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Mutation-based Binary Aquila optimizer for gene selection in cancer classification.

Elham Pashaei1

  • 1Department of Computer Engineering, Istanbul Gelisim University, Istanbul, Turkey.

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Summary
This summary is machine-generated.

A novel gene selection method, mutated binary Aquila Optimizer (MBAO) with a time-varying mirrored S-shaped (TVMS) transfer function, enhances microarray data classification accuracy. This approach effectively identifies informative genes, improving diagnostic capabilities.

Keywords:
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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Microarray data classification is crucial for disease diagnosis.
  • High dimensionality of gene data leads to reduced classification accuracy due to redundant genes.
  • Efficient gene selection is needed to improve diagnostic performance.

Purpose of the Study:

  • To propose a new wrapper gene selection method using mutated binary Aquila Optimizer (MBAO) with a time-varying mirrored S-shaped (TVMS) transfer function.
  • To enhance the accuracy of microarray data classification by identifying the most informative genes.
  • To improve the efficiency and global search capabilities of gene selection algorithms.

Main Methods:

  • A hybrid approach combining Minimum Redundancy Maximum Relevance (mRMR) filtering with the MBAO-TVMS wrapper method.
  • Utilizing TVMS to convert the continuous Aquila Optimizer (AO) to a binary version.
  • Incorporating a mutation mechanism into the binary AO to prevent local optima and enhance global search.

Main Results:

  • The proposed mRMR-MBAO method demonstrated superior performance compared to mRMR-BAO and other gene selection (GS) approaches.
  • Significant improvements in classification accuracy were observed across eleven benchmark microarray datasets.
  • The method effectively reduced the number of selected genes while maintaining high accuracy.

Conclusions:

  • The mRMR-MBAO hybrid method is highly effective for gene selection in microarray data classification.
  • This approach offers a promising solution for improving diagnostic accuracy in bioinformatics.
  • The developed MBAO algorithm shows enhanced global search capabilities for feature selection.