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Journal Abstract Search

211 related items for PubMed ID: 15998909

  • 1. Assessing the limits of genomic data integration for predicting protein networks.
    Lu LJ, Xia Y, Paccanaro A, Yu H, Gerstein M.
    Genome Res; 2005 Jul; 15(7):945-53. PubMed ID: 15998909
    [Abstract] [Full Text] [Related]

  • 2. Heterogeneous data integration by tree-augmented naïve Bayes for protein-protein interactions prediction.
    Lin X, Chen XW.
    Proteomics; 2013 Jan; 13(2):261-8. PubMed ID: 23112070
    [Abstract] [Full Text] [Related]

  • 3. Assessment and statistical modeling of the relationship between remotely sensed aerosol optical depth and PM2.5 in the eastern United States.
    Paciorek CJ, Liu Y, HEI Health Review Committee.
    Res Rep Health Eff Inst; 2012 May; (167):5-83; discussion 85-91. PubMed ID: 22838153
    [Abstract] [Full Text] [Related]

  • 4. A knowledge-driven probabilistic framework for the prediction of protein-protein interaction networks.
    Browne F, Wang H, Zheng H, Azuaje F.
    Comput Biol Med; 2010 Mar; 40(3):306-17. PubMed ID: 20138613
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  • 5. Probabilistic prediction and ranking of human protein-protein interactions.
    Scott MS, Barton GJ.
    BMC Bioinformatics; 2007 Jul 05; 8():239. PubMed ID: 17615067
    [Abstract] [Full Text] [Related]

  • 6. De novo prediction of RNA-protein interactions from sequence information.
    Wang Y, Chen X, Liu ZP, Huang Q, Wang Y, Xu D, Zhang XS, Chen R, Chen L.
    Mol Biosyst; 2013 Jan 27; 9(1):133-42. PubMed ID: 23138266
    [Abstract] [Full Text] [Related]

  • 7. Supervised enzyme network inference from the integration of genomic data and chemical information.
    Yamanishi Y, Vert JP, Kanehisa M.
    Bioinformatics; 2005 Jun 27; 21 Suppl 1():i468-77. PubMed ID: 15961492
    [Abstract] [Full Text] [Related]

  • 8. Kernel-based data fusion improves the drug-protein interaction prediction.
    Wang YC, Zhang CH, Deng NY, Wang Y.
    Comput Biol Chem; 2011 Dec 14; 35(6):353-62. PubMed ID: 22099632
    [Abstract] [Full Text] [Related]

  • 9. Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers.
    Regnier-Coudert O, McCall J, Lothian R, Lam T, McClinton S, N'dow J.
    Artif Intell Med; 2012 May 14; 55(1):25-35. PubMed ID: 22206941
    [Abstract] [Full Text] [Related]

  • 10. Predicting co-complexed protein pairs using genomic and proteomic data integration.
    Zhang LV, Wong SL, King OD, Roth FP.
    BMC Bioinformatics; 2004 Apr 16; 5():38. PubMed ID: 15090078
    [Abstract] [Full Text] [Related]

  • 11. Discovering biological networks from diverse functional genomic data.
    Myers CL, Chiriac C, Troyanskaya OG.
    Methods Mol Biol; 2009 Apr 16; 563():157-75. PubMed ID: 19597785
    [Abstract] [Full Text] [Related]

  • 12. Kernel methods for predicting protein-protein interactions.
    Ben-Hur A, Noble WS.
    Bioinformatics; 2005 Jun 16; 21 Suppl 1():i38-46. PubMed ID: 15961482
    [Abstract] [Full Text] [Related]

  • 13. Using machine learning techniques and genomic/proteomic information from known databases for defining relevant features for PPI classification.
    Urquiza JM, Rojas I, Pomares H, Herrera J, Florido JP, Valenzuela O, Cepero M.
    Comput Biol Med; 2012 Jun 16; 42(6):639-50. PubMed ID: 22575173
    [Abstract] [Full Text] [Related]

  • 14. Semi-supervised multi-label collective classification ensemble for functional genomics.
    Wu Q, Ye Y, Ho SS, Zhou S.
    BMC Genomics; 2014 Jun 16; 15 Suppl 9(Suppl 9):S17. PubMed ID: 25521242
    [Abstract] [Full Text] [Related]

  • 15. Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps.
    Nabieva E, Jim K, Agarwal A, Chazelle B, Singh M.
    Bioinformatics; 2005 Jun 16; 21 Suppl 1():i302-10. PubMed ID: 15961472
    [Abstract] [Full Text] [Related]

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  • 19. Gene expression trends and protein features effectively complement each other in gene function prediction.
    Wabnik K, Hvidsten TR, Kedzierska A, Van Leene J, De Jaeger G, Beemster GT, Komorowski J, Kuiper MT.
    Bioinformatics; 2009 Feb 01; 25(3):322-30. PubMed ID: 19050035
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