| Title | Combining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns |
| Publication Type | Journal Article |
| Year of Publication | 2002 |
| Authors | Herrero, J, Dopazo, J |
| Journal | J Proteome Res |
| Volume | 1 |
| Pagination | 467-70 |
| Keywords | Cluster Analysis Computational Biology/methods *Gene Expression Genes; Fungal/genetics *Genome Oligonucleotide Array Sequence Analysis/*methods Statistics as Topic/*methods Time Factors |
| 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. |
| Notes | Herrero, Javier Dopazo, Joaquin Research Support, Non-U.S. Gov’t United States Journal of proteome research J Proteome Res. 2002 Sep-Oct;1(5):467-70. |
| URL | http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12645919 |
