Clustering gene expression data using self-organizing maps and k-means clustering

被引：0
|
作者：
Yano, N [1 ]
Kotani, A [1 ]
机构：
[1] Kobe Univ, Fac Engn, Kobe, Hyogo 6578501, Japan
来源：
SICE 2003 ANNUAL CONFERENCE, VOLS 1-3 | 2003年
关键词：
gene expression data; self-organizing maps (SOM); k-means clustering;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We present a method of combining a self-organizing map (SOM) and k-means clustering for analyzing and categorizing gene expression data. Some studies have addressed about visualizing cluster structures in an easily understandable manner using a U-matrix or Sammon's mapping. However, it is difficult to find obvious clustering boundaries in the SOM results. We show that the method is effective for categorizing the published data of yeast gene expression.
引用
收藏
页码：3211 / 3215
页数：5
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