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Related Experiment Video

Updated: May 4, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Gene features selection for three-class disease classification via multiple orthogonal partial least square

Mingxing Yang¹, Xiumin Li², Zhibin Li²

¹Xiamen Diabetes Institute, the First Affiliated Hospital of Xiamen University, Xiamen, China ; Department of Electronic Science, School of Physics and Mechanical & Electrical Engineering, Xiamen University, Xiamen, China.

|January 4, 2014

Summary

This summary is machine-generated.

This study introduces a novel gene selection method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) and S-plots for accurate disease classification. The approach effectively identifies relevant genes, improving prediction accuracy in microarray data analysis.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

DNA microarray analysis generates numerous gene variables from limited observations.
Feature selection is crucial for effective cluster analysis of high-throughput data.
Identifying relevant genes improves disease classification and diagnosis accuracy.

Purpose of the Study:

To propose a new method for selecting relevant genes from DNA microarray data.
To enhance disease classification and prediction accuracy using feature selection.
To evaluate the proposed method against existing techniques.

Main Methods:

Utilized multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots for feature selection.
Applied hierarchical OPLS-DA for subtype classification and parallel OPLS-DA for class-specific marker gene selection.
Evaluated feature selection performance using cluster analysis and tested on four public datasets.

Main Results:

The mOPLS-DA method effectively selected the most relevant genes for three-class disease classification and prediction.
The proposed method demonstrated superior performance compared to four other feature selection techniques.
Accurate classification and prediction of diseases were achieved using the selected marker genes.

Conclusions:

The developed mOPLS-DA approach is a powerful tool for feature selection in DNA microarray analysis.
This method significantly improves the accuracy of disease classification and prediction.
The findings offer a robust strategy for identifying disease-associated genes from complex genomic data.