ArticlePLoS computational biology2021
Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A lightweight TcrLM model predicts T cell receptor and epitope binding specificity.Cell reports methods · 2026Article
- Enhancing sequence alignment of adaptive immune receptors through multi-task deep learning.Nucleic acids research · 2025Article
- Disease diagnostics using machine learning of B cell and T cell receptor sequences.Science (New York, N.Y.) · 2025Article
- Naive and memory T cells TCR-HLA-binding prediction.Oxford open immunology · 2022Article
- Counting is almost all you need.Frontiers in immunology · 2022Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Recent advances in T cell repertoire (TCR) sequencing allow for the characterization of repertoire properties, as well as the frequency and sharing of specific TCR. However, there is no efficient measure for the local density of a given TCR. TCRs are often described either through their Complementary Determining region 3 (CDR3) sequences, or theirV/J usage, or their clone size. We here show that the local repertoire density can be estimated using a combined representation of these components through distance conserving autoencoders and Kernel Density Estimates (KDE). We present ELATE-an Encoder-based LocAl Tcr dEnsity and show that the resulting density of a sample can be used as a novel measure to study repertoire properties. The cross-density between two samples can be used as a similarity matrix to fully characterize samples from the same host. Finally, the same projection in combination with machine learning algorithms can be used to predict TCR-peptide binding through the local density of known TCRs binding a specific target.
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