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Journal Abstract Search

635 related items for PubMed ID: 18407922

  • 1. Divisive Correlation Clustering Algorithm (DCCA) for grouping of genes: detecting varying patterns in expression profiles.
    Bhattacharya A, De RK.
    Bioinformatics; 2008 Jun 01; 24(11):1359-66. PubMed ID: 18407922
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  • 2. Average correlation clustering algorithm (ACCA) for grouping of co-regulated genes with similar pattern of variation in their expression values.
    Bhattacharya A, De RK.
    J Biomed Inform; 2010 Aug 01; 43(4):560-8. PubMed ID: 20144735
    [Abstract] [Full Text] [Related]

  • 3. Bi-correlation clustering algorithm for determining a set of co-regulated genes.
    Bhattacharya A, De RK.
    Bioinformatics; 2009 Nov 01; 25(21):2795-801. PubMed ID: 19734153
    [Abstract] [Full Text] [Related]

  • 4. Detecting clusters of different geometrical shapes in microarray gene expression data.
    Kim DW, Lee KH, Lee D.
    Bioinformatics; 2005 May 01; 21(9):1927-34. PubMed ID: 15647300
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  • 5. Clustering of change patterns using Fourier coefficients.
    Kim J, Kim H.
    Bioinformatics; 2008 Jan 15; 24(2):184-91. PubMed ID: 18025003
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  • 7. A mixture model with random-effects components for clustering correlated gene-expression profiles.
    Ng SK, McLachlan GJ, Wang K, Ben-Tovim Jones L, Ng SW.
    Bioinformatics; 2006 Jul 15; 22(14):1745-52. PubMed ID: 16675467
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  • 9. Graph-based consensus clustering for class discovery from gene expression data.
    Yu Z, Wong HS, Wang H.
    Bioinformatics; 2007 Nov 01; 23(21):2888-96. PubMed ID: 17872912
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  • 10. Analysis of a Gibbs sampler method for model-based clustering of gene expression data.
    Joshi A, Van de Peer Y, Michoel T.
    Bioinformatics; 2008 Jan 15; 24(2):176-83. PubMed ID: 18033794
    [Abstract] [Full Text] [Related]

  • 11. Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach.
    Pihur V, Datta S, Datta S.
    Bioinformatics; 2007 Jul 01; 23(13):1607-15. PubMed ID: 17483500
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  • 12. Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions.
    Qian J, Dolled-Filhart M, Lin J, Yu H, Gerstein M.
    J Mol Biol; 2001 Dec 14; 314(5):1053-66. PubMed ID: 11743722
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  • 13. An iterative data mining approach for mining overlapping coexpression patterns in noisy gene expression data.
    Ma PC, Chan KC.
    IEEE Trans Nanobioscience; 2009 Sep 14; 8(3):252-8. PubMed ID: 19605326
    [Abstract] [Full Text] [Related]

  • 14. A multi-stage approach to clustering and imputation of gene expression profiles.
    Wong DS, Wong FK, Wood GR.
    Bioinformatics; 2007 Apr 15; 23(8):998-1005. PubMed ID: 17308340
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  • 15. A fully Bayesian model to cluster gene-expression profiles.
    Vogl C, Sanchez-Cabo F, Stocker G, Hubbard S, Wolkenhauer O, Trajanoski Z.
    Bioinformatics; 2005 Sep 01; 21 Suppl 2():ii130-6. PubMed ID: 16204092
    [Abstract] [Full Text] [Related]

  • 16. Detecting biological associations between genes based on the theory of phase synchronization.
    Kim CS, Riikonen P, Salakoski T.
    Biosystems; 2008 May 01; 92(2):99-113. PubMed ID: 18289772
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  • 17. A new algorithm for comparing and visualizing relationships between hierarchical and flat gene expression data clusterings.
    Torrente A, Kapushesky M, Brazma A.
    Bioinformatics; 2005 Nov 01; 21(21):3993-9. PubMed ID: 16141251
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