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Using mixed effects models to integr...

Harvard University.

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Using mixed effects models to integrate high-dimensional, genomic data and an array-based analysis of the evolution of brain aging.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Using mixed effects models to integrate high-dimensional, genomic data and an array-based analysis of the evolution of brain aging./
作者:	Loerch, Patrick Michael.
面頁冊數:	134 p.
附註:	Advisers: Cheng Li; Bruce Yankner.
Contained By:	Dissertation Abstracts International69-01B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3295930
ISBN:	9780549408932

Using mixed effects models to integrate high-dimensional, genomic data and an array-based analysis of the evolution of brain aging.
Loerch, Patrick Michael.

Using mixed effects models to integrate high-dimensional, genomic data and an array-based analysis of the evolution of brain aging. - 134 p.

Advisers: Cheng Li; Bruce Yankner.

Thesis (Ph.D.)--Harvard University, 2008.

This dissertation presents a novel contribution to scientific knowledge in the form of the development of statistical methodology for integrating diverse, genomic data sets, and by contributing to the understanding of the conservation of post-reproductive brain aging in mammals. Broadly speaking, this work is divided into two complimentary sections. The first section describes the development of a mixed effects modeling approach for integrating high-dimensional, genomic data sets. Microarray-based technologies allow researchers to monitor gene expression, transcription factor binding and alternative splicing on a genome-wide scale. The current challenge is to develop methods that analyze and integrate these vast data sets in a manner that produces biologically meaningful results, which can then be experimentally followed-up in the lab. The approach is described in terms of analyzing array data in the context of biologically-related groups of genes. A key component to this approach is the development of a novel model selection strategy that utilizes the biological information contained in the Gene Ontology graph in order to balance between biological specificity and model parsimony.

ISBN: 9780549408932Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Using mixed effects models to integrate high-dimensional, genomic data and an array-based analysis of the evolution of brain aging.
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245 1 0 $a Using mixed effects models to integrate high-dimensional, genomic data and an array-based analysis of the evolution of brain aging.
300 $a 134 p.
500 $a Advisers: Cheng Li; Bruce Yankner.
500 $a Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0035.
502 $a Thesis (Ph.D.)--Harvard University, 2008.
520 $a This dissertation presents a novel contribution to scientific knowledge in the form of the development of statistical methodology for integrating diverse, genomic data sets, and by contributing to the understanding of the conservation of post-reproductive brain aging in mammals. Broadly speaking, this work is divided into two complimentary sections. The first section describes the development of a mixed effects modeling approach for integrating high-dimensional, genomic data sets. Microarray-based technologies allow researchers to monitor gene expression, transcription factor binding and alternative splicing on a genome-wide scale. The current challenge is to develop methods that analyze and integrate these vast data sets in a manner that produces biologically meaningful results, which can then be experimentally followed-up in the lab. The approach is described in terms of analyzing array data in the context of biologically-related groups of genes. A key component to this approach is the development of a novel model selection strategy that utilizes the biological information contained in the Gene Ontology graph in order to balance between biological specificity and model parsimony.
520 $a The second section discusses a phylogenetic comparison of post-reproductive brain aging and its relevance to studies the employ animal models in the aging field. This portion of the dissertation is further divided into three subsections. To obtain greater insight into mammalian brain aging, subsection one describes the first genome-scale comparison of the aging brain transcriptomes of humans, rhesus macaques and mice. This analysis indicates that increased expression of neuroprotective genes and reduced expression of synaptic signaling genes are conserved features of mammalian brain aging. However, the repression of neuronal gene expression in humans, and in particular genes that mediate inhibitory neurotransmission, may render the human brain uniquely vulnerable to age-related neurodegeneration. The limited conservation of post-reproductive brain aging is then examined in the context of the recent advances in animal models of aging, specifically long-lived and advanced-aging models. Lastly, the age-related increase in DNA damage that is observed in the human cortex, which is absent from the aging mouse cortex, is further characterized using ChIP-Chip technology to probe for age-related differences in oxidative DNA damage.
520 $a This dissertation concludes with a description of future steps for the application of a mixed effects modeling approach to combining ChIP-Chip-based DNA damage data with microarray expression data as a means of characterizing the genome-wide transcriptional response to age-related changes in DNA damage.
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791 $a Ph.D.
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