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相关概念视频

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

464
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
464
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

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The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
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相关实验视频

Updated: Jan 10, 2026

Measuring TCR-pMHC Binding In Situ using a FRET-based Microscopy Assay
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基准比较无监督的方法来推断TCR的特异性.

Charline Jouannet1,2, Hélène Vantomme1,2, Kenz Le Gouge1

  • 1Sorbonne Université, INSERM, Immunoregulation-Immunopathology-Immunotherapy (i3), 75005, Paris, France.

NAR genomics and bioinformatics
|November 21, 2025
PubMed
概括

通过比较T细胞受体 (TCR) 聚类方法,可以发现性能差异. 深TCR在抗原特异性TCR识别方面表现出色,而其他提供不同的集群纯度和大小,有助于选择适应性免疫研究的工具.

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Simultaneous Quantification of T-Cell Receptor Excision Circles TRECs and K-Deleting Recombination Excision Circles KRECs by Real-time PCR
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Determining Optimal Cytotoxic Activity of Human Her2neu Specific CD8 T cells by Comparing the Cr51 Release Assay to the xCELLigence System
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Determining Optimal Cytotoxic Activity of Human Her2neu Specific CD8 T cells by Comparing the Cr51 Release Assay to the xCELLigence System

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相关实验视频

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Simultaneous Quantification of T-Cell Receptor Excision Circles TRECs and K-Deleting Recombination Excision Circles KRECs by Real-time PCR
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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:

  • 了解T细胞受体 (TCR) 的特异性对于适应性免疫研究至关重要.
  • 推断TCR特异性的现有计算方法缺乏全面的比较分析.
  • 聚类算法对于将具有相似特异性的TCR分组至关重要.

研究的目的:

  • 为了对九种不同的TCR集群方法的性能进行比较和比较.
  • 评估这些方法如何有效地识别抗原特异性T细胞受体.
  • 为TCR特异性推断工具提供统一的数据库和性能基准.

主要方法:

  • 从IEDB,McPAS-TCR和VDJdb获得的已知表位特异性的190,670个人类TCR统一数据库.
  • 在这个数据集上使用了9个TCR聚类算法 (DeepTCR,ClusTCR,TCRMatch,GLIPH2,Levenstein距离,Hamming距离,GIANA,iSMART) 的基准.
  • 使用大型10X基因组学数据集与抗原特异标记TCR验证的发现.

主要成果:

  • 深度TCR显示了抗原特异性TCR的最高保留率.
  • 集群TCR,TCRMatch和GLIPH2提供了高集群纯度,但保留率较低.
  • 像GLIPH2和集群TCR这样的方法产生了更大的集群,而GIANA和iSMART则产生了更小的,抗原特定的集群.
  • 深度TCR在捕获抗原特异性TCR方面表现出卓越的灵敏度.

结论:

  • TCR集群方法在集群纯度,大小和抗原特异性TCR的保留方面具有独特的性能特征.
  • 深度TCR是识别抗原特异性TCR的最敏感的方法.
  • 这项研究提供了一项有价值的基准,以指导研究人员选择适当的TCR聚类工具,以满足他们在适应性免疫研究中的特定需求.