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

ABSTRACT:

Background

Microarray 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.

Results

The 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.

Conclusions

We provide an efficient gene selection algorithm can select

SUBMITTER: Alanni R 

PROVIDER: S-EPMC6880643 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature