Issue No. 02 - March/April (2012 vol. 9)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.53
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.
Gene expression, Clustering algorithms, Bioinformatics, Search problems, Computational biology, Algorithm design and analysis, Optics
Qinghua Huang, Dacheng Tao, Xuelong Li and A. Liew, "Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 2, pp. 560-570, 2012.