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BMC Bioinformatics. 2012; 13(Suppl 7): S11.
Published online May 8, 2012. doi:  10.1186/1471-2105-13-S7-S11
PMCID: PMC3348021
Pattern-driven neighborhood search for biclustering of microarray data
Wassim Ayadi,1,2 Mourad Elloumi,2 and Jin-Kao Haocorresponding author1
1LERIA, Université d'Angers, 2 Boulevard Lavoisier, 49045 Angers Cedex 01, France
2LaTICE, Higher School of Sciences and Technologies of Tunis, 5 Avenue Taha Hussein, B. P. : 56, Bab Menara, 1008 Tunis, University of Tunis, Tunisia
corresponding authorCorresponding author.
Wassim Ayadi: ayadi/at/info.univ-angers.fr; Mourad Elloumi: mourad.elloumi/at/fsegt.rnu.tn; Jin-Kao Hao: hao/at/info.univ-angers.fr
Supplement
Advanced intelligent computing theories and their applications in bioinformatics. Proceedings of the 2011 International Conference on Intelligent Computing (ICIC 2011)
M Michael Gromiha and De-Shuang Huang
The conference and publication charges were partly funded by grants from the National Science Foundation of China Nos. 61133010, and 31071168.
Conference
The 2011 International Conference on Intelligent Computing (ICIC 2011)
11-14 August 2011
Zhengzhou, China
Abstract
Background
Biclustering aims at finding subgroups of genes that show highly correlated behaviors across a subgroup of conditions. Biclustering is a very useful tool for mining microarray data and has various practical applications. From a computational point of view, biclustering is a highly combinatorial search problem and can be solved with optimization methods.
Results
We describe a stochastic pattern-driven neighborhood search algorithm for the biclustering problem. Starting from an initial bicluster, the proposed method improves progressively the quality of the bicluster by adjusting some genes and conditions. The adjustments are based on the quality of each gene and condition with respect to the bicluster and the initial data matrix. The performance of the method was evaluated on two well-known microarray datasets (Yeast cell cycle and Saccharomyces cerevisiae), showing that it is able to obtain statistically and biologically significant biclusters. The proposed method was also compared with six reference methods from the literature.
Conclusions
The proposed method is computationally fast and can be applied to discover significant biclusters. It can also used to effectively improve the quality of existing biclusters provided by other biclustering methods.
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