scholarly journals A Gene Selection Method for Cancer Classification

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Xiaodong Wang ◽  
Jun Tian
Keyword(s):  
Correlation Analysis ◽  
Gene Selection ◽  
Reference Range ◽  
Selection Method ◽  
Cancer Classification ◽  
Gene Selection Method ◽  
Number Of Genes ◽  
Good Classification

This paper proposes a method to select a set of genes from a large number of genes with the ability of classifying types of diseases. The proposed gene selection method is designed according to correlation analysis and the concept of 95% reference range. The method is very simple and uses the information of all genes. We have used the method in leukemia patients and achieved good classification results.

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

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Russul Alanni ◽  
Jingyu Hou ◽  
Hasseeb Azzawi ◽  
Yong Xiang
Keyword(s):  
Gene Selection ◽  
Computational Cost ◽  
Selection Method ◽  
Cancer Classification ◽  
Gene Selection Method ◽  
Microarray Expression Data ◽  
Efficient Gene ◽  
Number Of Genes ◽  
Efficiency And Effectiveness ◽  
Microarray Datasets

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 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.

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