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Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data
March/April 2012 (vol. 9 no. 2)
pp. 560-570
ASCII Text
x 
 
Qinghua Huang, Dacheng Tao, Xuelong Li, A. Liew, "Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 2, pp. 560-570, March/April, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2011.53,
author = { Qinghua Huang and Dacheng Tao and Xuelong Li and A. Liew},
title = {Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {9},
number = {2},
issn = {1545-5963},
year = {2012},
pages = {560-570},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.53},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data
IS - 2
SN - 1545-5963
SP560
EP570
EPD - 560-570
A1 - Qinghua Huang,
A1 - Dacheng Tao,
A1 - Xuelong Li,
A1 - A. Liew,
PY - 2012
KW - learning (artificial intelligence)
KW - biology computing
KW - evolutionary computation
KW - genetics
KW - genomics
KW - additive biclusters
KW - parallelized evolutionary learning
KW - biclusters detection
KW - gene expression data
KW - microarray experiments
KW - Gene expression
KW - Clustering algorithms
KW - Bioinformatics
KW - Search problems
KW - Computational biology
KW - Algorithm design and analysis
KW - Optics
KW - gene expression data analysis.
KW - Biclustering
KW - genetic learning
KW - subdimensional search strategy
VL - 9
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
Qinghua Huang, Sch. of Electron. & Inf. Eng., South China Univ. of Technol., Guangzhou, China
Dacheng Tao, Centre for Quantum Comput. & Intell. Syst., Univ. of Technol., Sydney, NSW, Australia
Xuelong Li, State Key Lab. of Transient Opt. & Photonics, Xi'an Inst. of Opt. & Precision Mech., Xi'an, China
A. Liew, Sch. of Inf. & Commun. Technol., Griffith Univ., Griffith, QLD, Australia
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.

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Index Terms:
learning (artificial intelligence),biology computing,evolutionary computation,genetics,genomics,additive biclusters,parallelized evolutionary learning,biclusters detection,gene expression data,microarray experiments,Gene expression,Clustering algorithms,Bioinformatics,Search problems,Computational biology,Algorithm design and analysis,Optics,gene expression data analysis.,Biclustering,genetic learning,subdimensional search strategy
Citation:
Qinghua Huang, Dacheng Tao, Xuelong Li, A. Liew, "Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 2, pp. 560-570, March-April 2012, doi:10.1109/TCBB.2011.53
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