title: Combining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns.
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.
authors.affiliation: Bioinformatics Unit, Spanish National Cancer Center (CNIO), Melchor Fernández Almagro 3, 28029 Madrid, Spain.
authors.forename: Javier
authors.initials: J
authors.lastname: Herrero
authors.forename: Joaquín
authors.initials: J
authors.lastname: Dopazo
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D015870:Gene Expression; D005800:Genes, Fungal / Q000235:genetics; D016678:Genome; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D013223:Statistics as Topic / Q000379:methods*; D013997:Time Factors