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In recent years, microarray gene expression studies have been actively pursued for extracting significant biological knowledge hidden under a large volume of gene expression profiles accumulated by DNA microarray experiments. Particularly great attentions have been paid to a variety of data mining schemes for gene function discovery [Eisen et al., 1998], disease diagnosis [Saiki et al., 2008], pathway analysis [Werner et al., 2008], pharmaceutical target identification [Corn et al., 2007], and etc. Out of these, the pathway analysis is one of the most significant problems in current bioinformatics researches. Pathway analysis concerns about identifying significant pathways, which are the groups of genes actively involved in some biological processes, based on the gene expression profiles. By doing so, our objective is to understand the role of such biological processes in a given experiment condition and their associated gene activities. In this chapter, we investigate several computational techniques that are often used in a variety of contexts for pathway analysis. Specifically, we first give a brief overview of microarray gene expression profile data and some biological resources available to be used for pathway analysis. Then we examine three different approaches, i.e. clustering-based methods, gene-based methods, and Gene set-based methods, all of which can be employed for understanding biological pathways in various environments. Subsequently we perform some case studies with the leukemia disease data and finally conclude this chapter with some remarks and discussions.

Microarray Data Mining for Biological Pathway Analysis