Combining Hierarchical Clustering and Self-Organizing Maps for Exploratory Analysis of Gene Expression Patterns
- 11 July 2002
- journal article
- research article
- Published by American Chemical Society (ACS) in Journal of Proteome Research
- Vol. 1 (5) , 467-470
- https://doi.org/10.1021/pr025521v
Abstract
Self-organizing maps (SOM) constitute an alternative to classical clustering methods because of its linear run times and superior performance to deal with noisy data. Nevertheless, the clustering obtained with SOM is dependent on the relative sizes of the clusters. Here, we show how the combination of SOM with hierarchical clustering methods constitutes an excellent tool for exploratory analysis of massive data like DNA microarray expression patterns. Keywords: DNA array, gene expression patterns, hierarchical clustering, SOM, SOTAKeywords
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