Abstract
Gene expression pattern is one of the important biological information that represents the process that a gene works. Gene expression pattern has been widely used for the hint in predicting gene function. Gene classification based on gene expression pattern is a powerful approach to understand gene function and/or gene regulation for model plants; those of gene expression are comprehensively characterized sufficiently. However, a large-scale data set is required and there is a limitation of valuation of quantitative gene expression pattern in current gene classification methodology using gene expression correlation. Here, we try to use Cosine distance and Euclidean distance as indices for similarity of gene expression pattern to classify rice genes. We calculated the distances between all pair of genes from gene expression pattern of Affymetrix Rice genome array data, which was processed with RMA and searched for similarly expressed genes. As a result, we found similarly expressed gene populations including far more a designated GO term than using Cosine distance alone. We'll also report the advantage in this methodology to compare gene expression.
