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Fuzzy Logic, Genetic Algorithms and Next-State Models

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abstract
The construction of networks that model biological activities is an important open question. In this thesis, we explore the modeling of biological networks using Fuzzy Logic. Fuzzy Logic models allow for genes to not only be on or off but also be partially on or partially off. The models we developed are compared to network models developed from a literature sources by jActiveModules (JAM). Our results are mixed. The models we constructed attempt to minimize the average absolute value of the differences between the original laboratory biological data set and a data set generated by the model itself. The simulated data was constructed in a next-state process. We found that we could construct Fuzzy Logic models that generated simulated data within the apparent biological variability of the original microarray data. However, our models do not match the results from JAM process. The conclusion is that the Genetic Algorithm works for selecting optimal Fuzzy Logic network but the Fuzzy Logic modeling method may not be an appropriate approach to create the next state models.
subject
Fuzzy Logic
Gene Connections
Genetic Algorithm
Microarray Data
Modeling
Network
contributor
Liu, Yihua (author)
Allen, Edward E (committee chair)
Norris, James (committee member)
Fetrow, Jacquelyn S (committee member)
date
2011-09-08T08:36:01Z (accessioned)
2011 (issued)
degree
Mathematics (discipline)
10000-01-01 (liftdate)
embargo
10000-01-01 (terms)
identifier
http://hdl.handle.net/10339/36155 (uri)
language
en (iso)
publisher
Wake Forest University
title
Fuzzy Logic, Genetic Algorithms and Next-State Models
type
Thesis

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