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

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

Dysregulation of Immune Regulatory Mediators and Reduced Antitetanus Toxoid Antibodies Are Associated with Submicroscopic Plasmodium Infection in Pregnancy in Colombia.

The American journal of tropical medicine and hygiene·2026
Same author

Associations between precipitation, temperature, and malaria prevalence in children under 5 in Mali.

PloS one·2026
Same author

Adjuvants and MHCII modulate the immunogenicity of subdominant epitopes in <i>Plasmodium vivax</i> Duffy binding protein.

iScience·2025
Same author

Machine learning framework to extract physicochemical features of B-cell epitopes recognized by a cross-reactive antibody.

NPJ systems biology and applications·2025
Same author

A machine learning framework to identify complex physicochemical features of B cell epitopes.

Research square·2025
Same author

Preconception immunisation to prevent pregnancy-associated malaria.

The Lancet. Infectious diseases·2024

相关实验视频

Updated: Jun 10, 2025

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

整合机器学习来推进表位图绘制.

Simranjit Grewal1, Nidhi Hegde2, Stephanie K Yanow1,3

  • 1Department of Medical Microbiology and Immunology, University of Alberta, Edmonton, AB, Canada.

Frontiers in immunology
|October 15, 2024
PubMed
概括

机器学习增强了用于免疫疗法和诊断的表皮图映射. 这种方法提高了预测的准确性和可行性,解决了疫苗设计和疾病生物标志物识别方面的挑战.

关键词:
在B细胞中,B细胞是算法算法是一种算法.数据库就是数据库.标志性 标志性 标志性 标志性功能 功能 功能 功能 功能 功能 功能机器学习是机器学习.工具箱 工具箱 工具箱疫苗 疫苗 疫苗 疫苗

更多相关视频

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
08:09

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope

Published on: March 24, 2017

9.4K
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

相关实验视频

Last Updated: Jun 10, 2025

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
Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
08:09

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope

Published on: March 24, 2017

9.4K
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

科学领域:

  • 免疫学和生物信息学
  • 蛋白质结构和抗体相互作用

背景情况:

  • 在开发免疫疗法和诊断方面,皮质的识别至关重要.
  • 目前用于表位图的实验方法在准确性,吞吐量和成本方面存在局限性.

研究的目的:

  • 为了比较用于表位映射的机器学习 (ML) 工具.
  • 讨论数据选择,特征设计和算法对ML预测准确性的影响.
  • 探索ML在完善表位预测和解决诸如多活性抗体和构造表位等挑战方面的潜力.

主要方法:

  • 对现有的机器学习表征映射工具进行比较分析.
  • 讨论影响ML模型性能的关键因素:数据选择,特征工程和算法选择.
  • 关于实验和计算表位图绘制技术的当前文献的综述.

主要成果:

  • 与一些实验方法相比,机器学习提供了更好的特异性和预测准确性.
  • ML可以提高表位图绘制的解释和可行性.
  • ML方法可以帮助精细预测复杂的表位类型,如形态表位.

结论:

  • 机器学习是推动表位预测和映射的强大工具.
  • 优化ML模型需要仔细考虑数据和算法参数.
  • 在表位标识中应用ML有望指导开发更有效和可预测的治疗干预措施.