BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

188 related articles for article (PubMed ID: 21071804)

  • 1. Learning genetic regulatory network connectivity from time series data.
    Barker NA; Myers CJ; Kuwahara H
    IEEE/ACM Trans Comput Biol Bioinform; 2011; 8(1):152-65. PubMed ID: 21071804
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A hybrid Bayesian network learning method for constructing gene networks.
    Wang M; Chen Z; Cloutier S
    Comput Biol Chem; 2007 Oct; 31(5-6):361-72. PubMed ID: 17889617
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A non-homogeneous dynamic Bayesian network with sequentially coupled interaction parameters for applications in systems and synthetic biology.
    Grzegorczyk M; Husmeier D
    Stat Appl Genet Mol Biol; 2012 Jul; 11(4):. PubMed ID: 22850067
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Inferring gene networks from time series microarray data using dynamic Bayesian networks.
    Kim SY; Imoto S; Miyano S
    Brief Bioinform; 2003 Sep; 4(3):228-35. PubMed ID: 14582517
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Inference of gene regulatory networks by means of dynamic differential Bayesian networks and nonparametric regression.
    Sugimoto N; Iba H
    Genome Inform; 2004; 15(2):121-30. PubMed ID: 15706498
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data.
    Zou M; Conzen SD
    Bioinformatics; 2005 Jan; 21(1):71-9. PubMed ID: 15308537
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Gene regulatory network clustering for graph layout based on microarray gene expression data.
    Kojima K; Imoto S; Nagasaki M; Miyano S
    Genome Inform; 2010; 24():84-95. PubMed ID: 22081591
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Inferring gene regulatory networks by integrating static and dynamic data.
    Ferrazzi F; Magni P; Sacchi L; Nuzzo A; Petrovic U; Bellazzi R
    Int J Med Inform; 2007 Dec; 76 Suppl 3():S462-75. PubMed ID: 17825607
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A Bayesian network driven approach to model the transcriptional response to nitric oxide in Saccharomyces cerevisiae.
    Zhu J; Jambhekar A; Sarver A; DeRisi J
    PLoS One; 2006 Dec; 1(1):e94. PubMed ID: 17183726
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Combining sequence and time series expression data to learn transcriptional modules.
    Kundaje A; Middendorf M; Gao F; Wiggins C; Leslie C
    IEEE/ACM Trans Comput Biol Bioinform; 2005; 2(3):194-202. PubMed ID: 17044183
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Inferring gene regulatory networks using differential evolution with local search heuristics.
    Noman N; Iba H
    IEEE/ACM Trans Comput Biol Bioinform; 2007; 4(4):634-47. PubMed ID: 17975274
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A sub-space greedy search method for efficient Bayesian Network inference.
    Zhang Q; Cao Y; Li Y; Zhu Y; Sun SS; Guo D
    Comput Biol Med; 2011 Sep; 41(9):763-70. PubMed ID: 21741635
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Using Bayesian networks to analyze expression data.
    Friedman N; Linial M; Nachman I; Pe'er D
    J Comput Biol; 2000; 7(3-4):601-20. PubMed ID: 11108481
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. An effective structure learning method for constructing gene networks.
    Chen XW; Anantha G; Wang X
    Bioinformatics; 2006 Jun; 22(11):1367-74. PubMed ID: 16543279
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Estimating sparse gene regulatory networks using a bayesian linear regression.
    Sarder P; Schierding W; Cobb JP; Nehorai A
    IEEE Trans Nanobioscience; 2010 Jun; 9(2):121-31. PubMed ID: 20650703
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Hierarchical Probabilistic Interaction Modeling for Multiple Gene Expression Replicates.
    Patton KL; John DJ; Norris JL; Lewis DR; Muday GK
    IEEE/ACM Trans Comput Biol Bioinform; 2014; 11(2):336-46. PubMed ID: 26355781
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A Maximum A Posteriori Probability and Time-Varying Approach for Inferring Gene Regulatory Networks from Time Course Gene Microarray Data.
    Chan SC; Zhang L; Wu HC; Tsui KM
    IEEE/ACM Trans Comput Biol Bioinform; 2015; 12(1):123-35. PubMed ID: 26357083
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Data-based model and parameter evaluation in dynamic transcriptional regulatory networks.
    Cavelier G; Anastassiou D
    Proteins; 2004 May; 55(2):339-50. PubMed ID: 15048826
    [TBL] [Abstract][Full Text] [Related]  

  • 20. TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach.
    Zoppoli P; Morganella S; Ceccarelli M
    BMC Bioinformatics; 2010 Mar; 11():154. PubMed ID: 20338053
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.