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Comparison and evaluation of statistical-learning methods for gene function prediction in Arabidopsis thaliana 1 edition

Comparison and evaluation of statistical-learning methods for gene fun ...
Hui Lan

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Comparison and evaluation of statistical-learning methods for gene function prediction in Arabidopsis thaliana.

Published 2005 .
Written in English.

About the Book

Approximately 30,000 genes have been discovered by genome sequencing in Arabidopsis thaliana completed in 2000. However, about half of these genes have not been assigned any function yet. The goal of this study is to identify unknown genes that are potentially involved in plant responses to stresses. We evaluated and compared five basic statistical learning methods for gene function prediction on a genome-wide scale using gene expression data. None of these methods was uniformly better than the others. In addition, we investigated combining these methods for prediction. The combined method achieved better classification performance than the basic methods for the top "response to stress" function. With precision above 50%, we identified a considerable number of unknown genes that are potentially stress-associated, which are currently being validated by biologists.

Edition Notes

Source: Masters Abstracts International, Volume: 44-02, page: 0936.

Advisor: A. Bonner

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

Pagination
111 leaves.
Number of pages
111

ID Numbers

Open Library
OL19216564M
ISBN 10
0494071869

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