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ClareV: a contrastive learning framework for context-aware TRBV representations in TCR repertoires
Miaozhe Huo1, Yu Cheng2, Tongfei Shen1
1Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Frontiers in Immunology
|August 7, 2026
Summary
ClareV, a new framework, captures T cell receptor repertoire dynamics for improved antigen recognition. It enhances classification by learning adaptive V-gene representations beyond simple frequency counts.
Area of Science:
- Immunology
- Bioinformatics
- Machine Learning
Background:
- T cell receptor (TCR) V gene segments (TRBV) are vital for antigen recognition.
- Previous studies treated TRBV segments as fixed, overlooking repertoire-specific immune dynamics and functional diversity.
Purpose of the Study:
- To introduce ClareV, a novel contrastive learning framework.
- To partition TCR repertoires into V gene-specific groups for data-driven embeddings.
- To capture immune dynamics informing functional diversity in TCR repertoires.
Main Methods:
- Developed ClareV, a contrastive learning framework.
- Evaluated ClareV on TCR repertoire cohorts: cytomegalovirus (CMV), gastric cancer, and multi-cancer.
- Utilized Random Forest (RF) Fusion model for performance evaluation.
Main Results:
- ClareV RF Fusion improved AUC by 14.3% over V-family usage baselines and 13.3% over V-gene usage baselines in the CMV cohort.
- ClareV embeddings captured repertoire-level information beyond V-frequency summaries.
- Downstream analyses showed ClareV representations recapitulated IMGT V-gene structure and retained CMV-responsive, non-sequence components.
Conclusions:
- Adaptive V-gene representations offer a compact feature space.
- ClareV complements V-frequency summaries for repertoire-level classification.
- The framework enhances understanding of TCR repertoire dynamics and functional diversity.
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