A novel biclustering with parallel genetic algorithm

A novel biclustering algorithm is proposed in this paper, which can be used to cluster gene expression data. One of the contributions of this paper is a novel and effective residue function of the biclustering algorithm. Furthermore, the parallel genetic algorithm is firstly used to the algorithm of the biclustering for gene expression data. This method can avoid local convergence in the optimal algorithm mostly. The Yeast Saccharomyces cerevisiae cell cycle gene expression profiles from the Spellman's data to bicluster are used to test the performance of new algorithm. And we compared our algorithm with traditional genetic algorithm in biclustering. The results reveal that novel proposed algorithms could discover the interesting patterns in the gene expression profiles.

[1]  George M. Church,et al.  Biclustering of Expression Data , 2000, ISMB.

[2]  Yuanqing Li,et al.  A linear discriminant analysis method based on mutual information maximization , 2011, Pattern Recognit..

[3]  Eckart Zitzler,et al.  BicAT: a biclustering analysis toolbox , 2006, Bioinform..

[4]  Mohsen Ebrahimi Moghaddam,et al.  An Image Enhancement Method Based on Genetic Algorithm , 2009, 2009 International Conference on Digital Image Processing.

[5]  Arlindo L. Oliveira,et al.  Biclustering algorithms for biological data analysis: a survey , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[6]  Lonnie R. Welch,et al.  AGRIS: the Arabidopsis Gene Regulatory Information Server, an update , 2010, Nucleic Acids Res..

[7]  Julio C. Facelli,et al.  A parallel genetic algorithm to discover patterns in genetic markers that indicate predisposition to multifactorial disease , 2008, Comput. Biol. Medicine.

[8]  Guoji Zhang,et al.  A Hybrid PSO/GA Algorithm for Job Shop Scheduling Problem , 2010, ICSI.

[9]  Wan Baocheng,et al.  The Implementation of Parallel Genetic Algorithm Based on MATLAB , 2007, APPT.

[10]  H. Pang,et al.  The interaction between aggrecan gene VNTR polymorphism and cigarette smoking in predicting incident symptomatic intervertebral disc degeneration , 2010, Connective tissue research.

[11]  D. di Bernardo,et al.  How to infer gene networks from expression profiles , 2007, Molecular systems biology.

[12]  Richard M. Karp,et al.  Discovering local structure in gene expression data: the order-preserving submatrix problem , 2002, RECOMB '02.

[13]  Alok J. Saldanha,et al.  Java Treeview - extensible visualization of microarray data , 2004, Bioinform..

[14]  Jun S Liu,et al.  Bayesian biclustering of gene expression data , 2008, BMC Genomics.

[15]  Philip S. Yu,et al.  WF-MSB: A weighted fuzzy-based biclustering method for gene expression data , 2011, Int. J. Data Min. Bioinform..

[16]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[17]  Mohsen Ebrahimi Moghaddam,et al.  An image contrast enhancement method based on genetic algorithm , 2010, Pattern Recognit. Lett..

[18]  Samuel Granjeaud,et al.  TranscriptomeBrowser: A Powerful and Flexible Toolbox to Explore Productively the Transcriptional Landscape of the Gene Expression Omnibus Database , 2008, PloS one.

[19]  Dimitris Bertsimas,et al.  Robust optimization with simulated annealing , 2010, J. Glob. Optim..