Doctoral Dissertations
Microarray gene expression data analysis using machine learning and neural networks
Abstract
"As an important experimental technology, DNA microarray provides an effective way to measure the expression levels of tens of thousands of genes simultaneously under different conditions, which makes it possible to investigate the gene activities of the whole genome. However, computational challenges have to be faced as a result of the large volume of generated data. In this dissertation, two important applications of microarray data, i.e., genetic regulatory networks inference and cancer classification, are addressed with machine learning and neural networks"--Abstract, page iii.
Advisor(s)
Wunsch, Donald C.
Committee Member(s)
Beetner, Daryl G.
Frank, Ronald L.
Pottinger, Hardy J., 1944-
Stanley, R. Joe
Department(s)
Electrical and Computer Engineering
Degree Name
Ph. D. in Electrical Engineering
Sponsor(s)
Mary K. Finley Missouri Endowment
National Science Foundation (U.S.)
Publisher
University of Missouri--Rolla
Publication Date
Spring 2006
Journal article titles appearing in thesis/dissertation
- Survey of clustering algorithms
Pagination
viii, 184 pages
Note about bibliography
Includes bibliographical references (pages 170-183).
Rights
© 2006 Rui Xu, All rights reserved.
Document Type
Dissertation - Citation
File Type
text
Language
English
Subject Headings
DNA microarraysGene expression -- Data processing
Thesis Number
T 9003
Print OCLC #
123442890
Recommended Citation
Xu, Rui, "Microarray gene expression data analysis using machine learning and neural networks" (2006). Doctoral Dissertations. 1696.
https://scholarsmine.mst.edu/doctoral_dissertations/1696
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