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Association based prioritization of ...

Lee, Jang H.

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Association based prioritization of genes.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Association based prioritization of genes./
作者:	Lee, Jang H.
面頁冊數:	153 p.
附註:	Source: Dissertation Abstracts International, Volume: 72-07, Section: B, page: .
Contained By:	Dissertation Abstracts International72-07B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3453091
ISBN:	9781124609683

Association based prioritization of genes.
Lee, Jang H.

Association based prioritization of genes. - 153 p.

Source: Dissertation Abstracts International, Volume: 72-07, Section: B, page: .

Thesis (Ph.D.)--Arizona State University, 2011.

Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse kinds, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data.

ISBN: 9781124609683Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Association based prioritization of genes.
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500 $a Source: Dissertation Abstracts International, Volume: 72-07, Section: B, page: .
500 $a Adviser: Graciela Gonzalez.
502 $a Thesis (Ph.D.)--Arizona State University, 2011.
520 $a Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse kinds, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data.
520 $a Biological networks are often sparse, which can impede the operation of association-based gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties.
520 $a While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our network-based model for prioritization, with encouraging results.
520 $a To validate these approaches in a disease-specific context, we built a schizophrenia-specific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
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790 1 0 $a Gonzalez, Graciela, $e advisor
790 1 0 $a Ye, Jieping $e committee member
790 1 0 $a Davulcu, Hasan $e committee member
790 1 0 $a Gallitano-Mendel, Amelia $e committee member
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