Tumor Classification Using Non-negative Matrix Factorization

被引：0
|
作者：
Zhang, Ping [2 ]
Zheng, Chun-Hou [1 ,3 ]
Li, Bo [3 ]
Wen, Chang-Gang [1 ]
机构：
[1] Qufu Normal Univ, Coll Informat & Commun Technol, Rizhao 276826, Shandong, Peoples R China
[2] Qufu Normal Univ, Inst Automat, Rizhao 276826, Shandong, Peoples R China
[3] Chinese Acad Sci, Inst Intelligent Machines, Intelligent Comp Lab, Hefei 230031, Peoples R China
来源：
ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, PROCEEDINGS: WITH ASPECTS OF CONTEMPORARY INTELLIGENT COMPUTING TECHNIQUES | 2008年 / 15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; Non-negative matrix factorization; SVM;
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：
With the advent of DNA microarrys, it is now possible to use the microarry data for tumor classification. Yet previous works have not use the non-negative information of gene expression data for classification. In this paper, we propose a new method for tumor classification using gene expression data. In this method, we first extract new features of the gene expression data by virtue of non-negative matrix factorization (NMF) and its extension, i.e. sparse NMF (SNMF) then apply support vector machines (SVM) to classify the tumor samples using the extracted features. To better fit for classification aim, a new SNMF algorithm is also proposed.
引用
收藏
页码：236 / +
页数：3
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