Type of Document Master's Thesis Author Karmakar, Saurav URN etd-07222005-220551 Title Statistical Stability and Biological Validity of Clustering Algorithms for Analyzing Microarray Data Degree Master of Science Department Mathematics and Statistics Advisory Committee
Advisor Name Title SUSMITA DATTA Committee Chair GHENGSHENG QIN Committee Member YICHUAN ZHAO Committee Member YU-SHENG HSU Committee Member Keywords
- Functional similarity
- Functional Validation
- Statistical validation
- Gene Function
- Gene Expression
- Microarray
- Stability
- Clustering
Date of Defense 2005-07-18 Availability restricted 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.Files
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