Document Type
Dissertation
Date of Award
Spring 5-31-2004
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.
Recommended Citation
Aris, Virginie, "Non-parametric algorithms for evaluating gene expression in cancer using DNA microarray technology" (2004). Dissertations. 620.
https://digitalcommons.njit.edu/dissertations/620
