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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 1/2013

01-02-2013 | Original Article

Enhancing gene expression clustering analysis using tangent transformation

Author: Xin Xu

Published in: International Journal of Machine Learning and Cybernetics | Issue 1/2013

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Abstract

Even though extensive work has been done on clustering gene expression data, none existing algorithms evaluates gene expression coherence simultaneously by both regulation direction and relative proportion. As an example, density-based algorithms group genes with similar expression levels together and may separate genes whose expression levels have a large difference in value but vary in a fixed proportion relative to one another. In order to simultaneously measure profile coherence in regulation proportion as well as regulation direction, we propose a novel tangent transformation method. Experimental results indicate that our tangent transformation method has enhanced the gene expression clustering results significantly. Our tangent transformation method can be flexibly applied for either global clustering or biclustering, in either unsupervised or supervised scenario.
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Metadata
Title
Enhancing gene expression clustering analysis using tangent transformation
Author
Xin Xu
Publication date
01-02-2013
Publisher
Springer-Verlag
Published in
International Journal of Machine Learning and Cybernetics / Issue 1/2013
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-012-0069-9

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