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

2.0K
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...
2.0K
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

2.1K
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
2.1K

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

Exploring homology detection via k-means clustering of proteins embedded with a large language model.

Bioinformatics (Oxford, England)·2025
Same author

Path sampling challenges in large biomolecular systems: RETIS and REPPTIS for ABL-imatinib kinetics.

Biophysical journal·2025
Same author

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning.

Nucleic acids research·2025
Same author

Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning.

Cell systems·2024
Same author

Predictability of antigen binding based on short motifs in the antibody CDRH3.

Briefings in bioinformatics·2024
Same author

Identification of Transcripts with Shared Roles in the Pathogenesis of Postmenopausal Osteoporosis and Cardiovascular Disease.

International journal of molecular sciences·2024
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
查看所有相关文章

相关实验视频

Updated: Mar 18, 2026

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

9.3K

检测免疫受体数据中的统计相互作用:一项比较研究

Thomas Minotto1, Ingrid Hobæk Haff1, Enrico Riccardi2

  • 1Department of Mathematics, University of Oslo, Oslo, Norway.

Journal of applied statistics
|March 16, 2026
PubMed
概括
此摘要是机器生成的。

通过机器学习,可以检测免疫受体结合中的统计相互作用. 在1000个序列中确定了对交互,而逻辑回归和随机森林在更高阶交互中表现出色.

关键词:
62J07 这是一个很好的例子.抗体与抗原的结合.互动检测 相互作用检测逻辑回归的逻辑回归逻辑回归的逻辑回归方法机器学习是机器学习.随机的森林随机的森林

更多相关视频

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing
08:59

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

Published on: January 12, 2021

9.0K
Avidity-based Extracellular Interaction Screening AVEXIS for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions
12:30

Avidity-based Extracellular Interaction Screening AVEXIS for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions

Published on: March 5, 2012

22.3K

相关实验视频

Last Updated: Mar 18, 2026

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

9.3K
T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing
08:59

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

Published on: January 12, 2021

9.0K
Avidity-based Extracellular Interaction Screening AVEXIS for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions
12:30

Avidity-based Extracellular Interaction Screening AVEXIS for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions

Published on: March 5, 2012

22.3K

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:

  • 统计相互作用在生物过程中至关重要,包括免疫受体与抗原结合.
  • 先进的机器学习方法通过识别氨基酸内链相互作用来预测结合亲和力.

研究的目的:

  • 审查和比较用于免疫受体结合预测的统计相互作用检测方法.
  • 评估物流拉索,逻辑回归,随机森林和神经网络在识别模拟免疫数据交互方面的性能.

主要方法:

  • 模拟免疫数据与植入的氨基酸基因通过后勤回归来确定结合状态.
  • 基于相互作用顺序,强度,频率和数据集大小的检测性能比较.
  • 对不同机器学习方法的计算运行时间的评估.

主要成果:

  • 只用1000个序列就能检测到对互动,最佳检测率约为20%的植入率.
  • 逻辑回归和随机森林方法在高阶相互作用方面表现优越.
  • 神经网络方法表现出最快的运行时间,其次是基于激光的方法.

结论:

  • 机器学习方法有效地检测免疫受体结合数据中的统计相互作用.
  • 方法的选择影响基于交互顺序和计算资源的性能.
  • 对实验数据的应用确定了显著的双向和三向相互作用,提高了预测准确性.