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January 24, 2010 | History

Integration of clustering and statistical analysis of microarray data 1 edition

Integration of clustering and statistical analysis of microarray data
Julia Min-Jeong Chae

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Integration of clustering and statistical analysis of microarray data.

Published 2005 .
Written in English.

About the Book

The enormous amount of biological information produced by DNA microarrays gave rise to challenges in creating more powerful analysis techniques for computer scientists. In this thesis, we examine the strengths and the weaknesses of clustering and statistical analysis and propose different integration approaches to improve the effectiveness of analyzing microarray data.The success of clustering algorithms relies on the integrity of the expression data, and they do not account for noise and non-independence among a set of experimental conditions. We used statistical analysis to take into account any dependencies and to select differentially regulated genes that were used as the inputs for clustering algorithms. On the other hand, statistical analysis relies on the integrity of the clinical data, which has some restrictions and drawbacks. We used a clustering algorithm to dynamically assign classes to the samples from the data itself and used these classes as response variables for statistical analysis.

Edition Notes

Source: Masters Abstracts International, Volume: 44-02, page: 0928.

Thesis (M.Sc.)--University of Toronto, 2005.

Electronic version licensed for access by U. of T. users.

GERSTEIN MICROTEXT copy on microfiche (2 microfiches).

The Physical Object

102 leaves.
Number of pages

ID Numbers

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History Created December 11, 2009 · 2 revisions Download catalog record: RDF / JSON

January 24, 2010 Edited by WorkBot add more information to works
December 11, 2009 Created by WorkBot add works page