Date of Award

Spring 2004

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Biology - (Ph.D.)

Department

Federated Department of Biological Sciences

First Advisor

Michael Recce

Second Advisor

Peter P. Tolias

Third Advisor

Marvin N. Schwalb

Fourth Advisor

Farzan Nadim

Fifth Advisor

Ronald Philip Hart

Abstract

Microarray technology has transformed the field of cancer biology by enabling the simultaneous evaluation of tens of thousands mRNA expression levels in a single experiment. This technology has been applied to medical science in order to find gene expression markers that cluster diseased and normal tissues, genes affected by treatments, and gene network interactions. All methods of microarray data analysis can be summarized as a study of differential gene expression. This study addresses three questions, 1) the roles of selectively expressed genes for the classification of cancer, 2) issues of accounting for both experimental and biological noise, and 3) issues of comparing data derived from different research groups using the Affymetrix GeneChipTM platform. A key finding of this study is that selectively expressed genes are very powerful when used for disease classification. A model was designed to reduce noise and eliminate false positives from true results. With this approach, data from different research groups can be integrated to increase information and enable a better understanding of cancer.

Included in

Biology Commons

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