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Analysis of gene expression data using self‐organizing maps
Author(s) -
Törönen Petri,
Kolehmainen Mikko,
Wong Garry,
Castrén Eero
Publication year - 1999
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/s0014-5793(99)00524-4
Subject(s) - self organizing map , computer science , data mining , visualization , computational biology , microarray analysis techniques , expression (computer science) , artificial neural network , unsupervised learning , data visualization , gene , gene expression , artificial intelligence , bioinformatics , pattern recognition (psychology) , biology , genetics , programming language
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.