Database.bioRxiv : the preprint server for biology · 2026Article
- General prediction of T cell receptor antigen specificity from sequence using AlphaFold 3.bioRxiv : the preprint server for biology · 2026Article
- TCR representation learning with protein language models: a comprehensive review.International immunology · 2026Review
- Computational identification of B- and T-cell epitopes: a unified task taxonomy and review of databases, datasets, predictive pipelines, and gaps.Frontiers in immunology · 2026Review
- Personalizing DNA Cancer Vaccines.Journal of personalized medicine · 2025Review
- Constructing the cure: engineering the next wave of antibody and cellular immune therapies.Journal for immunotherapy of cancer · 2025Review
- Benchmarking of T cell receptor-epitope predictors with ePytope-TCR.Cell genomics · 2025Article
- TRAIT: A Comprehensive Database for T-cell Receptor-antigen Interactions.Genomics, proteomics & bioinformatics · 2025Article
- PepTCR-Net: prediction of multi-class antigen peptides by T-cell receptor sequences with deep learning.Briefings in bioinformatics · 2025Article
- A roadmap for T cell receptor-peptide-bound major histocompatibility complex binding prediction by machine learning: glimpse and foresight.Briefings in bioinformatics · 2025Review
- T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity.Communications biology · 2025Article
- Multimodal learning for scalable representation of high-dimensional medical data.Frontiers in digital health · 2025Article
- Molecular targets and strategies in the development of nucleic acid cancer vaccines: from shared to personalized antigens.Journal of biomedical science · 2024Review
- TCR-T cell therapy: current development approaches, preclinical evaluation, and perspectives on regulatory challenges.Journal of translational medicine · 2024Review
- Predictability of antigen binding based on short motifs in the antibody CDRH3.Briefings in bioinformatics · 2024Article
- Low-frequency CD8NPJ vaccines · 2024Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It has recently become possible to simultaneously assay T-cell specificity with respect to large sets of antigens and the T-cell receptor sequence in high-throughput single-cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T-cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single-cell immune repertoire screens can be mitigated by modeling cell-specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell-to-dextramer binding strength and receptor-to-pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single-cell RNA-seq studies on T cells without the need for MHC staining.
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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 OpenQuestion graph.