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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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 |
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