BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

134 related articles for article (PubMed ID: 20140212)

  • 1. A top-performing algorithm for the DREAM3 gene expression prediction challenge.
    Ruan J
    PLoS One; 2010 Feb; 5(2):e8944. PubMed ID: 20140212
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Towards a rigorous assessment of systems biology models: the DREAM3 challenges.
    Prill RJ; Marbach D; Saez-Rodriguez J; Sorger PK; Alexopoulos LG; Xue X; Clarke ND; Altan-Bonnet G; Stolovitzky G
    PLoS One; 2010 Feb; 5(2):e9202. PubMed ID: 20186320
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approach.
    Meyer P; Siwo G; Zeevi D; Sharon E; Norel R; ; Segal E; Stolovitzky G
    Genome Res; 2013 Nov; 23(11):1928-37. PubMed ID: 23950146
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Gene expression prediction by soft integration and the elastic net-best performance of the DREAM3 gene expression challenge.
    Gustafsson M; Hörnquist M
    PLoS One; 2010 Feb; 5(2):e9134. PubMed ID: 20169069
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Generating realistic in silico gene networks for performance assessment of reverse engineering methods.
    Marbach D; Schaffter T; Mattiussi C; Floreano D
    J Comput Biol; 2009 Feb; 16(2):229-39. PubMed ID: 19183003
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Inferring regulatory networks from expression data using tree-based methods.
    Huynh-Thu VA; Irrthum A; Wehenkel L; Geurts P
    PLoS One; 2010 Sep; 5(9):. PubMed ID: 20927193
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. Revealing strengths and weaknesses of methods for gene network inference.
    Marbach D; Prill RJ; Schaffter T; Mattiussi C; Floreano D; Stolovitzky G
    Proc Natl Acad Sci U S A; 2010 Apr; 107(14):6286-91. PubMed ID: 20308593
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Sample scale-free gene regulatory network using gene ontology.
    Chen G; Larsen P; Almasri E; Dai Y
    Conf Proc IEEE Eng Med Biol Soc; 2006; 2006():5523-6. PubMed ID: 17946312
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Lessons from the DREAM2 Challenges.
    Stolovitzky G; Prill RJ; Califano A
    Ann N Y Acad Sci; 2009 Mar; 1158():159-95. PubMed ID: 19348640
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Combining multiple results of a reverse-engineering algorithm: application to the DREAM five-gene network challenge.
    Marbach D; Mattiussi C; Floreano D
    Ann N Y Acad Sci; 2009 Mar; 1158():102-13. PubMed ID: 19348636
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Teamwork: improved eQTL mapping using combinations of machine learning methods.
    Ackermann M; Clément-Ziza M; Michaelson JJ; Beyer A
    PLoS One; 2012; 7(7):e40916. PubMed ID: 22911718
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Optimal in silico target gene deletion through nonlinear programming for genetic engineering.
    Hong CC; Song M
    PLoS One; 2010 Feb; 5(2):e9331. PubMed ID: 20195367
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Construction of gene networks with hybrid approach from expression profile and gene ontology.
    Jing L; Ng MK; Liu Y
    IEEE Trans Inf Technol Biomed; 2010 Jan; 14(1):107-18. PubMed ID: 19789116
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Ensemble inference and inferability of gene regulatory networks.
    Ud-Dean SM; Gunawan R
    PLoS One; 2014; 9(8):e103812. PubMed ID: 25093509
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks.
    Michoel T; De Smet R; Joshi A; Van de Peer Y; Marchal K
    BMC Syst Biol; 2009 May; 3():49. PubMed ID: 19422680
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches.
    Cantone I; Marucci L; Iorio F; Ricci MA; Belcastro V; Bansal M; Santini S; di Bernardo M; di Bernardo D; Cosma MP
    Cell; 2009 Apr; 137(1):172-81. PubMed ID: 19327819
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A relative variation-based method to unraveling gene regulatory networks.
    Wang Y; Zhou T
    PLoS One; 2012; 7(2):e31194. PubMed ID: 22363578
    [TBL] [Abstract][Full Text] [Related]  

  • 19. An approach for reduction of false predictions in reverse engineering of gene regulatory networks.
    Khan A; Saha G; Pal RK
    J Theor Biol; 2018 May; 445():9-30. PubMed ID: 29462626
    [TBL] [Abstract][Full Text] [Related]  

  • 20. An ensemble learning approach to reverse-engineering transcriptional regulatory networks from time-series gene expression data.
    Ruan J; Deng Y; Perkins EJ; Zhang W
    BMC Genomics; 2009 Jul; 10 Suppl 1(Suppl 1):S8. PubMed ID: 19594885
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.