Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A hybrid machine learning framework with two-step feature selection for identifying key biomarkers and drug targets in monkeypox.

Biochemistry and biophysics reports·2026

Same author

MIF-MAPMS: Enhancing identification of myelin autoantigenic peptides in multiple sclerosis through multimodal information fusion.

Protein science : a publication of the Protein Society·2026

Same author

NToxSEM: Enhancing prediction of neurotoxic peptides and neurotoxins using a stacked ensemble-based multimodal framework.

Protein science : a publication of the Protein Society·2026

Same author

Enhancing the efficacy and selectivity of novel antimicrobial peptides against methicillin-resistant <i>Staphylococcus aureus</i> through computational and experimental approaches.

Biofouling·2026

Same author

Leveraging a Meta-Learning Strategy to Advance the Accuracy of Neutralizing Antibodies against Dengue Virus Serotype Prediction.

ACS omega·2026

Same author

Accurate identification of broadly neutralizing antibodies against dengue virus based on deep stacking strategy with multi-perspective features.

Scientific reports·2025

Search research articles

Related Experiment Video

Updated: Mar 30, 2026

Detection of Human Leukocyte Antigen Biomarkers in Breast Cancer Utilizing Label-free Biosensor Technology

Detection of Human Leukocyte Antigen Biomarkers in Breast Cancer Utilizing Label-free Biosensor Technology

Published on: March 24, 2015

Sequence based human leukocyte antigen gene prediction using informative physicochemical properties.

Watshara Shoombuatong, Panuwat Mekha, Jeerayut Chaijaruwanich

International Journal of Data Mining and Bioinformatics

|November 10, 2015

Summary

This study introduces HLAPred, a novel method for predicting human leukocyte antigen (HLA) gene classes. HLAPred utilizes physicochemical properties and Support Vector Machines for accurate and interpretable immune system insights.

More Related Videos

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

Related Experiment Videos

Last Updated: Mar 30, 2026

Detection of Human Leukocyte Antigen Biomarkers in Breast Cancer Utilizing Label-free Biosensor Technology

Detection of Human Leukocyte Antigen Biomarkers in Breast Cancer Utilizing Label-free Biosensor Technology

Published on: March 24, 2015

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

Area of Science:

Immunogenetics
Bioinformatics
Computational Biology

Background:

Human leukocyte antigen (HLA) gene prediction is crucial for understanding immune responses to pathogens.
Existing methods for HLA gene class prediction lack efficiency and interpretability.

Purpose of the Study:

To develop an efficient and interpretable method for predicting human leukocyte antigen (HLA) gene classes.
To improve upon existing HLA gene prediction techniques.

Main Methods:

Established a reduced sequence identity dataset (HLA262) with 30% identity.
Proposed a feature set of physicochemical properties integrated with Support Vector Machines (SVM).
Developed a novel prediction tool named HLAPred.

Main Results:

Achieved high accuracy (90.04%) and sensitivity (82.99%) in HLA gene class prediction.
Demonstrated superior performance compared to existing methods.
Identified informative physicochemical properties contributing to prediction.

Conclusions:

HLAPred offers an accurate and interpretable approach for HLA gene class prediction.
The identified physicochemical properties provide insights into HLA gene family mechanisms.
This method enhances understanding of the human immune system's response to viral pathogens.