ArticleBriefings in bioinformatics2021
Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 1 of them a synthesis that pooled it.
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Who cites it
102 citing papers in PubMed, 1 synthesis or guideline pooled it, 181 citations in OpenAlex.
- Transformers meets neoantigen detection: a systematic literature review.Journal of integrative bioinformatics · 2024Pooled it
- Calibrating T cell responsiveness through interactions with self.Nature reviews. Immunology · 2026Review
- Current landscape and future directions of neoantigen vaccines: A new era of personalized cancer immunotherapy.Innovation (Cambridge (Mass.)) · 2026Review
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- Unsupervised identification of low-frequency antigen-specific TCRs using distance-based anomaly scoring.Briefings in bioinformatics · 2026Article
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- Cross-task interpretability through unified modeling reveals a universal shortcut bias in neoantigen prediction.Cell genomics · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Mitigating negative data bias to enhance TCR-epitope binding and residue interaction prediction.Briefings in bioinformatics · 2026Article
- Machine Learning for TCR Repertoire Epitope Annotation and Pattern Discovery.Immunological reviews · 2026Review
- 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
- Deciphering small sequence differences in T cell receptor-antigen pairing.Nature communications · 2026Article
- TCRBinder: Unified pre-trained language model with paired-chain synergy for predicting T-cell receptor binding specificity.PLoS computational biology · 2026Article
- Deep peptide recognition profiling decodes TCR specificity and enables disease-associated antigen discovery.Nature biotechnology · 2026Article
- Supervised fine-tuning enhances unsupervised learning from 45 million amino acids in TCR and peptide sequences.Bioinformatics (Oxford, England) · 2026Article
- Advances in predicting T cell epitope recognition for cancer immunotherapy.Nature cancer · 2026Review
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- AI-driven computational methods and benchmarking for T-cell antigen identification.Briefings in bioinformatics · 2026Review
- A biophysical framework for accurately identifying antigen single-amino acid escape variants and corresponding variant-specific compensatory TCR sequences.bioRxiv : the preprint server for biology · 2026Article
42 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prediction of epitope recognition by T-cell receptors (TCRs) has seen many advancements in recent years, with several methods now available that can predict recognition for a specific set of epitopes. However, the generic case of evaluating all possible TCR-epitope pairs remains challenging, mainly due to the high diversity of the interacting sequences and the limited amount of currently available training data. In this work, we provide an overview of the current state of this unsolved problem. First, we examine appropriate validation strategies to accurately assess the generalization performance of generic TCR-epitope recognition models when applied to both seen and unseen epitopes. In addition, we present a novel feature representation approach, which we call ImRex (interaction map recognition). This approach is based on the pairwise combination of physicochemical properties of the individual amino acids in the CDR3 and epitope sequences, which provides a convolutional neural network with the combined representation of both sequences. Lastly, we highlight various challenges that are specific to TCR-epitope data and that can adversely affect model performance. These include the issue of selecting negative data, the imbalanced epitope distribution of curated TCR-epitope datasets and the potential exchangeability of TCR alpha and beta chains. Our results indicate that while extrapolation to unseen epitopes remains a difficult challenge, ImRex makes this feasible for a subset of epitopes that are not too dissimilar from the training data. We show that appropriate feature engineering methods and rigorous benchmark standards are required to create and validate TCR-epitope predictive models.
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