Towards high-quality large-scale T cell receptor antigen specificity data: challenges and promises
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TL;DR - This Nature Methods perspective argues that T cell receptor–antigen specificity research is constrained more by noisy, weakly validated data than by data volume. Better experimental validation, statistical filtering, and multimodal AI could enable reliable large-scale datasets for immunology and therapeutic research.
- High-throughput methods and databases have rapidly expanded available TCR–antigen specificity records.
- Many existing records lack strong validation or contain substantial noise, limiting their utility.
- Rigorous experiments and statistical quality controls are needed to produce high-confidence specificity labels.
- Multimodal AI may help integrate complementary evidence and improve TCR specificity data quality.
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Towards high-quality large-scale T cell receptor antigen specificity data: challenges and promises
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TL;DR - This Nature Methods perspective argues that T cell receptor–antigen specificity research is constrained more by noisy, weakly validated data than by data volume. Better experimental validation, statistical filtering, and multimodal AI could enable reliable large-scale datasets for immunology and therapeutic research.
- High-throughput methods and databases have rapidly expanded available TCR–antigen specificity records.
- Many existing records lack strong validation or contain substantial noise, limiting their utility.
- Rigorous experiments and statistical quality controls are needed to produce high-confidence specificity labels.
- Multimodal AI may help integrate complementary evidence and improve TCR specificity data quality.