ArticleNucleic acids research2024
Language model-based B cell receptor sequence embeddings can effectively encode receptor specificity.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 26 citations in OpenAlex.
- Language Model Embedding Classifiers Enable Identification of Multiple Sclerosis-Associated BCRs and Repertoires.bioRxiv : the preprint server for biology · 2026Article
- PEPE: scalable extraction of multi-modal protein language model representations.Bioinformatics (Oxford, England) · 2026Article
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- Article
- AMULETY: A Python package to embed adaptive immune receptor sequences.Immunoinformatics (Amsterdam, Netherlands) · 2026Article
- CoMBCR: Co-Learning Multi-Modalities of BCRs and gene expressions.Bioinformatics (Oxford, England) · 2026Article
- Biological sequence analysis: Advances, medical applications, and challenges.Fundamental research · 2026Review
- Optimizing the breadth of SARS-CoV-2-neutralizing antibodies in vivo and in silico.Human vaccines & immunotherapeutics · 2025Review
- Separating selection from mutation in antibody language models.bioRxiv : the preprint server for biology · 2025Article
- Enhancing sequence alignment of adaptive immune receptors through multi-task deep learning.Nucleic acids research · 2025Article
- Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction.PLoS computational biology · 2025Article
- Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence.Journal of biomedical science · 2025Review
- Article
- AI and immunology as a new research paradigm.Nature immunology · 2024Article
- RAIN: machine learning-based identification for HIV-1 bNAbs.Nature communications · 2024Article
- RAIN: a Machine Learning-based identification for HIV-1 bNAbs.Research square · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
High throughput sequencing of B cell receptors (BCRs) is increasingly applied to study the immense diversity of antibodies. Learning biologically meaningful embeddings of BCR sequences is beneficial for predictive modeling. Several embedding methods have been developed for BCRs, but no direct performance benchmarking exists. Moreover, the impact of the input sequence length and paired-chain information on the prediction remains to be explored. We evaluated the performance of multiple embedding models to predict BCR sequence properties and receptor specificity. Despite the differences in model architectures, most embeddings effectively capture BCR sequence properties and specificity. BCR-specific embeddings slightly outperform general protein language models in predicting specificity. In addition, incorporating full-length heavy chains and paired light chain sequences improves the prediction performance of all embeddings. This study provides insights into the properties of BCR embeddings to improve downstream prediction applications for antibody analysis and discovery.
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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.