Non-parametric algorithms for evaluating gene expression in cancer using DNA microarray technology

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 GeneChip^{TM} 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.

Name thesis:degree_name

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

Discipline thesis:degree_discipline

Federated Department of Biological Sciences

Year

2004

Author dc:creator

• Aris, Virginie

Contributors dc:contributor

• Michael Recce
• Peter P. Tolias
• Marvin N. Schwalb

• Breast cancer
• Ovarian cancer
• Oral cancer
• Multiple testing
• Prostate cancer
• Lung cancer
• Biology

Repository record dc:identifier

https://digitalcommons.njit.edu/dissertations/620

OAI identifier oai:identifier

oai:digitalcommons.njit.edu:dissertations-1675