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Published on: May 16, 2022
Graph coloring for extracting discriminative genes in cancer data
Mohamed A Mahfouz1, Juan A Nepomuceno2
1Department of Computer and Systems Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt.
This study introduces a graph coloring approach (GCA) for reducing dimensionality in gene expression data. GCA improves cancer classification accuracy by selecting relevant genes, aiding in automated cancer diagnosis.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Microarray analysis for cancer diagnosis faces challenges due to high dimensionality and noise in gene expression data.
- The limited number of samples compared to the vast number of genes presents a significant obstacle.
Purpose of the Study:
- To address the dimensionality reduction challenge in microarray-based cancer diagnosis.
- To develop an effective technique for selecting relevant genes from high-dimensional datasets.
Main Methods:
- Introduced a dimension-reduction technique called graph coloring approach (GCA).
- GCA analyzes gene-gene correlations and partitions genes into hubs using graph coloring.
- Employs a gene-selection step using biserial correlation and identifies redundant genes based on a correlation threshold.
Main Results:
- GCA demonstrated significant improvements in accuracy and reduced feature numbers compared to existing methods.
- The selected genes were validated as relevant using scientific repositories.
- The technique efficiently produces new feature subsets for analysis.
Conclusions:
- The proposed GCA technique aids biologists in accurately predicting cancer.
- This dimension-reduction method has potential applications in various areas of cancer research and diagnosis.
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