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相关实验视频

Updated: Jun 9, 2025

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Published on: October 15, 2021

HLA-DR4Pred2:用于预测HLA-DRB1*04:01结合剂的改进方法

Sumeet Patiyal¹, Anjali Dhall¹, Nishant Kumar¹

¹Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India.

Methods (San Diego, Calif.)

|October 21, 2024

概括

使用大型数据集和机器学习,HLA-DR4Pred2可以准确预测HLA-DRB1*04:01结合剂. 这个工具有助于开发免疫疗法和疫苗,用于COVID-19等相关疾病.

关键词:

抗原结合剂抗原结合剂爆炸搜索搜索爆炸搜索在HLA-DRB1*04:01中.免疫治疗是一种免疫疗法.机器学习机器学习预测方法预测方法

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Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Published on: September 6, 2017

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

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

Published on: March 25, 2014

相关实验视频

Last Updated: Jun 9, 2025

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Published on: October 15, 2021

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Published on: September 6, 2017

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

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

Published on: March 25, 2014

科学领域:

免疫信息学是指免疫信息学.
计算生物学计算生物学
机器学习在免疫学中的应用

背景情况:

这种HLA-DRB1*04:01等位基因与各种疾病有关,包括自身免疫性疾病和COVID-19.
准确预测与HLA-DRB1*04:01的结合对于开发向免疫疗法和疫苗至关重要.
现有的预测方法通常受到小型训练数据集的限制,这影响了它们的预测能力.

研究的目的:

开发一个改进的计算工具,HLA-DR4Pred2,用于预测HLA-DRB1*04:01结合.
与先前的增强模型培训方法相比,利用更大的数据集.
为研究人员提供一个用户友好的工具来预测,设计和虚拟扫描HLA-DRB1*04:01结合.

主要方法:

开发HLA-DR4Pred2使用12676个结合剂和同等数量的非结合剂的大数据集.
在80%的数据上使用五倍交叉验证进行培训和优化,其余20%的数据进行评估.
应用各种机器学习技术,包括组合和二进制配置特征,并使用AUROC进行性能评估.

主要成果:

使用组合特征,HLA-DR4Pred2模型实现了0.90的最大AUROC,使用二进制配置特征达到0.87.
将基于组合的模型与BLAST搜索相结合,AUROC提高到0.93.
在现实数据集 (12,676个绑定器,86,300个非绑定器) 上训练的模型达到0.99.99的最大AUROC.
拟议的方法在独立数据集上的现有方法相比,显示出更高的性能.

结论:

HLA-DR4Pred2显著提升了HLA-DRB1*04:01结合的预测,超过了以前的方法.
开发的工具和网络服务器有助于设计和虚拟选用于免疫疗法和疫苗开发的.
一个公开可用的Python包提高了研究界的可访问性.