Date of Award

8-8-2005

Degree Type

Closed Thesis

Degree Name

Master of Science (MS)

Department

Mathematics and Statistics

First Advisor

Susmita Datta - Chair

Second Advisor

Yu-Sheng Hsu

Third Advisor

Yichuan Zhao

Fourth Advisor

Ghengsheng Qin

Abstract

Simultaneous measurement of the expression levels of thousands to ten thousand genes in multiple tissue types is a result of advancement in microarray technology. These expression levels provide clues about the gene functions and that have enabled better diagnosis and treatment of serious disease like cancer. To solve the mystery of unknown gene functions, biological to statistical mapping is needed in terms of classifying the genes. Here we introduce a novel approach of combining both statistical consistency and biological relevance of the clusters produced by a clustering method. Here we employ two performance measures in combination for measuring statistical stability and functional similarity of the cluster members using a set of gene expressions with known biological functions. Through this analysis we construct a platform to predict about unknown gene functions using the outperforming clustering algorithm.

Share

COinS