Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data

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Title Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data
Author Huang, Qinghua; Tao, Dacheng; Li, Xuelong; Liew, Alan Wee-Chung
Journal Name IEEE - ACM Transactions on Computational Biology and Bioinformatics
Year Published 2012
Place of publication United States
Publisher IEEE/ACM
Abstract The analysis of gene expression data obtained from microarray experiments is important for discovering the biological process of genes. Biclustering algorithms have been proven to be able to group the genes with similar expression patterns under a number of experimental conditions. In this paper, we propose a new biclustering algorithm based on evolutionary learning. By converting the biclustering problem into a common clustering problem, the algorithm can be applied in a search space constructed by the conditions. To further reduce the size of the search space, we randomly separate the full conditions into a number of condition subsets (subspaces), each of which has a smaller number of conditions. The algorithm is applied to each subspace and is able to discover bicluster seeds within a limited computing time. Finally, an expanding and merging procedure is employed to combine the bicluster seeds into larger biclusters according to a homogeneity criterion. We test the performance of the proposed algorithm using synthetic and real microarray data sets. Compared with several previously developed biclustering algorithms, our algorithm demonstrates a significant improvement in discovering additive biclusters.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1109/TCBB.2011.53
Volume 9
Issue Number 2
Page from 560
Page to 570
ISSN 1545-5963
Date Accessioned 2012-06-20
Date Available 2013-06-03T04:55:51Z
Language en_US
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Neural, Evolutionary and Fuzzy Computation; Pattern Recognition and Data Mining
URI http://hdl.handle.net/10072/47637
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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