ArticleNature communications2021
DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 172 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
172 citing papers in PubMed, 1 synthesis or guideline pooled it, 274 citations in OpenAlex.
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Recovering CDR3 sequences from RNA-seq data: a critical assessment of computational and experimental strategies.Genomics & informatics · 2026Review
- Spatiotemporal single-cell profiling reveals T cell clonal dynamics and phenotypic plasticity in human graft-versus-host disease.Nature immunology · 2026Article
- Cross-species molecular stratification enables T cell receptor diagnosis of Parkinson's disease.Genome medicine · 2026Article
- Unsupervised identification of low-frequency antigen-specific TCRs using distance-based anomaly scoring.Briefings in bioinformatics · 2026Article
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- Artificial intelligence for translational personalized neoantigen cancer vaccine development.Journal of biomedical science · 2026Review
- A computational method to design broad-spectrum T cell-inducing vaccines applied to Betacoronaviruses.Cell reports methods · 2026Article
- When multimodal fusion fails: contrastive alignment as a necessary stabilizer for TCR-peptide binding prediction.Bioinformatics (Oxford, England) · 2026Article
- Machine Learning of Personal Repertoires From Public T Cell Receptors.Immunological reviews · 2026Review
- The Role of the T Cell Receptor Sequence in Shaping T Cell Functional Fate.Immunological reviews · 2026Review
- Computational identification of antigen-specific T cell groups through generative epitope modeling.iScience · 2026Article
- The challenge and promise of studying human antigen-specific T cells.Nature reviews. Immunology · 2026Review
- T-cell receptor alpha to beta chains binding prediction.Briefings in bioinformatics · 2026Article
- DUET: a graph-based workflow for TCR-epitope prioritization and tumor-reactive T-cell identification.Briefings in bioinformatics · 2026Article
- Mitigating negative data bias to enhance TCR-epitope binding and residue interaction prediction.Briefings in bioinformatics · 2026Article
- A comparative and exploratory analysis of computational methods for TCR structural prediction and antigen-specific TCR discovery.Briefings in bioinformatics · 2026Article
- PU-GRAIL: residue-level graph learning for identifying protective bacterial antigens under positive-unlabeled supervision.Bioinformatics (Oxford, England) · 2026Article
- Modeling TCR-Epitope Recognition Specificity: What We Should Learn to Succeed.Immunological reviews · 2026Review
- Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens.Nature communications · 2026Article
112 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diversity of the adaptive immune system and allows for modeling its sequence determinants of antigenicity. We present DeepTCR, a suite of unsupervised and supervised deep learning methods able to model highly complex TCR sequencing data by learning a joint representation of a TCR by its CDR3 sequences and V/D/J gene usage. We demonstrate the utility of deep learning to provide an improved 'featurization' of the TCR across multiple human and murine datasets, including improved classification of antigen-specific TCRs and extraction of antigen-specific TCRs from noisy single-cell RNA-Seq and T-cell culture-based assays. Our results highlight the flexibility and capacity for deep neural networks to extract meaningful information from complex immunogenomic data for both descriptive and predictive purposes.
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What OpenQuestion holds
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.