A novel multi-stage feature selection method for microarray expression data analysis. (1st January 2013)

• Record Type: Journal Article
• Authors: Du, Wei; Sun, Ying; Wang, Yan; Cao, Zhongbo; Zhang, Chen; Liang, Yanchun
• Is Part Of: International journal of data mining and bioinformatics. Volume 7:Number 1(2013)
• Page Start: 58
• Page End: 77
• Publication Date: 2013-01-01
• Subjects: feature selection -- microarray expression data analysis -- cancer classification -- expression correlation analysis -- disease biomarker identification -- improved normalised signal to noise ratio -- support vector clustering; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

With the development of genome research, finding method to classify cancer and detect biomarkers efficiently has become a challenging problem. In this paper, a novel multi-stage method for feature selection is proposed which considers all kinds of genes in the original gene set. The method eliminates the irrelevant, noisy and redundant genes and selects a subset of relevant genes at different stages. The proposed method is examined on microarray datasets of Leukemia, Prostate, Colon, Breast, Nervous and DLBCL by different classifiers and the best accuracies of the method in these datasets are 100%, 98.04%, 100%, 89.74%, 100% and 98.28%, respectively.