title: Analysis of gene expression data using self-organizing maps.
abstract: DNA microarray technologies together with rapidly increasing genomic sequence information is leading to an explosion in available gene expression data. Currently there is a great need for efficient methods to analyze and visualize these massive data sets. A self-organizing map (SOM) is an unsupervised neural network learning algorithm which has been successfully used for the analysis and organization of large data files. We have here applied the SOM algorithm to analyze published data of yeast gene expression and show that SOM is an excellent tool for the analysis and visualization of gene expression profiles.
authors.affiliation: A.I. Virtanen Institute, University of Kuopio, Finland.
authors.forename: P
authors.initials: P
authors.lastname: Törönen
authors.forename: M
authors.initials: M
authors.lastname: Kolehmainen
authors.forename: G
authors.initials: G
authors.lastname: Wong
authors.forename: E
authors.initials: E
authors.lastname: Castrén
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D016000:Cluster Analysis; D016208:Databases, Factual; D015870:Gene Expression; D008967:Molecular Biology; D016571:Neural Networks, Computer; D012984:Software