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Probe level models for DNA microarrays.

Wu, Zhijin.

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Probe level models for DNA microarrays.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Probe level models for DNA microarrays./
Author:	Wu, Zhijin.
Description:	108 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-04, Section: B, page: 1832.
Contained By:	Dissertation Abstracts International66-04B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3172728
ISBN:	9780542106712

Probe level models for DNA microarrays.
Wu, Zhijin.

Probe level models for DNA microarrays. - 108 p.

Source: Dissertation Abstracts International, Volume: 66-04, Section: B, page: 1832.

Thesis (Ph.D.)--The Johns Hopkins University, 2005.

Microarrays are an example of the powerful high-throughput genomics tools that are revolutionizing the measurement of biological systems. The applications of microarrays range from measuring gene expression levels to diverse high genomic endpoints including yeast mutant representations, the presence of SNPs, presence of deletions/insertions, and protein binding sites by chromatin immuno-precipitation (known as ChIP-chip). In each case, the genomic units of measurement are relatively short DNA molecules referred to as probes1. The raw measurements from microarrays are fluorescent intensities of the probes. Besides specific binding, optical noise, non-specific hybridization and systematic variation are unavoidable. Therefore, probe level data typically go through several steps that adjust for non-biological variation and convert the probe-level measurements to gene level summary. This procedure is referred to as preprocessing. Without appropriate understanding of the bias and variance of the probe level measurements, biological inferences following the preprocessing steps will be compromised. This thesis presents a model-based approach that is empirically motivated and inspired by hybridization theory. Background adjustment motivated from this model improves existing approaches on bottom line results. A statistical framework is developed from this model that permits the integration of preprocessing into the standard statistical analysis flow of microarray data.

ISBN: 9780542106712Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Probe level models for DNA microarrays.
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100 1 $a Wu, Zhijin. $3 1911987
245 1 0 $a Probe level models for DNA microarrays.
300 $a 108 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-04, Section: B, page: 1832.
500 $a Advisers: Rafael A. Irizarry; Scott L. Zeger.
502 $a Thesis (Ph.D.)--The Johns Hopkins University, 2005.
520 $a Microarrays are an example of the powerful high-throughput genomics tools that are revolutionizing the measurement of biological systems. The applications of microarrays range from measuring gene expression levels to diverse high genomic endpoints including yeast mutant representations, the presence of SNPs, presence of deletions/insertions, and protein binding sites by chromatin immuno-precipitation (known as ChIP-chip). In each case, the genomic units of measurement are relatively short DNA molecules referred to as probes1. The raw measurements from microarrays are fluorescent intensities of the probes. Besides specific binding, optical noise, non-specific hybridization and systematic variation are unavoidable. Therefore, probe level data typically go through several steps that adjust for non-biological variation and convert the probe-level measurements to gene level summary. This procedure is referred to as preprocessing. Without appropriate understanding of the bias and variance of the probe level measurements, biological inferences following the preprocessing steps will be compromised. This thesis presents a model-based approach that is empirically motivated and inspired by hybridization theory. Background adjustment motivated from this model improves existing approaches on bottom line results. A statistical framework is developed from this model that permits the integration of preprocessing into the standard statistical analysis flow of microarray data.
520 $a 1The current standard term for these units is feature. For historical reasons, we use probe throughout this thesis.
590 $a School code: 0098.
650 4 $a Biology, Biostatistics. $3 1018416
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710 2 0 $a The Johns Hopkins University. $3 1017431
773 0 $t Dissertation Abstracts International $g 66-04B.
790 1 0 $a Irizarry, Rafael A., $e advisor
790 1 0 $a Zeger, Scott L., $e advisor
790 $a 0098
791 $a Ph.D.
792 $a 2005
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