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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Graph-based relevancy-redundancy gene selection method for cancer diagnosis.

Saeid Azadifar¹, Mehrdad Rostami², Kamal Berahmand³

¹Department of Computer Engineering, University of Khajeh Nasir Toosi, Tehran, Iran.

Computers in Biology and Medicine

|July 2, 2022

Summary

This study introduces a novel graph-based gene selection method for cancer diagnosis using social network analysis. The approach effectively identifies relevant genes while minimizing redundancy, outperforming existing methods.

Keywords:

Cancer diagnosis Edge centrality Gene selection Maximum clique Social network analysis

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Area of Science:

Molecular Biology
Bioinformatics
Computational Biology

Background:

Microarray data analysis is crucial for cancer diagnosis.
Gene selection is a key step to reduce dimensionality and improve accuracy.
Existing methods often struggle with redundancy and relevance.

Purpose of the Study:

To develop a graph theoretic-based gene selection method for enhanced cancer diagnosis.
To maximize gene relevancy to the target class while minimizing inner redundancy.
To leverage social network analysis techniques for gene ranking.

Main Methods:

Utilized graph theory and social network approaches (maximum weighted clique, edge centrality).
Implemented both unsupervised and supervised modes for gene selection.
Iteratively selected maximum weighted cliques to identify relevant gene subsets.

Main Results:

The developed model demonstrated superior performance in gene selection for cancer diagnosis.
Effectiveness shown across diverse datasets including Colon, Leukemia, SRBCT, Prostate Tumor, and Lung Cancer.
Outperformed established filter-based gene selection approaches.

Conclusions:

The proposed graph-based method offers a robust and effective solution for gene selection in cancer diagnosis.
This approach enhances accuracy by focusing on gene relevance and reducing redundancy.
The findings suggest a promising direction for molecular biology applications in oncology.