| 对基因表达数据进行聚类的一种新型自组织映射模型 |
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| 引用本文： | 郝伟,郁松年,席福利.对基因表达数据进行聚类的一种新型自组织映射模型[J].上海大学学报(英文版),2007,11(2):163-166. |
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| 作者姓名： | 郝伟 郁松年 席福利 |
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| 作者单位： | School of Computer Engineering and Science Shanghai University，School of Computer Engineering and Science，Shanghai University，School of Computer Engineering and Science，Shanghai University，Shanghai 200072，P.R.China，Shanghai 200072，P.R.China，Shanghai 200072，P.R.China |
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| 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）.
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| 关 键 词： | 基因表达数据 聚类分析 自组织图模型 神经网络 机器学习 |
| 收稿时间： | 2005-07-18 |
| 修稿时间： | 2005-10-09 |
Clustering gene expression data using a novel model of self-organizing map |
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| Wei Hao,Song-nian Yu,Fu-li Xi.Clustering gene expression data using a novel model of self-organizing map[J].Journal of Shanghai University(English Edition),2007,11(2):163-166. |
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| Authors: | Wei Hao Song-nian Yu Fu-li Xi |
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| Institution: | School of Computer Engineering and Science, Shanghai University, Shanghai 200072, P. R. China |
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| Abstract: | 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 arc 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). |
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| Keywords: | clustering neural network microarray machine learning |
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