Fuzzy clustering analysis of microarray data

被引：4

作者：

Han, Lixin ^{[1
,2
]}

Zeng, Xiaoqin ^{[1
]}

Yan, Hong ^{[3
,4
]}

机构：

[1] Hohai Univ, Dept Comp Sci & Engn, Nanjing 210008, Jiangsu, Peoples R China

[2] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210008, Peoples R China

[3] City Univ Hong Kong, Dept Elect Engn, Kowloon, Hong Kong, Peoples R China

[4] Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia

来源：

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE | 2008年 / 222卷 / H7期

基金：

中国国家自然科学基金;

关键词：

gene expression data analysis; fuzzy clustering; fuzzy c-means; principal component analysis;

D O I：

10.1243/09544119JEIM384

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Fuzzy clustering is a useful tool for identifying relevant subsets of microarray data. This paper proposes a fuzzy clustering method for microarray data analysis. An advantage of the method is that it used a combination of the fuzzy c-means and the principal component analysis to identify the groups of genes that show similar expression patterns. It allows a gene to belong to more than a gene expression pattern with different membership grades. The method is suitable for the analysis of large amounts of noisy microarray data.

引用

页码：1143 / 1148

页数：6

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