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Related Experiment Video

Updated: Feb 19, 2026

Analysis of HBV-Specific CD4 T-cell Responses and Identification of HLA-DR-Restricted CD4 T-Cell Epitopes Based on a Peptide Matrix

Analysis of HBV-Specific CD4 T-cell Responses and Identification of HLA-DR-Restricted CD4 T-Cell Epitopes Based on a Peptide Matrix

Published on: October 20, 2021

[Improved weighted cross-correlation coefficient with a new kernel and its application in predicting T cell

Jing Huang¹, Jian-hua Ma, Nan Liu

¹School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China. hjing@fimmu.com

Nan Fang Yi Ke Da Xue Xue Bao = Journal of Southern Medical University

|October 23, 2010

Summary

This study introduces a novel weighted cross-correlation coefficient for T cell epitope analysis, optimizing peptide similarity using an evolutionary algorithm. The method showed strong performance in T-cell data set evaluations.

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Last Updated: Feb 19, 2026

Analysis of HBV-Specific CD4 T-cell Responses and Identification of HLA-DR-Restricted CD4 T-Cell Epitopes Based on a Peptide Matrix

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Published on: October 20, 2021

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Published on: March 25, 2014

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

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

Published on: October 22, 2012

Area of Science:

Immunology
Bioinformatics
Computational Biology

Context:

T cell epitopes are crucial for adaptive immunity.
Accurate prediction of T cell epitope interactions is vital for vaccine and immunotherapy development.
Existing methods for peptide similarity may not fully capture critical epitope features.

Purpose:

To develop an improved peptide similarity metric for T cell epitope analysis.
To incorporate an "anchor" concept into a weighted cross-correlation coefficient.
To optimize this metric using an evolutionary algorithm and evaluate its performance in a Support Vector Machine (SVM) model.

Summary:

A novel weighted cross-correlation coefficient was designed, emphasizing the "anchor" of T cell epitopes.
An evolutionary algorithm was employed to determine the optimal weight vector for this coefficient.
A Support Vector Machine (SVM) model incorporating this new peptide similarity kernel was tested on a T-cell data set.

Impact:

The proposed method demonstrates good performance in classifying T-cell interactions.
This approach offers a potentially more accurate tool for T cell epitope analysis.
Findings could advance the design of more effective immunotherapies and vaccines.