ArticleFrontiers in immunology2024
TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasets.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- MHCXGraph: a graph-based approach to detecting T-cell receptor cross-reactivity.Briefings in bioinformatics · 2026Article
- Modeling TCR-Epitope Recognition Specificity: What We Should Learn to Succeed.Immunological reviews · 2026Review
- Decoding TCR recognition via geometric deep learning of immunological fingerprints.Briefings in bioinformatics · 2026Article
- Identification of a type 1 diabetes-associated T cell receptor repertoire signature from the human peripheral blood.Science advances · 2026Article
- TCR representation learning with protein language models: a comprehensive review.International immunology · 2026Review
- AI-powered mapping of tumor immunity for optimized mRNA vaccine engineering.Frontiers in oncology · 2026Review
- AI-driven discovery in protein science for immunology and infectious disease research.Frontiers in bioinformatics · 2026Review
- Computational identification of B- and T-cell epitopes: a unified task taxonomy and review of databases, datasets, predictive pipelines, and gaps.Frontiers in immunology · 2026Review
- Assessment of computational methods in predicting TCR-epitope binding recognition.Nature methods · 2026Article
- Assessing data size requirements for training generalizable sequence-based TCR specificity models via pan-allelic MHC-I point-mutation ligandome evaluation.Scientific reports · 2025Article
- AI-driven epitope prediction: a system review, comparative analysis, and practical guide for vaccine development.NPJ vaccines · 2025Review
- Origins of T-cell-mediated autoimmunity in acquired aplastic anaemia.British journal of haematology · 2025Review
- The dawn of biophysical representations in computational immunology.QRB discovery · 2025Review
- T-cell receptor dynamics in digestive system cancers: a multi-layer machine learning approach for tumor diagnosis and staging.Frontiers in immunology · 2025Article
- Artificial intelligence and machine learning in the development of vaccines and immunotherapeutics-yesterday, today, and tomorrow.Frontiers in artificial intelligence · 2025Review
- Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics.bioRxiv : the preprint server for biology · 2024Article
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3 authors.
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Abstract
Artificial-intelligence and machine-learning (AI/ML) approaches to predicting T-cell receptor (TCR)-epitope specificity achieve high performance metrics on test datasets which include sequences that are also part of the training set but fail to generalize to test sets consisting of epitopes and TCRs that are absent from the training set, i.e., are 'unseen' during training of the ML model. We present TCR-H, a supervised classification Support Vector Machines model using physicochemical features trained on the largest dataset available to date using only experimentally validated non-binders as negative datapoints. TCR-H exhibits an area under the curve of the receiver-operator characteristic (AUC of ROC) of 0.87 for epitope 'hard splitting' (i.e., on test sets with all epitopes unseen during ML training), 0.92 for TCR hard splitting and 0.89 for 'strict splitting' in which neither the epitopes nor the TCRs in the test set are seen in the training data. Furthermore, we employ the SHAP (Shapley additive explanations) eXplainable AI (XAI) method for
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