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Deep gene selection method to select genes from microarray datasets for cancer classification

Al-Anni, Russul, Hou, Jingyu, Azzawi, Hasseeb and Xiang, Yong 2019, Deep gene selection method to select genes from microarray datasets for cancer classification, BMC bioinformatics, vol. 20, pp. 1-15, doi: 10.1186/s12859-019-3161-2.

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Title Deep gene selection method to select genes from microarray datasets for cancer classification
Author(s) Al-Anni, RussulORCID iD for Al-Anni, Russul orcid.org/0000-0002-6445-0137
Hou, JingyuORCID iD for Hou, Jingyu orcid.org/0000-0002-6403-9786
Azzawi, HasseebORCID iD for Azzawi, Hasseeb orcid.org/0000-0002-9849-3565
Xiang, YongORCID iD for Xiang, Yong orcid.org/0000-0003-3545-7863
Journal name BMC bioinformatics
Volume number 20
Article ID 608
Start page 1
End page 15
Total pages 15
Publisher BMC
Place of publication London, Eng.
Publication date 2019
ISSN 1471-2105
1471-2105
Keyword(s) Evolutionary algorithms
Gene expression programming
Gene selection
Microarray
Summary BackgroundMicroarray datasets consist of complex and high-dimensional samples and genes, and generally the number of samples is much smaller than the number of genes. Due to this data imbalance, gene selection is a demanding task for microarray expression data analysis.ResultsThe gene set selected by DGS has shown its superior performances in cancer classification. DGS has a high capability of reducing the number of genes in the original microarray datasets. The experimental comparisons with other representative and state-of-the-art gene selection methods also showed that DGS achieved the best performance in terms of the number of selected genes, classification accuracy, and computational cost.ConclusionsWe provide an efficient gene selection algorithm can select relevant genes which are significantly sensitive to the samples’ classes. With the few discriminative genes and less cost time by the proposed algorithm achieved much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.
Language eng
DOI 10.1186/s12859-019-3161-2
Indigenous content off
Field of Research 06 Biological Sciences
08 Information and Computing Sciences
01 Mathematical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2019, The Author(s)
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30132762

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.