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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
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Updated: May 17, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data.

Yongjun Piao1, Minghao Piao, Kiejung Park

  • 1Department of Electrical and Computer Engineering, Chungbuk National University, Chungbuk, Korea.

Bioinformatics (Oxford, England)
|October 13, 2012
PubMed
Summary

This study introduces an Ensemble Correlation-Based Gene Selection algorithm for cancer classification using microarray data. The novel method effectively identifies key genes, outperforming existing feature selection techniques for improved learning performance.

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Last Updated: May 17, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Gene selection is crucial for cancer classification, but microarray data present computational challenges.
  • Effective dimension reduction techniques are needed to identify informative gene subsets for enhanced learning.
  • Gene selection is framed as a feature selection problem in machine learning to find discriminative features.

Purpose of the Study:

  • To propose an Ensemble Correlation-Based Gene Selection algorithm for improved cancer classification.
  • To address the challenges of high-dimensional microarray data in gene selection.
  • To develop a method that identifies a minimal set of genes with high discriminative power.

Main Methods:

  • Developed an Ensemble Correlation-Based Gene Selection algorithm.
  • Utilized symmetrical uncertainty to assess gene relevance.
  • Employed Support Vector Machine as a wrapper-based evaluation criterion for gene subsets.

Main Results:

  • The proposed algorithm demonstrated superior efficiency and effectiveness compared to other feature selection techniques.
  • The method successfully identified relevant gene subsets for cancer classification.
  • Experimental results confirmed the outperformance of the proposed method over existing literature.

Conclusions:

  • The Ensemble Correlation-Based Gene Selection algorithm offers a robust approach for cancer classification.
  • The method effectively handles high-dimensional gene expression data.
  • This approach enhances machine learning performance by identifying critical genes.