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Related Concept Videos

Diversity of Antigen Receptors01:28

Diversity of Antigen Receptors

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Antigen receptors are essential components of the immune system crucial in defending the body against foreign invaders. These receptors are present on the surface of B and T cells, enabling them to recognize antigens and mount an appropriate immune response.
Before encountering any antigen, lymphocytes express these receptors. On B cells, the antigen receptor is a membrane-bound antibody molecule called BCR; on T cells, it is a T cell receptor or TCR. B and T cell receptors are composed of two...
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B Cell Activation and Differentiation01:24

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The adaptive immune response, a sophisticated defense mechanism, relies on the activation and differentiation of B lymphocytes, or B cells. These processes enable our bodies to mount a tailored response against specific pathogens such as bacteria, free virus particles, toxins, and parasites.
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AI-Driven BCR Modeling for Precision Immunology.

Tao Liu1,2,3, Xusheng Zhao2, Fan Yang2

  • 1Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.

International Journal of Molecular Sciences
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and deep learning models are revolutionizing the analysis of B cell receptor (BCR) repertoires. These advanced methods help uncover immune history and accelerate the discovery of therapeutic antibodies.

Keywords:
BCRantibody repertoiredeep learningimmunogeneticsmachine learning

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Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations
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Area of Science:

  • Immunology
  • Bioinformatics
  • Computational Biology

Background:

  • The B cell receptor (BCR) repertoire reflects an individual's immune history and antigen-driven evolution.
  • Adaptive immune receptor repertoire sequencing (AIRR-seq) provides deep profiling of BCR diversity but faces challenges in interpretation due to heterogeneity and complex relationships.

Purpose of the Study:

  • To summarize how artificial intelligence (AI) and deep learning can address challenges in interpreting BCR repertoire data.
  • To highlight the applications of AI in understanding immune responses and discovering therapeutic antibodies.

Main Methods:

  • Utilizing advanced deep learning architectures such as antibody-specific language models and graph neural networks (GNNs).
  • Applying generative frameworks to analyze BCR sequence data and predict functional properties.
  • Integrating multimodal datasets and interpretable AI for a closed-loop framework.

Main Results:

  • AI methods can uncover clonal topology, structural features, and antigen-binding semantics within BCR repertoires.
  • Deep learning effectively addresses challenges like inter-individual heterogeneity and rare functional clones.
  • Demonstrated applications of AI in cancer, infectious disease, and autoimmunity research.

Conclusions:

  • AI offers a powerful framework for interpreting complex BCR repertoire data.
  • A closed-loop approach integrating AI and experimental validation can advance predictive immunology.
  • AI accelerates the discovery and development of novel therapeutic antibodies.