These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

130 related articles for article (PubMed ID: 23962479)

  • 1. A comparative study of statistical methods used to identify dependencies between gene expression signals.
    de Siqueira Santos S; Takahashi DY; Nakata A; Fujita A
    Brief Bioinform; 2014 Nov; 15(6):906-18. PubMed ID: 23962479
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Comparing Pearson, Spearman and Hoeffding's D measure for gene expression association analysis.
    Fujita A; Sato JR; Demasi MA; Sogayar MC; Ferreira CE; Miyano S
    J Bioinform Comput Biol; 2009 Aug; 7(4):663-84. PubMed ID: 19634197
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Gene interaction networks based on kernel correlation metrics.
    Cheng L; Khorasani K; Ding Y; Guo X
    Int J Comput Biol Drug Des; 2013; 6(1-2):72-92. PubMed ID: 23428475
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Cross-platform comparison and visualisation of gene expression data using co-inertia analysis.
    Culhane AC; Perrière G; Higgins DG
    BMC Bioinformatics; 2003 Nov; 4():59. PubMed ID: 14633289
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A GMM-IG framework for selecting genes as expression panel biomarkers.
    Wang M; Chen JY
    Artif Intell Med; 2010; 48(2-3):75-82. PubMed ID: 20004087
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Identification of partially linear structure in additive models with an application to gene expression prediction from sequences.
    Lian H; Chen X; Yang JY
    Biometrics; 2012 Jun; 68(2):437-45. PubMed ID: 21950383
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Testing the significance of a correlation with nonnormal data: comparison of Pearson, Spearman, transformation, and resampling approaches.
    Bishara AJ; Hittner JB
    Psychol Methods; 2012 Sep; 17(3):399-417. PubMed ID: 22563845
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Smoothing gene expression data with network information improves consistency of regulated genes.
    Dørum G; Snipen L; Solheim M; Saebo S
    Stat Appl Genet Mol Biol; 2011 Aug; 10(1):. PubMed ID: 23089828
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Reconstruction of large-scale gene regulatory networks using Bayesian model averaging.
    Kim H; Gelenbe E
    IEEE Trans Nanobioscience; 2012 Sep; 11(3):259-65. PubMed ID: 22987132
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Sparse time series chain graphical models for reconstructing genetic networks.
    Abegaz F; Wit E
    Biostatistics; 2013 Jul; 14(3):586-99. PubMed ID: 23462022
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A tutorial to identify nonlinear associations in gene expression time series data.
    Fujita A; Miyano S
    Methods Mol Biol; 2014; 1164():87-95. PubMed ID: 24927837
    [TBL] [Abstract][Full Text] [Related]  

  • 12. KNOWLEDGE-ASSISTED APPROACH TO IDENTIFY PATHWAYS WITH DIFFERENTIAL DEPENDENCIES.
    Speyer G; Kiefer J; Dhruv H; Berens M; Kim S
    Pac Symp Biocomput; 2016; 21():33-44. PubMed ID: 26776171
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Fast calculation of pairwise mutual information for gene regulatory network reconstruction.
    Qiu P; Gentles AJ; Plevritis SK
    Comput Methods Programs Biomed; 2009 May; 94(2):177-80. PubMed ID: 19167129
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Identifying drug active pathways from gene networks estimated by gene expression data.
    Tamada Y; Imoto S; Tashiro K; Kuhara S; Miyano S
    Genome Inform; 2005; 16(1):182-91. PubMed ID: 16362921
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Ranking genes by their co-expression to subsets of pathway members.
    Adler P; Peterson H; Agius P; Reimand J; Vilo J
    Ann N Y Acad Sci; 2009 Mar; 1158():1-13. PubMed ID: 19348627
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Weighted lasso in graphical Gaussian modeling for large gene network estimation based on microarray data.
    Shimamura T; Imoto S; Yamaguchi R; Miyano S
    Genome Inform; 2007; 19():142-53. PubMed ID: 18546512
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Gene expression complex networks: synthesis, identification, and analysis.
    Lopes FM; Cesar RM; Costa Lda F
    J Comput Biol; 2011 Oct; 18(10):1353-67. PubMed ID: 21548810
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Correlation between gene expression and clinical data through linear and nonlinear principal components analyses: muscular dystrophies as case studies.
    Romualdi C; Giuliani A; Millino C; Celegato B; Benigni R; Lanfranchi G
    OMICS; 2009 Jun; 13(3):173-84. PubMed ID: 19405797
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A new multiple regression approach for the construction of genetic regulatory networks.
    Zhang SQ; Ching WK; Tsing NK; Leung HY; Guo D
    Artif Intell Med; 2010; 48(2-3):153-60. PubMed ID: 19963359
    [TBL] [Abstract][Full Text] [Related]  

  • 20. An empirical Bayesian method for estimating biological networks from temporal microarray data.
    Rau A; Jaffrézic F; Foulley JL; Doerge RW
    Stat Appl Genet Mol Biol; 2010; 9():Article 9. PubMed ID: 20196759
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.