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    Parallelized evolutionary learning for detection of biclusters in gene expression data

    Huang, Q. and Tao, D. and Li, Xuelong and Liew, A. (2012) Parallelized evolutionary learning for detection of biclusters in gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 9 (2), pp. 560-570. ISSN 1545-5963.

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    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.


    Item Type: Article
    Keyword(s) / Subject(s): 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
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 07 Jun 2013 08:55
    Last Modified: 11 Oct 2016 15:27


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