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    J Theor Biol. 2008 Mar 21;251(2):264-74. Epub 2007 Dec 4.

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

    Source

    Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong. hyzhao@ee.cityu.edu.hk

    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.

    PMID:
    18199458
    [PubMed - indexed for MEDLINE]

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