聚类分析
计算机科学
人工智能
维数(图论)
表达式(计算机科学)
数据挖掘
高维数据聚类
无监督学习
模式识别(心理学)
机器学习
计算生物学
数学
生物
程序设计语言
纯数学
作者
Chun Tang,Li Zhang,Aidong Zhang,Murali Ramanathan
出处
期刊:Bioinformatics and Bioengineering
日期:2001-03-04
被引量:111
标识
DOI:10.1109/bibe.2001.974410
摘要
DNA arrays can be used to measure the expression levels of thousands of genes simultaneously. Most research is focusing on interpretation of the meaning of the data. However, the majority of methods are supervised, with less attention having been paid to unsupervised approaches which are important when domain knowledge is incomplete or hard to obtain. In this paper we present a new framework for unsupervised analysis of gene expression data which applies an interrelated two-way clustering approach to the gene expression matrices. The goal of clustering is to find important gene patterns and perform cluster discovery on samples. The advantage of this approach is that we can dynamically use the relationships between the groups of genes and samples while iteratively clustering through both gene-dimension and sample-dimension. We illustrate the method on gene expression data from a study of multiple sclerosis patients. The experiments demonstrate the effectiveness of this approach.
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