ArticleBriefings in bioinformatics2024
A comprehensive benchmarking for evaluating TCR embeddings in modeling TCR-epitope interactions.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- AI-driven computational methods and benchmarking for T-cell antigen identification.Briefings in bioinformatics · 2026Review
- ClareV: a contrastive learning framework for context-aware TRBV representations in TCR repertoires.Frontiers in immunology · 2026Article
- Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.Frontiers in genetics · 2026Article
- 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
- Benchmarking unsupervised methods for inferring TCR specificity.NAR genomics and bioinformatics · 2025Review
- Disrupting explicit encoding paradigms: property-interactive transformers decode T-cell receptor specificity beyond dataset biases.Briefings in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The complexity of T cell receptor (TCR) sequences, particularly within the complementarity-determining region 3 (CDR3), requires efficient embedding methods for applying machine learning to immunology. While various TCR CDR3 embedding strategies have been proposed, the absence of their systematic evaluations created perplexity in the community. Here, we extracted CDR3 embedding models from 19 existing methods and benchmarked these models with four curated datasets by accessing their impact on the performance of TCR downstream tasks, including TCR-epitope binding affinity prediction, epitope-specific TCR identification, TCR clustering, and visualization analysis. We assessed these models utilizing eight downstream classifiers and five downstream clustering methods, with the performance measured by a diverse range of metrics for precision, robustness, and usability. Overall, handcrafted embeddings outperformed data-driven ones in modeling TCR-epitope interactions. To further refine our comparative findings, we developed an all-in-one TCR CDR3 embedding package comprising all evaluated embedding models. This package will assist users in easily selecting suitable embedding models for their data.
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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.