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  1. (1. 西安电子科技大学 理学院, 陕西 西安 710071;
    2. 西安电子科技大学 电子工程学院, 陕西 西安 710071)

  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2004-04-20 发布日期:2004-04-20

Fuzzy c-mean clustering method for analyzing microarray gene expression data

GONG Gai-yun1;MAO Yong-cai2;GAO Xin-bo2;LIU San-yang1


  1. (1. School of Science, Xidian Univ., Xi'an 710071, China;
    2. School of Electronic Engineering, Xidian Univ., Xi'an 710071, China)

  • Received:1900-01-01 Revised:1900-01-01 Online:2004-04-20 Published:2004-04-20

摘要: 微阵列技术已成为染色体研究的主要工具,但是它所面临的挑战是如何对海量数据进行分析.利用模糊c-均值聚类对这些数据进行分析,从而发现有差异的基因表达.结果表明,模糊聚类是一种用来为微阵列基因表达数据寻找有差异的基因表达的一种有用工具.

关键词: 微阵列基因表达数据, 模糊c-均值聚类, 差异基因表达

Abstract: Microarray technologies are emerging as a promising tool for genomic studies. Today the challenge is how to analyze the resulting amounts of data. For this purpose clustering technologies have been applied to this field, but fuzy clustering technology analysis has not been used for microarray gene expression data. in this paper the fuzzy c-mean(FCM) clustering method is used to analyze such data in order to detect differentially expressed genes. Our results indicate that fuzzy clustering can be a useful tool to exploit the differential gene expression for microarray data.

Key words: microarray gene expression data, fuzzy c-mean(FCM) clustering, differential gene expression


  • R318