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New techniques in clustering and mic...

Dyson, Gregory E.

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New techniques in clustering and microarray data analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	New techniques in clustering and microarray data analysis./
Author:	Dyson, Gregory E.
Description:	102 p.
Notes:	Source: Dissertation Abstracts International, Volume: 65-06, Section: B, page: 2987.
Contained By:	Dissertation Abstracts International65-06B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3138143
ISBN:	0496853134

New techniques in clustering and microarray data analysis.
Dyson, Gregory E.

New techniques in clustering and microarray data analysis. - 102 p.

Source: Dissertation Abstracts International, Volume: 65-06, Section: B, page: 2987.

Thesis (Ph.D.)--University of Michigan, 2004.

In recent years, the use of gene expression data has expanded to many areas of medical research, drug discovery and development. Technological development will enable the entire human genome to be spotted onto one microarray in the near future.

ISBN: 0496853134Subjects--Topical Terms:

517247
Statistics.

New techniques in clustering and microarray data analysis.
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035 $a (UnM)AAI3138143
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100 1 $a Dyson, Gregory E. $3 1930343
245 1 0 $a New techniques in clustering and microarray data analysis.
300 $a 102 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-06, Section: B, page: 2987.
500 $a Chair: Chien-Fu Jeff Wu.
502 $a Thesis (Ph.D.)--University of Michigan, 2004.
520 $a In recent years, the use of gene expression data has expanded to many areas of medical research, drug discovery and development. Technological development will enable the entire human genome to be spotted onto one microarray in the near future.
520 $a Multiplicity issues arise when attempting to discern which of the &sim;10,000 genes are differentially expressed. The Multiplicity-Adjusted Order Statistics Analysis (MAOSA) technique developed in the thesis is based on the normality of the midd
520 $a There is no tool to explore the relationship between groups of clustered genes at both the cluster level and the object level (i.e., gene-to-gene). The Inter-Cluster Investigator (ICI) is developed to address this need. It identifies positive an
590 $a School code: 0127.
650 4 $a Statistics. $3 517247
650 4 $a Biology, Biostatistics. $3 1018416
690 $a 0463
690 $a 0308
710 2 0 $a University of Michigan. $3 777416
773 0 $t Dissertation Abstracts International $g 65-06B.
790 1 0 $a Wu, Chien-Fu Jeff, $e advisor
790 $a 0127
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3138143