Microarray gene expression data analysis using machine learning and neural networks
"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, leaf iii.
Wunsch, Donald C.
Beetner, Daryl G.
Frank, Ronald L.
Pottinger, Hardy J., 1944-
Stanley, R. Joe
Electrical and Computer Engineering
Ph. D. in Electrical Engineering
Mary K. Finley Missouri Endowment
National Science Foundation (U.S.)
University of Missouri--Rolla
Journal article titles appearing in thesis/dissertation
- Survey of clustering algorithms
viii, 184 leaves
© 2006 Rui Xu, All rights reserved.
Dissertation - Citation
Library of Congress Subject Headings
Gene expression -- Data processing
Print OCLC #
Link to Catalog Record
Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.http://laurel.lso.missouri.edu/record=b5845429~S5
Xu, Rui, "Microarray gene expression data analysis using machine learning and neural networks" (2006). Doctoral Dissertations. 1696.
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