Theses and Dissertations at Montana State University (MSU)
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Item Profile matrix analyzer : clustering microarray data(Montana State University - Bozeman, College of Engineering, 2001) Taubman, Julie LynnItem Genepart algorithm, clustering and feature selection for DNA micro-array data(Montana State University - Bozeman, College of Engineering, 2004) Zhang, Weihua; Chairperson, Graduate Committee: Brendan MumeyThis paper provides the theoretical analysis of a new clustering and feature selection algorithm for the DNA micro-array data. This algorithm utilizes a branch and bound algorithm as the basic tool to quickly generate the optimal tissue sample partitions and select the gene subset which contributes the most to certain sample partition, it also combines the statistical probability method to identify important genes that have meaningful biological relationships to the classification or clustering problem. The proposed method combines feature selection and clustering processes and can be applied to the diagnostic system. Simulation results and analysis shown in the paper support the effectiveness of the combined algorithm.Item Design and application of the Kentucky microarray analysis suite(Montana State University - Bozeman, College of Engineering, 2006) Raghavan, Vijay Anand; Chairperson, Graduate Committee: Brendan MumeyIn 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.