A novel multi-stage feature selection method for microarray expression data analysis

被引：19
|
作者：
Du, Wei [1 ,2 ]
Sun, Ying [1 ]
Wang, Yan [1 ,3 ]
Cao, Zhongbo [1 ]
Zhang, Chen [1 ]
Liang, Yanchun [1 ]
机构：
[1] Jilin Univ, Minist Educ, Coll Comp Sci & Technol, Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Peoples R China
[2] Jilin Univ, Coll Chem, Changchun 130012, Peoples R China
[3] Jilin Univ, Coll Math, Changchun 130012, Peoples R China
来源：
INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS | 2013年 / 7卷 / 01期
关键词：
feature selection; microarray expression data analysis; cancer classification; expression correlation analysis; disease biomarker identification; improved normalised signal to noise ratio; support vector clustering; GENE SELECTION; CANCER; CLASSIFICATION; PREDICTION; DISCOVERY;
D O I：
10.1504/IJDMB.2013.050977
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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.
引用
收藏
页码：58 / 77
页数：20
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