Title

Reliability Assessment of Microarray Data Using Fuzzy Classification Methods: A Comparative Study

Document Type

Article

Publication Date

2011

Publication Title

Communications in Computer and Information Science 2011

Volume

190

First page number:

351

Last page number:

360

Abstract

Microarrays have become the tool of choice for the global analysis of gene expression. Powerful data acquisition systems are now available to produce massive amounts of genetic data. However, the resultant data consists of thousands of points that are error-prone, which in turn results in erroneous biological conclusions. In this paper, a comparative study of the performance of fuzzy clustering algorithms i.e. Fuzzy C-Means, Fuzzy C-medoid, Gustafson and Kessel, Gath Geva classification, Fuzzy Possibilistic C-Means and Kernel based Fuzzy C-Means is carried out to separate microarray data into reliable and unreliable signal intensity populations. The performance criteria used in the evaluation of the classification algorithm deal with reliability, complexity and agreement rate with that of Normal Mixture Modeling. It is shown that Kernel Fuzzy C-Means classification algorithms appear to be highly sensitive to the selection of the values of the kernel parameters.

Keywords

Classification--Computer programs; Computer algorithms; DNA microarrays; Fuzzy algorithms

Disciplines

Computer Engineering | Electrical and Computer Engineering | Engineering | Signal Processing | Systems and Communications

Language

English

Permissions

Use Find in Your Library, contact the author, or interlibrary loan to garner a copy of the item. Publisher policy does not allow archiving the final published version. If a post-print (author's peer-reviewed manuscript) is allowed and available, or publisher policy changes, the item will be deposited.

Identifier

DOI: http://dx.doi.org/10.1007/978-3-642-22709-7_36

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