Gene Expression Data Classification Using Independent Variable Group Analysis

被引：0
|
作者：
Zheng, Chunhou [2 ,3 ]
Zhang, Lei [1 ]
Li, Bo [3 ]
Xu, Min [2 ]
机构：
[1] Hong Kong Polytech Univ, Dept Comp, Biometr Res Ctr, Hong Kong, Hong Kong, Peoples R China
[2] Qufu Normal Univ, Coll Informat & Commun Technol, Shandong Sheng 276826, Peoples R China
[3] Chinese Acad Sci, Inst Machine Intelligence, Intelligent Comp Lab, Hefei 230031, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2008, PT 2, PROCEEDINGS | 2008年 / 5264卷
基金：
美国国家科学基金会; 中国博士后科学基金;
关键词：
Gene expression data; Independent variable group analysis; Gene selection; Classification;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Microarrays are capable of detecting the expression levels of thousands of genes simultaneously. In this paper, a new method for gene selection based on independent variable group analysis is proposed. In this method. we first used t-statistics method to select a part of genes from the original data. Then we selected the key genes from the selected genes by t-statistics for tumor classification using IVGA. Finally, we used SVM to classify tumors based on the key genes selected using IVGA. To validate the efficiency, the proposed method is applied to classify three different DNA microarray data sets. The prediction results show that our method is efficient and feasible.
引用
收藏
页码：243 / +
页数：3
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