Clustering gene expression data using a novel model of self-organizing map  

作　　者：郝件 郁松年 席福利 

机构地区：[1]School of Computer Engineering and Science, Shanghai University, Shanghai 200072, P. R. China

出　　处：《Journal of Shanghai University(English Edition)》2007年第2期163-166,共4页上海大学学报（英文版）

摘　　要：Clustering is an important technique for analyzing gene expression data. The self-organizing map is one of the most useful clustering algorithms. However, its applicability is limited by the fact that some knowledge about the data is required prior to clustering. This paper introduces a novel model of self-organizing map （SOM） called growing hierarchical self-organizing map （GHSOM） to cluster gene expression data, The training and growth processes of GHSOM are entirely data driven, requiring no prior knowledge or estimates for parameter specification, thus help find not only the appropriate number of clusters but also the hierarchical relations in the data set. Compared with other clustering algorithms, GHSOM has better accuracy. To validate the results, a novel validation technique is used, known as figure of merit （FOM）.Clustering is an important technique for analyzing gene expression data. The self-organizing map is one of the most useful clustering algorithms. However, its applicability is limited by the fact that some knowledge about the data is required prior to clustering. This paper introduces a novel model of self-organizing map （SOM） called growing hierarchical self-organizing map （GHSOM） to cluster gene expression data, The training and growth processes of GHSOM are entirely data driven, requiring no prior knowledge or estimates for parameter specification, thus help find not only the appropriate number of clusters but also the hierarchical relations in the data set. Compared with other clustering algorithms, GHSOM has better accuracy. To validate the results, a novel validation technique is used, known as figure of merit （FOM）.

关 键 词：CLUSTERING neural network MICROARRAY machine learning 

分 类 号：Q786[生物学—分子生物学] TP183[自动化与计算机技术—控制理论与控制工程]