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Journal Abstract Search

184 related items for PubMed ID: 26267476

  • 1. Progeny Clustering: A Method to Identify Biological Phenotypes.
    Hu CW, Kornblau SM, Slater JH, Qutub AA.
    Sci Rep; 2015 Aug 12; 5():12894. PubMed ID: 26267476
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  • 3. Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters.
    Tellaroli P, Bazzi M, Donato M, Brazzale AR, Drăghici S.
    PLoS One; 2016 Aug 12; 11(3):e0152333. PubMed ID: 27015427
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  • 4. Clustering microarray gene expression data using weighted Chinese restaurant process.
    Qin ZS.
    Bioinformatics; 2006 Aug 15; 22(16):1988-97. PubMed ID: 16766561
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  • 5. Simultaneous clustering of gene expression data with clinical chemistry and pathological evaluations reveals phenotypic prototypes.
    Bushel PR, Wolfinger RD, Gibson G.
    BMC Syst Biol; 2007 Feb 23; 1():15. PubMed ID: 17408499
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  • 6. Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens.
    Yin Z, Zhou X, Bakal C, Li F, Sun Y, Perrimon N, Wong ST.
    BMC Bioinformatics; 2008 Jun 05; 9():264. PubMed ID: 18534020
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  • 7. Visualization and evaluation of clusters for exploratory analysis of gene expression data.
    Kim JH, Kohane IS, Ohno-Machado L.
    J Biomed Inform; 2002 Feb 05; 35(1):25-36. PubMed ID: 12415724
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  • 10. Dynamic association rules for gene expression data analysis.
    Chen SC, Tsai TH, Chung CH, Li WH.
    BMC Genomics; 2015 Oct 14; 16():786. PubMed ID: 26467206
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  • 13. 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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  • 17. Shrinkage Clustering: a fast and size-constrained clustering algorithm for biomedical applications.
    Hu CW, Li H, Qutub AA.
    BMC Bioinformatics; 2018 Jan 23; 19(1):19. PubMed ID: 29361928
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