ArticleBriefings in bioinformatics2023
Accurate TCR-pMHC interaction prediction using a BERT-based transfer learning method.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Predicting specificity of TCR-pMHC interactions using machine-learning and biophysical models.Cell systems · 2026Article
- Structure-based TCR-pMHC binding prediction and generalization to unseen peptides.npj drug discovery · 2026Article
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- Computational identification of antigen-specific T cell groups through generative epitope modeling.iScience · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens.Nature communications · 2026Article
- Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.Journal of translational medicine · 2026Review
- Transfer learning for T-cell response prediction.BMC bioinformatics · 2026Article
- Protein language models accurately predict polymorphic peptide-modulated NK cell receptor-HLA class I interaction strengths.Science advances · 2026Article
- Mechanistic biomarkers for cancer vaccine development: from cancer cell biology to neoantigen-guided precision immunotherapy.Frontiers in cell and developmental biology · 2026Review
- Disrupting explicit encoding paradigms: property-interactive transformers decode T-cell receptor specificity beyond dataset biases.Briefings in bioinformatics · 2025Article
- THLANet: A deep learning framework for predicting TCR-pHLA binding in immunotherapy applications.PLoS computational biology · 2025Article
- Is the vaccination-induced B cell receptor repertoire predictable?Immunoinformatics (Amsterdam, Netherlands) · 2025Article
- Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy.Biomarker research · 2025Review
- A roadmap for T cell receptor-peptide-bound major histocompatibility complex binding prediction by machine learning: glimpse and foresight.Briefings in bioinformatics · 2025Review
- LightCTL: lightweight contrastive TCR-pMHC specificity learning with context-aware prompt.Briefings in bioinformatics · 2025Article
- AI/ML-empowered approaches for predicting T Cell-mediated immunity and beyond.Frontiers in immunology · 2025Article
- Targeting cancer with precision: strategical insights into TCR-engineered T cell therapies.Theranostics · 2025Review
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Accurate prediction of TCR-pMHC binding is important for the development of cancer immunotherapies, especially TCR-based agents. Existing algorithms often experience diminished performance when dealing with unseen epitopes, primarily due to the complexity in TCR-pMHC recognition patterns and the scarcity of available data for training. We have developed a novel deep learning model, 'TCR Antigen Binding Recognition' based on BERT, named as TABR-BERT. Leveraging BERT's potent representation learning capabilities, TABR-BERT effectively captures essential information regarding TCR-pMHC interactions from TCR sequences, antigen epitope sequences and epitope-MHC binding. By transferring this knowledge to predict TCR-pMHC recognition, TABR-BERT demonstrated better results in benchmark tests than existing methods, particularly for unseen epitopes.
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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.