Biclustering Analysis for Pattern Discovery: Current Techniques, Comparative Studies and Applications

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Title Biclustering Analysis for Pattern Discovery: Current Techniques, Comparative Studies and Applications
Author Zhao, Hongya; Liew, Alan Wee-Chung; Wang, Doris Z.; Yan, Hong
Journal Name Current Bioinformatics
Year Published 2012
Place of publication Netherlands
Publisher Bentham Science
Abstract Biclustering analysis is a useful methodology to discover the local coherent patterns hidden in a data matrix. Unlike the traditional clustering procedure, which searches for groups of coherent patterns using the entire feature set, biclustering performs simultaneous pattern classification in both row and column directions in a data matrix. The technique has found useful applications in many fields but notably in bioinformatics. In this paper, we give an overview of the biclustering problem and review some existing biclustering algorithms in terms of their underlying methodology, search strategy, detected bicluster patterns, and validation strategies. Moreover, we show that geometry of biclustering patterns can be used to solve biclustering problems effectively. Well-known methods in signal and image analysis, such as the Hough transform and relaxation labeling, can be employed to detect the geometrical biclustering patterns. We present performance evaluation results for several of the well known biclustering algorithms, on both artificial and real gene expression datasets. Finally, several interesting applications of biclustering are discussed.
Peer Reviewed Yes
Published Yes
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Copyright Statement Copyright 2012 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 7
Issue Number 1
Page from 43
Page to 55
ISSN 1574-8936
Date Accessioned 2012-06-20
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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