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Publication Type
Book Chapter
UWI Author(s)
Author, Analytic
Linder, Roland; Richards, Tereza; Wagner, Mathias
Author Affiliation, Ana.
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Title, Analytic
Microarray data classified by artifial neural networks
Medium Designator
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Connective Phrase
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Author, Monographic
Rampal, Jang B.
Author Role
Editor
Title, Monographic
Microarrays volume 2: Applications and data analysis
Reprint Status
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Edition
2nd
Place of Publication
Totowa, N.J.
Publisher Name
Humana Press
Date of Publication
2007
Volume ID
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Issue ID
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Page(s)
345-372
Series Editor
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Series Editor Role
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Series Title
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Series Volume Identification
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Series Issue Identification
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Connective Phrase
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Location/URL
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Notes
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Abstract
Systems biology has enjoyed explosive growth in both the number of people participating in this area of research and the number of publications on the topic. The field of systems biology encompasses the in silico analysis og high-throughput data as provided by DNA or protein microarrays. Along with the increasing availability of microarray data, attention is focused on methods of analyzing the expression rates. One important type of analysis is the classification task, for example, distinguishing different types of cell functions or tumors. Recently, interest has been awakened toward artificial neural networks (ANN), which have many appealing characteristics such as an exceptional degree of accuracy. Nonlinear relationships or independence from certain assumptions regarding the data distribution are also considered. The current works reviews advantages as well as disadvantages of neural networks in the context of microarray analysis. Comparisons are drawn to alternative methods. Selected solutions are discussed, and finally algorithms for the effective combination of multiple ANNs are presented. The development of approaches to use ANN-processed microarray data applicable to run cell and tissue simulations may be slated for future investigation.....
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