A new geometric biclustering algorithm based on the Hough transform for analysis of large-scale microarray data

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Title A new geometric biclustering algorithm based on the Hough transform for analysis of large-scale microarray data
Author Zhao, Hongya; Liew, Alan Wee-Chung; Xie, Xudong; Yan, Hong
Journal Name Journal of Theoretical Biology
Year Published 2008
Place of publication United Kingdom
Publisher Academic Press
Abstract Biclustering is an important tool in microarray analysis when only a subset of genes co-regulates in a subset of conditions. Different from standard clustering analyses, biclustering performs simultaneous classification in both gene and condition directions in a microarray data matrix. However, the biclustering problem is inherently intractable and computationally complex. In this paper, we present a new biclustering algorithm based on the geometrical viewpoint of coherent gene expression profiles. In this method, we perform pattern identification based on the Hough transform in a column-pair space. The algorithm is especially suitable for the biclustering analysis of large-scale microarray data. Our studies show that the approach can discover significant biclusters with respect to the increased noise level and regulatory complexity. Furthermore, we also test the ability of our method to locate biologically verifiable biclusters within an annotated set of genes.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1016/j.jtbi.2007.11.030
Copyright Statement Copyright 2008 Elsevier Ltd. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
Volume 251
Issue Number 2
Page from 264
Page to 274
ISSN 0022-5193
Date Accessioned 2008-05-16
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Gene Expression; PRE2009-Pattern Recognition
URI http://hdl.handle.net/10072/22590
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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