Microarray gene expression data analysis using machine learning and neural networks

Author

Rui Xu, Missouri University of Science and TechnologyFollow

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 microarrays
Gene expression -- Data processing

Thesis Number

T 9003

Print OCLC #

123442890

Link to Catalog Record

Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.

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