01191nas a2200157   4500008004100000245011600041210006900157300001100226490000600237520046000243653007400703653011900777100001600896700001500912856010600927       2002                        eng d00aCombining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns0 aCombining hierarchical clustering and selforganizing maps for ex  a467-700 v13 aSelf-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.10aCluster Analysis Computational Biology/methods *Gene Expression Genes10aFungal/genetics *Genome Oligonucleotide Array Sequence Analysis/*methods Statistics as Topic/*methods Time Factors1 aHerrero, J.1 aDopazo, J.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12645919