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Models for detecting gene regulatory...

Brock, Guy Nathaniel.

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Models for detecting gene regulatory networks from microarray data.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Models for detecting gene regulatory networks from microarray data./
Author:	Brock, Guy Nathaniel.
Description:	115 p.
Notes:	Chair: Laura Salter.
Contained By:	Dissertation Abstracts International64-06B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3093031

Models for detecting gene regulatory networks from microarray data.
Brock, Guy Nathaniel.

Models for detecting gene regulatory networks from microarray data. - 115 p.

Chair: Laura Salter.

Thesis (Ph.D.)--The University of New Mexico, 2003.

The analysis of gene expression microarrays plays an important role in elucidating the function of genes, including the discovery of genetic interactions that regulate gene expression. Several methods for modelling such gene regulatory networks exist, including a variety of continuous and discrete models. An interesting alternative to these methods is fuzzy logic. Fuzzy logic is a method for analyzing data that categorizes the data into multiple states with partial membership, thus violating the law of excluded middle. However, the guidelines for modelling gene expression data with fuzzy logic are fairly open. For example, the number of states used to classify the data and the shape of the membership functions used for classification may be altered to produce different implementations of the fuzzy logic method.Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Models for detecting gene regulatory networks from microarray data.
LDR:02823nam 2200289 a 45 001 929800
005 20110427
008 110427s2003 eng d
035 $a (UnM)AAI3093031
035 $a AAI3093031
040 $a UnM $c UnM
100 1 $a Brock, Guy Nathaniel. $3 1253285
245 1 0 $a Models for detecting gene regulatory networks from microarray data.
300 $a 115 p.
500 $a Chair: Laura Salter.
500 $a Source: Dissertation Abstracts International, Volume: 64-06, Section: B, page: 2735.
502 $a Thesis (Ph.D.)--The University of New Mexico, 2003.
520 $a The analysis of gene expression microarrays plays an important role in elucidating the function of genes, including the discovery of genetic interactions that regulate gene expression. Several methods for modelling such gene regulatory networks exist, including a variety of continuous and discrete models. An interesting alternative to these methods is fuzzy logic. Fuzzy logic is a method for analyzing data that categorizes the data into multiple states with partial membership, thus violating the law of excluded middle. However, the guidelines for modelling gene expression data with fuzzy logic are fairly open. For example, the number of states used to classify the data and the shape of the membership functions used for classification may be altered to produce different implementations of the fuzzy logic method.
520 $a In this work, an existing fuzzy logic model is modified to involve an arbitrary number of states. The affect of altering the number of states on the results is investigated, as is the limiting behavior of the algorithm as the number of states tends to infinity. In addition, a probabilistic model is proposed as an alternative to the fuzzy logic model. It is proven that as the number of states used to classify the data goes to infinity, both of these models converge to a limiting regression surface. Thus, a third alternative for modelling gene regulatory networks is developed using regression techniques.
520 $a All three models are tested and compared using simulated microarray data and actual yeast cell cycle microarray data. The models effectively recover networks from the simulation study, while returning biologically plausible results using the yeast cell cycle data. In addition, a unique application of the network models is developed which combines results from quantitative trait loci (QTL) and microarray experiments. All three models serve as useful tools for searching microarray data sets for genetic interactions.
590 $a School code: 0142.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Statistics. $3 517247
690 $a 0308
690 $a 0463
710 2 0 $a The University of New Mexico. $3 1018024
773 0 $t Dissertation Abstracts International $g 64-06B.
790 $a 0142
790 1 0 $a Salter, Laura, $e advisor
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3093031