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集群和注释的T细胞受体表单

Sebastiaan Valkiers^1,2, Sofie Gielis^1,2, Vincent M L Van Deuren^1,2

¹Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium.

Methods in molecular biology (Clifton, N.J.)

|May 31, 2023

概括

使用机器学习分析T细胞受体 (TCR) 库,可以揭示有关感染和疫苗反应的见解. 这项研究开发了一种计算方法来识别病原体特定的TCR,使用黄热病病毒疫苗接种数据证明了这一点.

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) 谱系对适应性免疫至关重要,它介绍了对病原体的保护.
对TCR谱系的分析可以提供对感染和疫苗的免疫反应的见解.
了解TCR动态对于开发和监测有效疫苗至关重要.

研究的目的:

开发和呈现一个计算工作流程,用于分析和注释完整的TCR目录数据.
使用机器学习技术识别病原体特异性T细胞受体.
使用黄热病病毒 (YFV) 疫苗接种研究数据来证明工作流的实用性.

主要方法:

采用了监督和无监督机器学习算法的组合.
利用一个高效的集群方法来分析TCR的曲目.
将工作流应用于一个人YFV疫苗接种前和后的TCR目录数据.

主要成果:

成功分析并注释了TCR目录数据.
确定了YFV特异性的T细胞受体.
在真实世界的疫苗接种研究中证明了计算工作流程的有效性.

结论:

开发的计算工作流允许有效分析和注释TCR目录数据.
机器学习方法可以识别病原体特定的TCR,有助于理解免疫反应.
这种方法具有疫苗开发和监测的潜力,通过揭示关键的免疫学信息.

关键词:

皮层特异性的T细胞.免疫信息学是指免疫信息学.机器学习机器学习 T细胞受体受体的 T 细胞受体.在TCR集群化中,TCR集群化.分析TCR曲目库的分析.在TCR的曲目排序中,排序是TCR的曲目排序.

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