Analysis and visualization of gene expression data using self-organizing maps
Neural Networks - New developments in self-organizing maps
Cancer classification using gene expression data
Information Systems - Special issue: Data management in bioinformatics
A primer on gene expression and microarrays for machine learning researchers
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Data mining using rule extraction from Kohonen self-organising maps
Neural Computing and Applications
Analyzing tumor gene expression profiles
Artificial Intelligence in Medicine
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The self-organizing map (SOM) is useful within bioinformatics research because of its clustering and visualization capabilities. The SOM is a vector quantization method that reduces the dimensionality of original measurement and visualizes individual tumor sample in a SOM component plane. The data is taken from cDNA microarray experiments on Diffuse Large B-Cell Lymphoma (DLBCL) data set of Alizadeh. The objective is to get the SOM to discover biologically meaningful clusters of genes that are active in this particular form of cancer. Despite their powers of visualization, SOMs cannot provide a full explanation of their structure and composition without further detailed analysis. The only method to have gone someway towards filling this gap is the unified distance matrix or U-matrix technique. This method will be used to provide a better understanding of the nature of discovered gene clusters. We enhance the work of previous researchers by integrating the clustering results with the Gene Ontology for deeper analysis of biological meaning, identification of diversity in gene expression of the DLBCL tumors and reflecting the variations in tumor growth rate.