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oai:www.ideals.illinois.edu:2142/81717 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

U of I Only 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. 103 p. Made available in DSpace on 2015-09-25T20:20:08Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3223632.pdf: 2716573 bytes, checksum: 659727c4286688d888bd0c46a8e1c0c9 (MD5) Previous issue date: 2006 Sylvian Ray Kosorukoff, Alexander Lvovich 2015-09-25T20:20:08Z 2015-09-25T20:20:08Z 10000-01-01 2006 2006 Embargo set by: Seth Robbins for item 82998 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. http://hdl.handle.net/2142/81717 (MiAaPQ)AAI3223632 eng Biology, Bioinformatics Methods for Cluster Analysis and Validation in Microarray Gene Expression Data text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.