Ordering Gene Expression Data Using One-Dimensional Self-Organizing Maps

Lalinka de C. T. GomesFernando J. Von ZubenPablo Moscato

The microarray technology allows researchers to simultaneously measure gene expression levels of thousands of genes. Analysis of data produced by such experiments provides knowledge about the gene function. An important step in the analysis of gene expression data is the detection of genes with similar expression patterns. Real-time computational tools for organization and visualization are crucial to understand and analyze the data. In this work, we make use of an algorithm based on self-organizing neural networks for organizing gene expression data in order to reveal trends in gene expression profiles under the biological viewpoint.

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