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Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence

M Dashtban1, Mohammadali Balafar1

  • 1Department of Computer Engineering, Faculty of Electrical & Computer Engineering, University of Tabriz, Iran.

Genomics
|February 5, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel evolutionary algorithm for gene selection in cancer classification from microarray data. The proposed method demonstrates superior performance, particularly for the DLBCL dataset, advancing predictive gene identification.

Keywords:
Cancer classificationCut and splice crossoverFeature selectionGene selectionIntelligent Dynamic AlgorithmMicroarray data analysisPenalizing strategyRandom-restart hill climbingReinforcement learningSelf-refinement strategy

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene selection is crucial for analyzing complex cancer microarray data.
  • Identifying predictive genes remains a significant challenge in cancer classification.

Purpose of the Study:

  • To propose a novel evolutionary method for identifying predictive genes for cancer classification.
  • To enhance gene selection accuracy using genetic algorithms and artificial intelligence.

Main Methods:

  • Applied a filter method for initial dimensionality reduction.
  • Employed an integer-coded genetic algorithm with dynamic-length genotype and intelligent parameter settings.
  • Investigated algorithmic behaviors, including convergence and parameter dynamics, and compared filter methods (Laplacian, Fisher score).

Main Results:

  • The proposed evolutionary method was benchmarked on five high-dimensional cancer datasets.
  • Statistical tests revealed significant differences in classifier and filter method performance across datasets.
  • The method demonstrated superior performance compared to state-of-the-art approaches on the DLBCL dataset.

Conclusions:

  • The novel evolutionary approach effectively identifies predictive genes for cancer classification.
  • The method offers an advancement in handling complex, high-dimensional cancer microarray data.
  • Further research can explore variations in filter methods and classifier choices for improved outcomes.