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Applying machine learning methods to...

University of Kansas., Electrical Engineering & Computer Science.

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Applying machine learning methods to suggest network involvement and functionality of genes in Saccharomyces cerevisiae.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Applying machine learning methods to suggest network involvement and functionality of genes in Saccharomyces cerevisiae./
Author:	Amthauer, Heather A.
Description:	1452 p.
Notes:	Advisers: Arvin Agah; Costas Tsatsoulis.
Contained By:	Dissertation Abstracts International69-08B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3324714
ISBN:	9780549772712

Applying machine learning methods to suggest network involvement and functionality of genes in Saccharomyces cerevisiae.
Amthauer, Heather A.

Applying machine learning methods to suggest network involvement and functionality of genes in Saccharomyces cerevisiae. - 1452 p.

Advisers: Arvin Agah; Costas Tsatsoulis.

Thesis (Ph.D.)--University of Kansas, 2008.

Elucidating genetic networks provides the foundation for the development of new treatments or cures for diseased pathways, and determining novel gene functionality is critical for bringing a better understanding on how an organism functions as a whole. In this dissertation, I developed a methodology that correctly locates genes that may be involved in genetic networks with a given gene based on its location over 50% of the time or based on its description over 43% of the time. I also developed a methodology that makes it easier to predict how a gene product behaves in a cellular context by suggesting the correct Gene Ontology term over 80% of the time. The designed software provides researchers with a way to focus their search for coregulated genes which will lead to better microarray chip design and limits the list of possible functions of a gene product. This ultimately saves the researcher time and money.

ISBN: 9780549772712Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Applying machine learning methods to suggest network involvement and functionality of genes in Saccharomyces cerevisiae.
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020 $a 9780549772712
035 $a (UMI)AAI3324714
035 $a AAI3324714
040 $a UMI $c UMI
100 1 $a Amthauer, Heather A. $3 1018714
245 1 0 $a Applying machine learning methods to suggest network involvement and functionality of genes in Saccharomyces cerevisiae.
300 $a 1452 p.
500 $a Advisers: Arvin Agah; Costas Tsatsoulis.
500 $a Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4831.
502 $a Thesis (Ph.D.)--University of Kansas, 2008.
520 $a Elucidating genetic networks provides the foundation for the development of new treatments or cures for diseased pathways, and determining novel gene functionality is critical for bringing a better understanding on how an organism functions as a whole. In this dissertation, I developed a methodology that correctly locates genes that may be involved in genetic networks with a given gene based on its location over 50% of the time or based on its description over 43% of the time. I also developed a methodology that makes it easier to predict how a gene product behaves in a cellular context by suggesting the correct Gene Ontology term over 80% of the time. The designed software provides researchers with a way to focus their search for coregulated genes which will lead to better microarray chip design and limits the list of possible functions of a gene product. This ultimately saves the researcher time and money.
590 $a School code: 0099.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0715
690 $a 0984
710 2 $a University of Kansas. $b Electrical Engineering & Computer Science. $3 1018713
773 0 $t Dissertation Abstracts International $g 69-08B.
790 $a 0099
790 1 0 $a Agah, Arvin, $e advisor
790 1 0 $a Alexander, Perry $e committee member
790 1 0 $a Chen, Xue-wen $e committee member
790 1 0 $a Ercal-Ozkaya, Gunes $e committee member
790 1 0 $a Kelly, John $e committee member
790 1 0 $a Tsatsoulis, Costas, $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3324714