Applying gene ontology to microarray gene expression data analysis

Selecting informative genes from microarray gene expression data is the most important task while performing data analysis on the large amount of data. Mining genes having regulatory relations within thousands of genes is essential. To fit this need, a number of methods were proposed from various points of view. However, most existing methods solely focus on gene expression values themselves without using any external information of genes. Gene Ontology (GO) provides biological information of genes or proteins involved. It utilizes a hierarchical structure to give additional biological information of genes as the aid for data analysis. In this paper, we first give a brief description about the GO structure and give a review of existing literatures that take GO into account. Subsequently, we propose a novel method to identify regulatory gene pairs in a real microarray dataset based on dynamic time warping (DTW) algorithm and GO. Finally, we summarize this paper with a discussion on how GO can be used to facilitate the analysis of microarray gene expression data.