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Scalable Universal T-Cell Receptor Embeddings from Adaptive Immune Repertoires
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Proposes scalable universal embeddings of T-cell receptor repertoires learned from adaptive immune sequencing data, evaluated on TCR-related tasks.
Problem
High-throughput TCR repertoire data are heterogeneous, high-dimensional, sparse, and mostly unlabeled, making it hard to learn representations of T cell specificity and immune history.
Approach
Learns universal embeddings for sets of TCRs from unlabeled adaptive immune repertoires, designed to scale to large repertoire datasets; the abstract is truncated so specifics are not stated.
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