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相关概念视频

T Cell Activation and Clonal Selection01:22

T Cell Activation and Clonal Selection

14.6K
T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
Naive T cells that have not yet encountered an antigen express two primary CD...
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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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相关实验视频

Updated: Jan 10, 2026

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing
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破坏显式编码范式:属性交互式变压器除了数据集偏差之外解码T细胞受体特异性.

Luming Yang1, Haoxian Liu2, Alec Calanche3

  • 1Photogrammetric Computer Vision Lab., The Ohio State University, 2070 Neil Ave, Columbus, OH 43210, United States.

Briefings in bioinformatics
|November 21, 2025
PubMed
概括

一种名为TCRoss的新模型通过模拟空间结构和结合环境数据来预测T细胞受体 (TCR) 和结合. 这种方法提高了准确性,并克服了免疫反应预测现有的深度学习方法的局限性.

关键词:
T细胞受体 (TCRs) 是一种进行交叉映射.结合的质结合.空间结构就是空间结构.变压器的变压器是一个变压器.

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Generation of Human Alloantigen-specific T Cells from Peripheral Blood
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科学领域:

  • 免疫学 免疫学 免疫学
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • T细胞受体 (TCR) 对于免疫监测至关重要,识别特定的抗原.
  • 现有的深度学习模型用于TCR-结合预测,通常学习数据集偏差,忽视生物化学和空间结合特性.

研究的目的:

  • 开发一种新的深度学习模型,通过结合空间和环境信息,准确预测TCR-结合.
  • 克服当前模型的局限性,这些模型因数据集偏差而高估结果.

主要方法:

  • 开发了TCRoss,这是一个基于变压器的模型,利用交叉映射的氨基酸特性来隐式模拟空间结构.
  • 将环境信息纳入培训数据集,以减轻学习偏见.
  • 使用湿实验室T细胞激活试验和生物物理分析验证的模型预测.

主要成果:

  • 通过交叉映射的氨基酸相互作用来模拟空间特性,TCRoss有效地捕获TCR-结相互作用.
  • 包括环境数据改善了模型性能,并减少了数据集偏差.
  • 与现有模型相比,TCRoss在已知和新场景中表现出优越的性能.
  • 湿实验室和生物物理验证证实了该模型的预测准确性和高注意力残留物对的生物相关性.

结论:

  • 通过整合空间和环境数据,TCRoss模型在预测TCR-结合方面取得了重大进展.
  • 这种方法提高了免疫反应预测的准确性,并为免疫学研究提供了更可靠的工具.
  • 这些发现强调了在生物系统的深度学习中考虑生化和空间因素的重要性.