Analysis of gene expression data using self‐organizing maps

FEBS Letters

Törönen, Petri²  Castrén, Eero²  Kolehmainen, Mikko¹  Wong, Garry² 

[1] Department of Environmental Sciences, University of Kuopio, Box 1627, 70211 Kuopio, Finland;A.I. Virtanen Institute, University of Kuopio, Box 1627, 70211 Kuopio, Finland

关键词: Self-organizing map;    Sammon's mapping;    Gene expression data;    Yeast;    Cluster analysis;   

DOI  :  10.1016/S0014-5793(99)00524-4

学科分类：生物化学/生物物理

来源: John Wiley & Sons Ltd.

【 摘 要 】

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.

【 授权许可】

Unknown