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A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm.

Saeid Azadifar¹, Ali Ahmadi²

¹Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran. saeid.azadifar@email.kntu.ac.ir.

BMC Medical Informatics and Decision Making

|November 28, 2021

Summary

This summary is machine-generated.

This study introduces a novel gene selection method combining graph theory and many-objective particle swarm optimization (PSO) to improve bioinformatics analysis. The hybrid approach effectively reduces gene dimensions, enhancing classification accuracy and feature selection efficiency.

Keywords:

Dimension reduction Gene clustering Gene selection High dimensional Many-objective PSO Repair operator

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Area of Science:

Bioinformatics
Computational Biology
Machine Learning

Background:

Gene expression data present high dimensionality with limited samples, challenging bioinformatics analyses.
Effective gene selection is crucial for reducing feature space and improving data mining algorithm performance.
Irrelevant and redundant genes negatively impact machine learning model accuracy.

Purpose of the Study:

To develop a hybrid gene selection method integrating graph theory and many-objective particle swarm optimization (PSO).
To enhance the efficiency and accuracy of gene selection in high-dimensional gene expression datasets.
To address the challenge of limited samples in gene expression data analysis.

Main Methods:

A filter method was applied for initial gene space reduction.
Graph theory and clustering grouped genes into relevant clusters.
A many-objective PSO algorithm optimized gene subsets based on classification error, centrality, specificity, and gene count.
A repair operator ensured gene selection diversity across all clusters.

Main Results:

The proposed method achieved higher classification accuracy compared to existing state-of-the-art techniques.
Fewer genes were selected by the hybrid method, indicating improved efficiency.
Experiments on seven datasets demonstrated the method's robust performance.
Comparisons included Decision Tree, Support Vector Machine, and K-Nearest Neighbors classifiers.

Conclusions:

The multi-objective PSO algorithm effectively removes irrelevant and redundant genes using multiple criteria.
The integration of clustering and a repair operator improved overall performance by ensuring comprehensive gene space coverage.
The proposed hybrid method offers a powerful tool for gene selection in bioinformatics.