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.
| Identifer | oai:union.ndltd.org:GEORGIA/oai:scholarworks.gsu.edu:math_theses-1002 |
| Date | 08 August 2005 |
| Creators | Karmakar, Saurav |
| Publisher | ScholarWorks @ Georgia State University |
| Source Sets | Georgia State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Mathematics Theses |
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