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Pursuing a biological interpretation...

Gunther, Cary Sidlett.

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Pursuing a biological interpretation of gene expression data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Pursuing a biological interpretation of gene expression data./
Author:	Gunther, Cary Sidlett.
Description:	96 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-01, Section: B, page: 0052.
Contained By:	Dissertation Abstracts International64-01B.
Subject:	Biology, Genetics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3078509
ISBN:	0493991328

Pursuing a biological interpretation of gene expression data.
Gunther, Cary Sidlett.

Pursuing a biological interpretation of gene expression data. - 96 p.

Source: Dissertation Abstracts International, Volume: 64-01, Section: B, page: 0052.

Thesis (Ph.D.)--The Rockefeller University, 2003.

Microarray technology allows simultaneous measurement of transcripts representing part or all of a genome. Optimal use of array data requires the ability to infer relationships among genes based on gene expression patterns in conjunction with the evolutionary history of genes and the interactions among gene products. This dissertation examines large-scale yeast and human gene expression studies in the light of information about evolutionary sequence relationships, protein interactions, biochemical pathways and disease phenotypes. Scrutiny of pairs of <italic>S. cerevisiae</italic> genes whose prokaryotic orthologs occur as regions of a single gene reveals that gene co-expression tends to reflect this fusion relationship. Analysis of the yeast protein interaction network demonstrates an inverse correlation between promiscuity of protein binding and frequency of regulatory events at the transcriptional level, as well as an ability for gene expression studies to predict conditions for multi-protein complex formation. Statistical analysis shows that gene expression distances can be modeled on shared, observed properties of genes. And analysis of human array data demonstrates the utility of arrays in the synthesis of diverse biological data sources and in the implication of genes or pathways in pathogenesis. The advent of the microarray platform affords basic scientists and clinicians a wealth of opportunities for exploring biological events and understanding disease pathogenesis. Taken together, the studies that comprise this dissertation define and elucidate biological approaches to the interpretation of array data and demonstrate that arrays can promote mechanistic explanations of disease phenomena.

ISBN: 0493991328Subjects--Topical Terms:

1017730
Biology, Genetics.

Pursuing a biological interpretation of gene expression data.
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100 1 $a Gunther, Cary Sidlett. $3 1946608
245 1 0 $a Pursuing a biological interpretation of gene expression data.
300 $a 96 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-01, Section: B, page: 0052.
500 $a Adviser: Theresa Gaasterland.
502 $a Thesis (Ph.D.)--The Rockefeller University, 2003.
520 $a Microarray technology allows simultaneous measurement of transcripts representing part or all of a genome. Optimal use of array data requires the ability to infer relationships among genes based on gene expression patterns in conjunction with the evolutionary history of genes and the interactions among gene products. This dissertation examines large-scale yeast and human gene expression studies in the light of information about evolutionary sequence relationships, protein interactions, biochemical pathways and disease phenotypes. Scrutiny of pairs of <italic>S. cerevisiae</italic> genes whose prokaryotic orthologs occur as regions of a single gene reveals that gene co-expression tends to reflect this fusion relationship. Analysis of the yeast protein interaction network demonstrates an inverse correlation between promiscuity of protein binding and frequency of regulatory events at the transcriptional level, as well as an ability for gene expression studies to predict conditions for multi-protein complex formation. Statistical analysis shows that gene expression distances can be modeled on shared, observed properties of genes. And analysis of human array data demonstrates the utility of arrays in the synthesis of diverse biological data sources and in the implication of genes or pathways in pathogenesis. The advent of the microarray platform affords basic scientists and clinicians a wealth of opportunities for exploring biological events and understanding disease pathogenesis. Taken together, the studies that comprise this dissertation define and elucidate biological approaches to the interpretation of array data and demonstrate that arrays can promote mechanistic explanations of disease phenomena.
590 $a School code: 0189.
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650 4 $a Biology, Biostatistics. $3 1018416
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3078509