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Gene selection for microarray data classification via multi-objective graph theoretic-based method.

Mehrdad Rostami1, Saman Forouzandeh2, Kamal Berahmand3

  • 1Centre of Machine Vision and Signal Processing, Faculty of Information Technology, University of Oulu, Oulu, Finland.

Artificial Intelligence in Medicine
|January 9, 2022
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Summary
This summary is machine-generated.

This study introduces a new gene selection method using social network analysis for DNA microarray data. The approach enhances classification accuracy and reduces computational time for high-dimensional datasets.

Keywords:
Community detectionFeature selectionGene selectionMicroarray data classificationMulti-objectiveNode centrality

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-dimensional DNA microarray data presents challenges for classification due to the large number of genes.
  • Gene selection is a crucial strategy to improve classification accuracy and reduce computational complexity in microarray data analysis.
  • Existing methods may not optimally balance gene relevance and redundancy.

Purpose of the Study:

  • To propose a novel gene selection approach for DNA microarray data classification.
  • To maximize the relevance of selected genes while minimizing redundancy.
  • To enhance the efficiency and accuracy of microarray data analysis.

Main Methods:

  • A social network analysis-based gene selection method is developed.
  • The method iteratively selects maximum communities.
  • Node centrality criteria are used to select appropriate genes within selected communities.

Main Results:

  • The proposed gene selection algorithm improves the classification accuracy of DNA microarray data.
  • The method effectively decreases the time complexity associated with data analysis.
  • Selected genes demonstrate both high relevance and low redundancy.

Conclusions:

  • The social network analysis-based gene selection approach is effective for high-dimensional microarray data.
  • This method offers a significant improvement over existing techniques for gene selection.
  • The developed algorithm contributes to more efficient and accurate genomic data analysis.