ArticleFrontiers in immunology2021
Machine Learning Analysis of Naïve B-Cell Receptor Repertoires Stratifies Celiac Disease Patients and Controls.
Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data.PLoS computational biology · 2026Article
- Naïve adaptive immune receptor repertoires in celiac disease assessed by machine learning; impact of the HLA-DQ2.5 allotype on the TCR repertoire.Immunogenetics · 2026Article
- Machine Learning of Personal Repertoires From Public T Cell Receptors.Immunological reviews · 2026Review
- Evaluating the utility of amino acid similarity-aware kmers to represent TCR repertoires for classification.PLoS computational biology · 2026Article
- Review
- The current landscape of adaptive immune receptor genomic and repertoire data: OGRDB and VDJbase.Nucleic acids research · 2026Article
- Germline based SARS-CoV-2 specific B cell repertoire motif identified with novel sequence based bioinformatic pipeline.Frontiers in immunology · 2026Article
- Machine learning in AIRR diagnostics: Advances and applications.Immunoinformatics (Amsterdam, Netherlands) · 2025Article
- Enhancing sequence alignment of adaptive immune receptors through multi-task deep learning.Nucleic acids research · 2025Article
- 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
- Toward Precision Medicine in Celiac Disease: Emerging Roles of Multiomics and Artificial Intelligence.Analytical cellular pathology (Amsterdam) · 2025Review
- Advancements in Computer-Aided Diagnosis of Celiac Disease: A Systematic Review.Biomimetics (Basel, Switzerland) · 2024Review
- Robust detection of infectious disease, autoimmunity, and cancer from the paratope networks of adaptive immune receptors.Briefings in bioinformatics · 2024Article
- B cell receptor repertoire abnormalities in autoimmune disease.Frontiers in immunology · 2024Review
- Deciphering the antigen specificities of antibodies by clustering their complementarity determining region sequences.mSystems · 2023Article
- Application of artificial intelligence to corelate food formulations to disease risk prediction: a comprehensive review.Journal of food science and technology · 2023Review
- Genetic variation in the immunoglobulin heavy chain locus shapes the human antibody repertoire.Nature communications · 2023Article
- A novel approach to T-cell receptor beta chain (TCRB) repertoire encoding using lossless string compression.Bioinformatics (Oxford, England) · 2023Article
- B cell M-CLL clones retain selection against replacement mutations in their immunoglobulin gene framework regions.Frontiers in oncology · 2023Article
- Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity.Frontiers in immunology · 2023Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Celiac disease (CeD) is a common autoimmune disorder caused by an abnormal immune response to dietary gluten proteins. The disease has high heritability. HLA is the major susceptibility factor, and the HLA effect is mediated via presentation of deamidated gluten peptides by disease-associated HLA-DQ variants to CD4+ T cells. In addition to gluten-specific CD4+ T cells the patients have antibodies to transglutaminase 2 (autoantigen) and deamidated gluten peptides. These disease-specific antibodies recognize defined epitopes and they display common usage of specific heavy and light chains across patients. Interactions between T cells and B cells are likely central in the pathogenesis, but how the repertoires of naïve T and B cells relate to the pathogenic effector cells is unexplored. To this end, we applied machine learning classification models to naïve B cell receptor (BCR) repertoires from CeD patients and healthy controls. Strikingly, we obtained a promising classification performance with an F1 score of 85%. Clusters of heavy and light chain sequences were inferred and used as features for the model, and signatures associated with the disease were then characterized. These signatures included amino acid (AA) 3-mers with distinct bio-physiochemical characteristics and enriched V and J genes. We found that CeD-associated clusters can be identified and that common motifs can be characterized from naïve BCR repertoires. The results may indicate a genetic influence by BCR encoding genes in CeD. Analysis of naïve BCRs as presented here may become an important part of assessing the risk of individuals to develop CeD. Our model demonstrates the potential of using BCR repertoires and in particular, naïve BCR repertoires, as disease susceptibility markers.
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