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Bioinformatics: Microarray Technology

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1. INTRODUCTION
Incorporation of prior pathway knowledge into microarray data has become a popular research area in bioinformatics. Ever since the advent of microarray technology in the field of biomedical research, it led to the development of numerous analytic methods to analyse gene expression data from microarray. However, most of the methods are single gene based which unable to detect subtle but coordinated differentially expressed genes and these genes often dropped during feature selection by strict cut-off threshold [1,2]. In contrast, pathway-based microarray analysis consider a set of biologically related genes and help to detect these subtle changes in gene expression. Many discussion has been done by researchers regard pathway-based analysis such as reviews done regarding computational approaches proposed for pathway-based analysis by Curtis et al. [3] and also Misman et al. [4], enrichment-approach using various Kolmogorov-Smirnov by Subramanian et al. [5], random forest-based pathway analysis by Pang et al. [6], and Harris et al. [7] who proposed gene grouping based on gene ontology.

A biological pathway is a series of action among molecules in a cell that triggers the production of new molecules such as fat or proteins or spur a cell to move as well as generate signal that able to turn genes on and off. This is very important to researchers as they can detect the problem or details regarding a particular disease by identifying genes, proteins and other
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