Design and application of the Kentucky microarray analysis suite

Abstract

In recent years, microarrays have become the most widely used standard in the study of gene expression. The biggest problem in microarray data analysis is the dimensionality of the data, compared to other more traditional biomedical research methods. The inherent nature of the data, and the problems associated with the microarray data analysis, has led to the development of many methods for microarray data analysis. Microarray data analysis methods are commonly classified into Class Discovery methods e.g. clustering, Class Comparison methods e.g. predicting differentially expressed genes, and Class Prediction methods e.g. classification. In this thesis, a new microarray analysis tool called Kentucky Microarray Analysis Suite that has all the three major microarray analysis methods is introduced. As a proof of concept Affymetrix array data related to aging in C. elegans is analyzed with the Kentucky Microarray Analysis Suite and the results are presented.