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Updated: Sep 3, 2025

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

A novel biomarker selection method combining graph neural network and gene relationships applied to microarray data.

Weidong Xie¹, Wei Li², Shoujia Zhang¹

¹School of Computer Science and Engineering, Northeastern University, Shenyang, China.

BMC Bioinformatics

|July 26, 2022

Summary

This study introduces a novel graph neural network approach for effective feature selection in biomarker discovery. The method enhances accuracy and stability by removing redundant features, identifying significant biomarkers for clinical applications.

Keywords:

Biomarker Feature selection Graph neural networ Spectral clustering

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Area of Science:

Bioinformatics
Machine Learning
Genomics

Background:

Biomarker discovery is crucial for clinical diagnosis and drug development.
High-dimensional microarray data presents challenges due to the curse of dimensionality.
Existing feature selection methods often neglect complex feature dependencies and gene pathway relationships.

Purpose of the Study:

To develop an advanced feature selection method for identifying critical biomarkers.
To address limitations in current methods by incorporating feature dependencies and pathway information.
To improve the efficiency and accuracy of biomarker discovery from high-dimensional data.

Main Methods:

A graph neural network (GNN) based feature selection method was proposed.
Graph-structured data was constructed using feature dependencies and Pearson correlation coefficients.
GNNs were employed for information fusion, followed by spectral clustering to group redundant features.
A feature ranking aggregation model with eight evaluation methods was applied to sub-clusters for selection.

Main Results:

The proposed method demonstrated superior performance compared to classical and advanced algorithms.
It achieved a reduced number of features while improving classification accuracy.
Selected features exhibited higher biological and statistical significance.

Conclusions:

The GNN-based method effectively eliminates redundant features.
The algorithm yields stable outputs with high classification accuracy.
This approach holds potential for identifying novel and significant biomarkers.