Methods for Cluster Analysis and Validation in Microarray Gene Expression Data
Kosorukoff, Alexander Lvovich
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https://hdl.handle.net/2142/81717
Description
Title
Methods for Cluster Analysis and Validation in Microarray Gene Expression Data
Author(s)
Kosorukoff, Alexander Lvovich
Issue Date
2006
Doctoral Committee Chair(s)
Sylvian Ray
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Biology, Bioinformatics
Language
eng
Abstract
Results. We evaluate this method using both artificial and yeast microarray data. By choosing parameters settings that minimize FCS values and maximize CS values we show major advantages over other clustering methods in particular for identifying combinatorially regulated groups of genes. The results produced provide remarkable enrichment for cis-regulatory elements in clusters of genes known to be regulated by such elements and evidence of extensive combinatorial regulation. Moreover, the method can be generalized when prior information about cis-regulatory sites is absent or it is desirable to calculate FCS values based on functional categorization.
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