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  • Recent Patents on Biclustering Algorithms for Gene Expression Data Analysis

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    Author(s)
    Liew, AWC
    Law, NF
    Yan, H
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2011
    Metadata
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    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 ...
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    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.
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    Journal Title
    Recent Patents on DNA & Gene Sequences
    Volume
    5
    Issue
    2
    DOI
    https://doi.org/10.2174/187221511796392097
    Copyright Statement
    © 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.
    Subject
    Pattern Recognition and Data Mining
    Biochemistry and Cell Biology
    Genetics
    Publication URI
    http://hdl.handle.net/10072/41027
    Collection
    • Journal articles

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