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Journal Abstract Search

262 related items for PubMed ID: 15838143

  • 21. Identifying projected clusters from gene expression profiles.
    Yip KY, Cheung DW, Ng MK, Cheung KH.
    J Biomed Inform; 2004 Oct; 37(5):345-57. PubMed ID: 15488748
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  • 22. A memetic-aided approach to hierarchical clustering from distance matrices: application to gene expression clustering and phylogeny.
    Cotta C, Moscato P.
    Biosystems; 2003 Nov; 72(1-2):75-97. PubMed ID: 14642660
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  • 23. An improved algorithm for clustering gene expression data.
    Bandyopadhyay S, Mukhopadhyay A, Maulik U.
    Bioinformatics; 2007 Nov 01; 23(21):2859-65. PubMed ID: 17720981
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  • 25. Efficiently finding regulatory elements using correlation with gene expression.
    Bannai H, Inenaga S, Shinohara A, Takeda M, Miyano S.
    J Bioinform Comput Biol; 2004 Jun 01; 2(2):273-88. PubMed ID: 15297982
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  • 28. Subdimension-based similarity measure for DNA microarray data clustering.
    Lam BS, Yan H.
    Phys Rev E Stat Nonlin Soft Matter Phys; 2006 Oct 01; 74(4 Pt 1):041906. PubMed ID: 17155095
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  • 30. Overcoming confounded controls in the analysis of gene expression data from microarray experiments.
    Bhattacharya S, Long D, Lyons-Weiler J.
    Appl Bioinformatics; 2003 Oct 01; 2(4):197-208. PubMed ID: 15130791
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  • 32. Maximum significance clustering of oligonucleotide microarrays.
    de Ridder D, Staal FJ, van Dongen JJ, Reinders MJ.
    Bioinformatics; 2006 Feb 01; 22(3):326-31. PubMed ID: 16303800
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  • 35. Incorporating biological knowledge into distance-based clustering analysis of microarray gene expression data.
    Huang D, Pan W.
    Bioinformatics; 2006 May 15; 22(10):1259-68. PubMed ID: 16500932
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  • 38. An iterative data mining approach for mining overlapping coexpression patterns in noisy gene expression data.
    Ma PC, Chan KC.
    IEEE Trans Nanobioscience; 2009 Sep 15; 8(3):252-8. PubMed ID: 19605326
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