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Journal Abstract Search

304 related items for PubMed ID: 15073019

  • 1. Probabilistic representation of gene regulatory networks.
    Mao L, Resat H.
    Bioinformatics; 2004 Sep 22; 20(14):2258-69. PubMed ID: 15073019
    [Abstract] [Full Text] [Related]

  • 2. Advances to Bayesian network inference for generating causal networks from observational biological data.
    Yu J, Smith VA, Wang PP, Hartemink AJ, Jarvis ED.
    Bioinformatics; 2004 Dec 12; 20(18):3594-603. PubMed ID: 15284094
    [Abstract] [Full Text] [Related]

  • 3. A method for estimating stochastic noise in large genetic regulatory networks.
    Orrell D, Ramsey S, de Atauri P, Bolouri H.
    Bioinformatics; 2005 Jan 15; 21(2):208-17. PubMed ID: 15319259
    [Abstract] [Full Text] [Related]

  • 4. Using a state-space model with hidden variables to infer transcription factor activities.
    Li Z, Shaw SM, Yedwabnick MJ, Chan C.
    Bioinformatics; 2006 Mar 15; 22(6):747-54. PubMed ID: 16403793
    [Abstract] [Full Text] [Related]

  • 5. On the attenuation and amplification of molecular noise in genetic regulatory networks.
    Chen BS, Wang YC.
    BMC Bioinformatics; 2006 Feb 02; 7():52. PubMed ID: 16457708
    [Abstract] [Full Text] [Related]

  • 6. Superiority of network motifs over optimal networks and an application to the revelation of gene network evolution.
    Ott S, Hansen A, Kim SY, Miyano S.
    Bioinformatics; 2005 Jan 15; 21(2):227-38. PubMed ID: 15377501
    [Abstract] [Full Text] [Related]

  • 7. Inferring gene regulatory networks from multiple microarray datasets.
    Wang Y, Joshi T, Zhang XS, Xu D, Chen L.
    Bioinformatics; 2006 Oct 01; 22(19):2413-20. PubMed ID: 16864593
    [Abstract] [Full Text] [Related]

  • 8. Inferring gene regulatory networks from time series data using the minimum description length principle.
    Zhao W, Serpedin E, Dougherty ER.
    Bioinformatics; 2006 Sep 01; 22(17):2129-35. PubMed ID: 16845143
    [Abstract] [Full Text] [Related]

  • 9. Learning regulatory programs that accurately predict differential expression with MEDUSA.
    Kundaje A, Lianoglou S, Li X, Quigley D, Arias M, Wiggins CH, Zhang L, Leslie C.
    Ann N Y Acad Sci; 2007 Dec 01; 1115():178-202. PubMed ID: 17934055
    [Abstract] [Full Text] [Related]

  • 10. Ensemble learning of genetic networks from time-series expression data.
    Nam D, Yoon SH, Kim JF.
    Bioinformatics; 2007 Dec 01; 23(23):3225-31. PubMed ID: 17977884
    [Abstract] [Full Text] [Related]

  • 11. Gene network inference from incomplete expression data: transcriptional control of hematopoietic commitment.
    Missal K, Cross MA, Drasdo D.
    Bioinformatics; 2006 Mar 15; 22(6):731-8. PubMed ID: 16332705
    [Abstract] [Full Text] [Related]

  • 12. Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks.
    Werhli AV, Grzegorczyk M, Husmeier D.
    Bioinformatics; 2006 Oct 15; 22(20):2523-31. PubMed ID: 16844710
    [Abstract] [Full Text] [Related]

  • 13. Predicting genetic regulatory response using classification.
    Middendorf M, Kundaje A, Wiggins C, Freund Y, Leslie C.
    Bioinformatics; 2004 Aug 04; 20 Suppl 1():i232-40. PubMed ID: 15262804
    [Abstract] [Full Text] [Related]

  • 14. Bayesian sparse hidden components analysis for transcription regulation networks.
    Sabatti C, James GM.
    Bioinformatics; 2006 Mar 15; 22(6):739-46. PubMed ID: 16368767
    [Abstract] [Full Text] [Related]

  • 15. Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models.
    Hirose O, Yoshida R, Imoto S, Yamaguchi R, Higuchi T, Charnock-Jones DS, Print C, Miyano S.
    Bioinformatics; 2008 Apr 01; 24(7):932-42. PubMed ID: 18292116
    [Abstract] [Full Text] [Related]

  • 16. A Bayesian approach to reconstructing genetic regulatory networks with hidden factors.
    Beal MJ, Falciani F, Ghahramani Z, Rangel C, Wild DL.
    Bioinformatics; 2005 Feb 01; 21(3):349-56. PubMed ID: 15353451
    [Abstract] [Full Text] [Related]

  • 17. Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method.
    Fujita A, Sato JR, Garay-Malpartida HM, Morettin PA, Sogayar MC, Ferreira CE.
    Bioinformatics; 2007 Jul 01; 23(13):1623-30. PubMed ID: 17463021
    [Abstract] [Full Text] [Related]

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  • 20. Local modeling of global interactome networks.
    Scholtens D, Vidal M, Gentleman R.
    Bioinformatics; 2005 Sep 01; 21(17):3548-57. PubMed ID: 15998662
    [Abstract] [Full Text] [Related]

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