ArticleBioinformatics (Oxford, England)2023
BERTrand-peptide:TCR binding prediction using Bidirectional Encoder Representations from Transformers augmented with random TCR pairing.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transformers meets neoantigen detection: a systematic literature review.Journal of integrative bioinformatics · 2024Pooled it
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Mapping the TCR landscape: computational tools empowering translational immunology and therapy design.Journal for immunotherapy of cancer · 2026Review
- Quantifying Cross-Attention Interaction in Transformers for Interpreting TCR-pMHC Binding.ArXiv · 2026Article
- TCR representation learning with protein language models: a comprehensive review.International immunology · 2026Review
- Disrupting explicit encoding paradigms: property-interactive transformers decode T-cell receptor specificity beyond dataset biases.Briefings in bioinformatics · 2025Article
- Article
- Toward high-efficiency, low-resource, and explainable neuropeptide prediction with MSKDNP.Briefings in bioinformatics · 2025Article
- Benchmarking of T cell receptor-epitope predictors with ePytope-TCR.Cell genomics · 2025Article
- A roadmap for T cell receptor-peptide-bound major histocompatibility complex binding prediction by machine learning: glimpse and foresight.Briefings in bioinformatics · 2025Review
- TCRCluster: a novel approach to T-cell receptor latent featurization and clustering using contrastive learning-guided two-stage variational autoencoders.NAR genomics and bioinformatics · 2025Article
- AI/ML-empowered approaches for predicting T Cell-mediated immunity and beyond.Frontiers in immunology · 2025Article
- Masked language modeling pretraining dynamics for downstream peptide: T-cell receptor binding prediction.Bioinformatics advances · 2025Article
- TSpred: a robust prediction framework for TCR-epitope interactions using paired chain TCR sequence data.Bioinformatics (Oxford, England) · 2024Article
- TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasets.Frontiers in immunology · 2024Article
- epiTCR-KDA: knowledge distillation model on dihedral angles for TCR-peptide prediction.Bioinformatics advances · 2024Article
- Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy.Frontiers in immunology · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationThe advent of T-cell receptor (TCR) sequencing experiments allowed for a significant increase in the amount of peptide:TCR binding data available and a number of machine-learning models appeared in recent years. High-quality prediction models for a fixed epitope sequence are feasible, provided enough known binding TCR sequences are available. However, their performance drops significantly for previously unseen peptides.
resultsWe prepare the dataset of known peptide:TCR binders and augment it with negative decoys created using healthy donors' T-cell repertoires. We employ deep learning methods commonly applied in Natural Language Processing to train part a peptide:TCR binding model with a degree of cross-peptide generalization (0.69 AUROC). We demonstrate that BERTrand outperforms the published methods when evaluated on peptide sequences not used during model training. AVAILABILITY AND IMPLEMENTATION: The datasets and the code for model training are available at https://github.com/SFGLab/bertrand.
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