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Journal Abstract Search

149 related items for PubMed ID: 23825550

  • 1. Complementing the Eukaryotic Protein Interactome.
    Pesch R, Zimmer R.
    PLoS One; 2013; 8(6):e66635. PubMed ID: 23825550
    [Abstract] [Full Text] [Related]

  • 2. Mycobacterium tuberculosis and Clostridium difficille interactomes: demonstration of rapid development of computational system for bacterial interactome prediction.
    Ananthasubramanian S, Metri R, Khetan A, Gupta A, Handen A, Chandra N, Ganapathiraju M.
    Microb Inform Exp; 2012 Mar 21; 2():4. PubMed ID: 22587966
    [Abstract] [Full Text] [Related]

  • 3. Information flow analysis of interactome networks.
    Missiuro PV, Liu K, Zou L, Ross BC, Zhao G, Liu JS, Ge H.
    PLoS Comput Biol; 2009 Apr 21; 5(4):e1000350. PubMed ID: 19503817
    [Abstract] [Full Text] [Related]

  • 4. Predicting whole genome protein interaction networks from primary sequence data in model and non-model organisms using ENTS.
    Rodgers-Melnick E, Culp M, DiFazio SP.
    BMC Genomics; 2013 Sep 10; 14():608. PubMed ID: 24015873
    [Abstract] [Full Text] [Related]

  • 5. New insights into protein-protein interaction data lead to increased estimates of the S. cerevisiae interactome size.
    Sambourg L, Thierry-Mieg N.
    BMC Bioinformatics; 2010 Dec 21; 11():605. PubMed ID: 21176124
    [Abstract] [Full Text] [Related]

  • 6. Gene function finding through cross-organism ensemble learning.
    Moro G, Masseroli M.
    BioData Min; 2021 Feb 12; 14(1):14. PubMed ID: 33579334
    [Abstract] [Full Text] [Related]

  • 7. Protein function prediction using neighbor relativity in protein-protein interaction network.
    Moosavi S, Rahgozar M, Rahimi A.
    Comput Biol Chem; 2013 Apr 12; 43():11-6. PubMed ID: 23314240
    [Abstract] [Full Text] [Related]

  • 8. Assessment of high-confidence protein-protein interactome in yeast.
    Karagoz K, Arga KY.
    Comput Biol Chem; 2013 Aug 12; 45():1-8. PubMed ID: 23608186
    [Abstract] [Full Text] [Related]

  • 9. Cross-organism learning method to discover new gene functionalities.
    Domeniconi G, Masseroli M, Moro G, Pinoli P.
    Comput Methods Programs Biomed; 2016 Apr 12; 126():20-34. PubMed ID: 26724853
    [Abstract] [Full Text] [Related]

  • 10. A New Method for Predicting Protein Functions From Dynamic Weighted Interactome Networks.
    Zhao B, Wang J, Li M, Li X, Li Y, Wu FX, Pan Y.
    IEEE Trans Nanobioscience; 2016 Mar 12; 15(2):131-9. PubMed ID: 26955047
    [Abstract] [Full Text] [Related]

  • 11. Protein-protein Interaction Networks of E. coli and S. cerevisiae are similar.
    Wuchty S, Uetz P.
    Sci Rep; 2014 Nov 28; 4():7187. PubMed ID: 25431098
    [Abstract] [Full Text] [Related]

  • 12. Cluster-based assessment of protein-protein interaction confidence.
    Kamburov A, Grossmann A, Herwig R, Stelzl U.
    BMC Bioinformatics; 2012 Oct 10; 13():262. PubMed ID: 23050565
    [Abstract] [Full Text] [Related]

  • 13. A kinetic model of the evolution of a protein interaction network.
    Pawlowski PH, Kaczanowski S, Zielenkiewicz P.
    BMC Genomics; 2013 Mar 14; 14():172. PubMed ID: 23497092
    [Abstract] [Full Text] [Related]

  • 14. Reconstituting protein interaction networks using parameter-dependent domain-domain interactions.
    Memišević V, Wallqvist A, Reifman J.
    BMC Bioinformatics; 2013 May 07; 14():154. PubMed ID: 23651452
    [Abstract] [Full Text] [Related]

  • 15. Prediction of evolutionarily conserved interologs in Mus musculus.
    Yellaboina S, Dudekula DB, Ko MSh.
    BMC Genomics; 2008 Oct 08; 9():465. PubMed ID: 18842131
    [Abstract] [Full Text] [Related]

  • 16. Discover protein complexes in protein-protein interaction networks using parametric local modularity.
    Kim J, Tan K.
    BMC Bioinformatics; 2010 Oct 19; 11():521. PubMed ID: 20958996
    [Abstract] [Full Text] [Related]

  • 17. Filtering high-throughput protein-protein interaction data using a combination of genomic features.
    Patil A, Nakamura H.
    BMC Bioinformatics; 2005 Apr 18; 6():100. PubMed ID: 15833142
    [Abstract] [Full Text] [Related]

  • 18. Noise reduction in protein-protein interaction graphs by the implementation of a novel weighting scheme.
    Kritikos GD, Moschopoulos C, Vazirgiannis M, Kossida S.
    BMC Bioinformatics; 2011 Jun 16; 12():239. PubMed ID: 21679454
    [Abstract] [Full Text] [Related]

  • 19. Assessing coverage of protein interaction data using capture-recapture models.
    Kelly WP, Stumpf MP.
    Bull Math Biol; 2012 Feb 16; 74(2):356-74. PubMed ID: 21870201
    [Abstract] [Full Text] [Related]

  • 20. A new method for predicting essential proteins based on dynamic network topology and complex information.
    Luo J, Kuang L.
    Comput Biol Chem; 2014 Oct 16; 52():34-42. PubMed ID: 25179858
    [Abstract] [Full Text] [Related]

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