Recent Patents on Biclustering Algorithms for Gene Expression Data Analysis

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Title Recent Patents on Biclustering Algorithms for Gene Expression Data Analysis
Author Liew, Alan Wee-Chung; Law, Ngai-Fong; Yan, Hong
Journal Name Recent Patents on DNA & Gene Sequences
Year Published 2011
Place of publication Netherlands
Publisher Bentham Science Publishers Ltd.
Abstract In DNA microarray experiments, discovering groups of genes that share similar transcriptional characteristics is instrumental in functional annotation, tissue classification and motif identification. However, in many situations a subset of genes only exhibits a consistent pattern over a subset of conditions. Although used extensively in gene expression data analysis, conventional clustering algorithms that consider the entire row or column in an expression matrix can therefore fail to detect useful patterns in the data. Recently, biclustering has been proposed as a powerful computational tool to detect subsets of genes that exhibit consistent pattern over subsets of conditions. In this article, we review several recent patents in bicluster analysis, and in particular, highlight a recent patent from our group about a novel geometric-based biclustering method that handles the class of bicluster patterns with linear coherent variation across the row and/or column dimension. This class of bicluster patterns is of particular importance since it subsumes all constant, additive, and multiplicative bicluster patterns normally used in gene expression.
Peer Reviewed Yes
Published Yes
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Copyright Statement Copyright 2011 Bentham Science Publishers. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal website for access to the definitive, published version.
Volume 5
Issue Number 2
Page from 117
Page to 125
ISSN 1872-2156
Date Accessioned 2011-07-28
Language en_US
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Pattern Recognition and Data Mining
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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