ReviewFrontiers in immunology2021
Immune2vec: Embedding B/T Cell Receptor Sequences in ℝ
Review 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 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- AI-Driven BCR Modeling for Precision Immunology.International journal of molecular sciences · 2026Review
- AMULETY: A Python package to embed adaptive immune receptor sequences.Immunoinformatics (Amsterdam, Netherlands) · 2026Article
- Enhancing sequence alignment of adaptive immune receptors through multi-task deep learning.Nucleic acids research · 2025Article
- Genome language modeling (GLM): a beginner's cheat sheet.Biology methods & protocols · 2025Article
- Robust detection of infectious disease, autoimmunity, and cancer from the paratope networks of adaptive immune receptors.Briefings in bioinformatics · 2024Article
- Self-supervised learning of T cell receptor sequences exposes core properties for T cell membership.Science advances · 2024Article
- Building Representation Learning Models for Antibody Comprehension.Cold Spring Harbor perspectives in biology · 2024Review
- Language model-based B cell receptor sequence embeddings can effectively encode receptor specificity.Nucleic acids research · 2024Article
- 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
- A somatic hypermutation-based machine learning model stratifies individuals with Crohn's disease and controls.Genome research · 2023Article
- Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity.Frontiers in immunology · 2023Article
- Exploring the impact of clonal definition on B-cell diversity: implications for the analysis of immune repertoires.Frontiers in immunology · 2023Article
- Organizing the bacterial annotation space with amino acid sequence embeddings.BMC bioinformatics · 2022Article
- Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.Briefings in bioinformatics · 2022Review
- Machine Learning Approaches to TCR Repertoire Analysis.Frontiers in immunology · 2022Review
- IgTreeZ, A Toolkit for Immunoglobulin Gene Lineage Tree-Based Analysis, Reveals CDR3s Are Crucial for Selection Analysis.Frontiers in immunology · 2022Article
- Comparative Study of Repertoire Classification Methods Reveals Data Efficiency ofFrontiers in immunology · 2022Article
- AI applications in functional genomics.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The adaptive branch of the immune system learns pathogenic patterns and remembers them for future encounters. It does so through dynamic and diverse repertoires of T- and B- cell receptors (TCR and BCRs, respectively). These huge immune repertoires in each individual present investigators with the challenge of extracting meaningful biological information from multi-dimensional data. The ability to embed these DNA and amino acid textual sequences in a vector-space is an important step towards developing effective analysis methods. Here we present Immune2vec, an adaptation of a natural language processing (NLP)-based embedding technique for BCR repertoire sequencing data. We validate Immune2vec on amino acid 3-gram sequences, continuing to longer BCR sequences, and finally to entire repertoires. Our work demonstrates Immune2vec to be a reliable low-dimensional representation that preserves relevant information of immune sequencing data, such as n-gram properties and IGHV gene family classification. Applying Immune2vec along with machine learning approaches to patient data exemplifies how distinct clinical conditions can be effectively stratified, indicating that the embedding space can be used for feature extraction and exploratory data analysis.
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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.