Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Diversity of Antigen Receptors01:28

Diversity of Antigen Receptors

489
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...
489
T Cell Activation and Clonal Selection01:22

T Cell Activation and Clonal Selection

627
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...
627
Epistasis01:39

Epistasis

45.5K
In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
45.5K
B Cell Activation and Differentiation01:24

B Cell Activation and Differentiation

1.4K
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.
When naive B cells encounter a specific antigen that can bind to the B cell receptor (BCR) on their surface, they undergo sensitization to respond to the antigen's presence. Sensitization begins with...
1.4K
Epistasis Analysis01:09

Epistasis Analysis

4.9K
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
4.9K
Special Features of Adaptive Immunity01:20

Special Features of Adaptive Immunity

721
The adaptive immune system, a crucial component of the overall immune response, offers a highly specialized defense against pathogens. It involves specific cell types and features, enabling it to combat infections effectively and efficiently.
The primary cell types involved in adaptive immunity are T cells and B cells. Each type has a unique role in defending the body against pathogens. T cells are responsible for cell-mediated immunity. They identify and eliminate infected cells directly,...
721

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Development of an Arterial Carbon Dioxide Estimation Model Using End-Tidal Carbon Dioxide Levels during Surgery in the Pediatric Population.

Anesthesiology·2026
Same author

Deep Learning outperforms physicians in myopathy and neuropathy classification based on Needle Electromyography Signal.

PloS one·2026
Same author

NeuroStream: an interactive platform for exploratory visualization and harmonization of multicohort brain MRI data.

Bioinformatics advances·2026
Same author

Dynamics of Radiation Damage Buildup in Ultrathin Hexagonal Boron Nitride Films under Ion Bombardment.

ACS applied materials & interfaces·2026
Same author

Response to a letter to the editor on fracture risks in patients with arginine vasopressin deficiency: a nationwide matched cohort study (OSIN-D-26-00525).

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA·2026
Same author

Short-term temperature and precipitation patterns associated with firearm discharge incidents in Detroit, MI, USA 2021-2025: A time-stratified case-crossover study.

Environmental research·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
查看所有相关文章

相关实验视频

Updated: May 26, 2025

Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells
13:58

Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells

Published on: October 22, 2012

17.9K

EpicPred:使用基于注意力的多实例学习预测由表位结合TCR驱动的表型.

Jaemin Jeon1, Suwan Yu1, Sangam Lee2

  • 1Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Republic of Korea.

Bioinformatics (Oxford, England)
|February 21, 2025
PubMed
概括
此摘要是机器生成的。

EpicPred识别了T细胞受体 (TCR) 和特定于疾病的表皮质相互作用. 这种方法通过过不太可能的TCR-表皮质对来改善癌症和COVID-19中的表型预测.

更多相关视频

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

14.9K
Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens
09:53

Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens

Published on: February 6, 2017

11.4K

相关实验视频

Last Updated: May 26, 2025

Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells
13:58

Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells

Published on: October 22, 2012

17.9K
A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

14.9K
Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens
09:53

Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens

Published on: February 6, 2017

11.4K

科学领域:

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

背景情况:

  • 识别T细胞受体 (TCR) -表位相互作用对于理解疾病机制和开发免疫疗法至关重要.
  • TCRs的CDR3区域是表皮图识别的关键,但在疾病背景下对这些相互作用进行分析尚未得到充分研究.

研究的目的:

  • 开发EpicPred,一种用于识别表型特异性TCR-表位相互作用的计算工具.
  • 通过使用TCR-表位组结合数据来提高疾病表型的预测.

主要方法:

  • EpicPred使用开放式识别 (OSR) 来过不太可能的TCR-表位相互作用,减少错误阳性.
  • 多重实例学习被用来确定与特定癌症类型或COVID-19严重程度相关的TCR-表位相互作用.

主要成果:

  • 这项研究分析了来自公共数据库的244,552个TCR序列和105个表位.
  • 在癌症和COVID-19数据集中,EpicPred在预测表型方面表现出卓越的表现,平均AUROC为0.80±0.07.
  • 该方法在批量和单细胞分辨率TCR测序数据上得到了验证.

结论:

  • EpicPred有效地识别了表型特异的TCR-表位相互作用.
  • 该工具在推进基于T细胞的诊断和治疗方面表现有前途.
  • 作为开源软件,EpicPred可用于进一步研究.