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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • Hongya Zhao
  • Alan Wee-Chung Liew
  • Xudong Xie
  • Hong Yan

Related Research Unit(s)


Original languageEnglish
Pages (from-to)264-274
Journal / PublicationJournal of Theoretical Biology
Issue number2
Publication statusPublished - 21 Mar 2008


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. © 2007 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Biclustering, Gene expression profiles, Microarray data analysis, The Hough transform