ArticleCommunications biology2023
Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 42 citations in OpenAlex.
- Machine Learning of Personal Repertoires From Public T Cell Receptors.Immunological reviews · 2026Review
- Heterogeneity and dynamics of DENV-specific CD8 + T cells in dengue infection.Nature communications · 2026Article
- Comparative review of artificial intelligence for transcriptomic biomarker discovery in coronavirus disease 2019 (COVID-19).Briefings in bioinformatics · 2026Review
- Medical software for precision diagnostics of infection with immunoprofiling and artificial intelligence.Journal of translational medicine · 2026Review
- Evaluating the utility of amino acid similarity-aware kmers to represent TCR repertoires for classification.PLoS computational biology · 2026Article
- Inference of SARS-CoV-2 exposure biomarkers using large-scale T-cell repertoire profiling.Genome medicine · 2026Article
- A multi-branch ensemble learning framework for detection of non-small cell lung cancer via T-cell receptor sequencing.BMC cancer · 2026Article
- HELP-TCR: Harmonized Explainable Language Processing Toolkit for T Cell Antigen Receptor Repertoires.Computational and structural biotechnology journal · 2026Article
- Machine learning in AIRR diagnostics: Advances and applications.Immunoinformatics (Amsterdam, Netherlands) · 2025Article
- Challenges and future directions of AIRR-seq-based diagnostics.Immunoinformatics (Amsterdam, Netherlands) · 2025Article
- Decoding the adaptive immune repertoire for disease prediction.Nature reviews. Rheumatology · 2025Article
- Immune age is correlated with decreased TCR clonal diversity and antibody response to SARS-CoV-2.Scientific reports · 2025Article
- EpicPred: predicting phenotypes driven by epitope-binding TCRs using attention-based multiple instance learning.Bioinformatics (Oxford, England) · 2025Article
- T-cell exhaustion in COVID-19: what do we know?Frontiers in immunology · 2025Review
- Optimizing clustering of CDR3 sequences using natural language processing, Word2Vec, and KMeans.Frontiers in bioinformatics · 2025Article
- Patterns of restricted TCR usage following SARS-CoV-2 vaccination and severe disease.Frontiers in immunology · 2025Article
- Integration of T cell repertoire, CyTOF, genotyping and symptomatology data reveals subphenotypic variability in COVID-19 patients.Computational and structural biotechnology journal · 2025Article
- KDDC: a new framework that integrates kmers, dataset filtering, dimension reduction and classification algorithms to achieve immune cell heterogeneity classification.Frontiers in immunology · 2025Article
- Article
- Unraveling the copper-death connection: Decoding COVID-19's immune landscape through advanced bioinformatics and machine learning approaches.Human vaccines & immunotherapeutics · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
T cell receptor (TCR) repertoires are critical for antiviral immunity. Determining the TCR repertoire composition, diversity, and dynamics and how they change during viral infection can inform the molecular specificity of host responses to viruses such as SARS-CoV-2. To determine signatures associated with COVID-19 disease severity, here we perform a large-scale analysis of over 4.7 billion sequences across 2130 TCR repertoires from COVID-19 patients and healthy donors. TCR repertoire analyses from these data identify and characterize convergent COVID-19-associated CDR3 gene usages, specificity groups, and sequence patterns. Here we show that T cell clonal expansion is associated with the upregulation of T cell effector function, TCR signaling, NF-kB signaling, and interferon-gamma signaling pathways. We also demonstrate that machine learning approaches accurately predict COVID-19 infection based on TCR sequence features, with certain high-power models reaching near-perfect AUROC scores. These analyses provide a systems immunology view of T cell adaptive immune responses to COVID-19.
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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.