ArticleeLife2019
Deep generative models for T cell receptor protein sequences.
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed.
- Spatiotemporal single-cell profiling reveals T cell clonal dynamics and phenotypic plasticity in human graft-versus-host disease.Nature immunology · 2026Article
- Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data.PLoS computational biology · 2026Article
- What Do Generative Models Learn About Adaptive Immune Receptor Repertoires? A Benchmark Study.bioRxiv : the preprint server for biology · 2026Article
- DTCR: generating realistic, diverse, and epitope-specific T cell receptor sequences via a discrete diffusion model.Briefings in bioinformatics · 2026Article
- Artificial Intelligence in genomics: a comprehensive survey of methods, resources, challenges, and prospects.Briefings in bioinformatics · 2026Review
- Self-Contemplating In-Context Learning Enhances T Cell Receptor Generation for Novel Epitopes.Proceedings of machine learning research · 2025Article
- Enhancing sequence alignment of adaptive immune receptors through multi-task deep learning.Nucleic acids research · 2025Article
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning.Nucleic acids research · 2025Article
- Conditional generation of real antigen-specific T cell receptor sequences.Nature machine intelligence · 2025Article
- Designing meaningful continuous representations of T cell receptor sequences with deep generative models.Nature communications · 2024Article
- Self-supervised learning of T cell receptor sequences exposes core properties for T cell membership.Science advances · 2024Article
- Review
- Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions.Bioengineering (Basel, Switzerland) · 2024Review
- Generative models for protein sequence modeling: recent advances and future directions.Briefings in bioinformatics · 2023Review
- A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity.eLife · 2023Article
- Modeling and predicting the overlap of B- and T-cell receptor repertoires in healthy and SARS-CoV-2 infected individuals.PLoS genetics · 2023Article
- Prediction of designer-recombinases for DNA editing with generative deep learning.Nature communications · 2022Article
- Comparing T cell receptor repertoires using optimal transport.PLoS computational biology · 2022Article
- Access to ground truth at unconstrained size makes simulated data as indispensable as experimental data for bioinformatics methods development and benchmarking.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Probabilistic models of adaptive immune repertoire sequence distributions can be used to infer the expansion of immune cells in response to stimulus, differentiate genetic from environmental factors that determine repertoire sharing, and evaluate the suitability of various target immune sequences for stimulation via vaccination. Classically, these models are defined in terms of a probabilistic V(D)J recombination model which is sometimes combined with a selection model. In this paper we take a different approach, fitting variational autoencoder (VAE) models parameterized by deep neural networks to T cell receptor (TCR) repertoires. We show that simple VAE models can perform accurate cohort frequency estimation, learn the rules of VDJ recombination, and generalize well to unseen sequences. Further, we demonstrate that VAE-like models can distinguish between real sequences and sequences generated according to a recombination-selection model, and that many characteristics of VAE-generated sequences are similar to those of real sequences.
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Identifiers
What OpenQuestion holds
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.