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A novel gene selection algorithm for cancer classification using microarray datasets.

Russul Alanni1, Jingyu Hou2, Hasseeb Azzawi2

  • 1School of Information Technology, Deakin University, Burwood, 3125, VIC, Australia. ralanni@deakin.edu.au.

BMC Medical Genomics
|January 17, 2019
PubMed
Summary
This summary is machine-generated.

A new Gene Selection Programming (GSP) method effectively identifies key genes for cancer classification from microarray data. This approach improves accuracy and reduces computational cost, outperforming existing gene selection techniques.

Keywords:
Gene expression programmingGene selectionMicroarray cancer datasetSupport vector machine

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray datasets are crucial for cancer classification but suffer from the curse of dimensionality due to small sample sizes and numerous genes.
  • Gene selection is vital for improving cancer classification by reducing irrelevant genes and focusing on informative ones.

Purpose of the Study:

  • To propose an innovative Gene Selection Programming (GSP) method for effective and efficient cancer classification using microarray data.
  • To address the challenges of high dimensionality and improve the performance of cancer classification algorithms.

Main Methods:

  • Developed Gene Selection Programming (GSP), an enhancement of Gene Expression Programming (GEP).
  • Incorporated a novel population initialization, fitness function, and improved genetic operators.
  • Utilized Support Vector Machine (SVM) with a linear kernel as the classifier within the GSP framework.

Main Results:

  • GSP effectively eliminated irrelevant and redundant genes from ten microarray cancer datasets.
  • Demonstrated superior performance in classification accuracy, reduced gene count, and computational efficiency compared to other methods.
  • The gene subsets selected by GSP showed enhanced cancer classification capabilities.

Conclusions:

  • The proposed GSP method achieves higher classification accuracy with reduced processing time.
  • GSP offers a superior approach to gene selection for cancer classification from microarray data.