ArticleBioinformatics (Oxford, England)2023
EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings.
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 17 papers.
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Who cites it
17 citing papers in PubMed.
- Calibrating T cell responsiveness through interactions with self.Nature reviews. Immunology · 2026Review
- Thymic selection of the T cell receptor repertoire is biased toward autoimmunity in females.eLife · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Modeling TCR-Epitope Recognition Specificity: What We Should Learn to Succeed.Immunological reviews · 2026Review
- Revised Adaptive Immune Receptor Data in the Immune Epitope Database.bioRxiv : the preprint server for biology · 2026Article
- TCR representation learning with protein language models: a comprehensive review.International immunology · 2026Review
- LANTERN: TCR-peptide binding predictionPeerJ · 2026Article
- Modeling TCR-pMHC Binding with Dual Encoders and Cross-Attention Fusion.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2025Article
- Disrupting explicit encoding paradigms: property-interactive transformers decode T-cell receptor specificity beyond dataset biases.Briefings in bioinformatics · 2025Article
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- Current developments in T-cell receptor therapy for acute myeloid leukemia.Blood advances · 2025Review
- EpicPred: predicting phenotypes driven by epitope-binding TCRs using attention-based multiple instance learning.Bioinformatics (Oxford, England) · 2025Article
- Assessing the generalization capabilities of TCR binding predictors via peptide distance analysis.PloS one · 2025Article
- Probing TCR Specificity Using Artificial In Vivo Diversification of CDR3 Regions.European journal of immunology · 2025Article
- Development and Clinical Applications of Therapeutic Cancer Vaccines with Individualized and Shared Neoantigens.Vaccines · 2024Review
- Predicting TCR sequences for unseen antigen epitopes using structural and sequence features.Briefings in bioinformatics · 2024Article
- TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasets.Frontiers in immunology · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
motivationT cells play an essential role in adaptive immune system to fight pathogens and cancer but may also give rise to autoimmune diseases. The recognition of a peptide-MHC (pMHC) complex by a T cell receptor (TCR) is required to elicit an immune response. Many machine learning models have been developed to predict the binding, but generalizing predictions to pMHCs outside the training data remains challenging.
resultsWe have developed a new machine learning model that utilizes information about the TCR from both α and β chains, epitope sequence, and MHC. Our method uses ProtBERT embeddings for the amino acid sequences of both chains and the epitope, as well as convolution and multi-head attention architectures. We show the importance of each input feature as well as the benefit of including epitopes with only a few TCRs to the training data. We evaluate our model on existing databases and show that it compares favorably against other state-of-the-art models. AVAILABILITY AND IMPLEMENTATION: https://github.com/DaniTheOrange/EPIC-TRACE.
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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.