Date of Award

2001

Level of Access

Open-Access Thesis

Degree Name

Master of Science (MS)

Department

Computer Engineering

Advisor

Habtom Ressom

Second Committee Member

Mohamad Musavi

Third Committee Member

Bruce Segee

Abstract

DNA microarray technology allows for the parallel analysis of the expression of genes in an organism. The wealth of spatio-temporal data provided by the technology allows us to attempt to reverse engineer the genetic network. Fuzzy logic has been proposed as a method of analyzing the relationships between genes as well as their corresponding proteins. Combinations of genes are entered into a fuzzy model of gene interaction and evaluated on the basis of how well the combination fits the model. Those combinations of genes that fit the model are likely to be related. However, current analysis algorithms are slow and computationally complex, sensitive to noise in gene expression data, and only tested and validated on simple models of gene interaction. This thesis proposes improvements to the fuzzy gene modeling method by reducing the computation time, altering the model to make it more robust with respect to noise, and generalizing the model to accommodate any combination of genes and model of gene interaction. The improved algorithm achieves a speed-up of 15-50%, significant resistance to noise, and a degree of generality that enables the analysis of large gene complexes.

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