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Journal Abstract Search

90 related items for PubMed ID: 19209703

  • 41. Protein Complexes Prediction Method Based on Core-Attachment Structure and Functional Annotations.
    Li B, Liao B.
    Int J Mol Sci; 2017 Sep 06; 18(9):. PubMed ID: 28878201
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  • 42. Identification of Essential Proteins Based on a New Combination of Local Interaction Density and Protein Complexes.
    Luo J, Qi Y.
    PLoS One; 2015 Sep 06; 10(6):e0131418. PubMed ID: 26125187
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  • 43. A unified representation of multiprotein complex data for modeling interaction networks.
    Ding C, He X, Meraz RF, Holbrook SR.
    Proteins; 2004 Oct 01; 57(1):99-108. PubMed ID: 15326596
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  • 44. A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks.
    Wang J, Zheng W, Qian Y, Liang J.
    Molecules; 2017 Dec 08; 22(12):. PubMed ID: 29292776
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  • 45. Are scale-free networks robust to measurement errors?
    Lin N, Zhao H.
    BMC Bioinformatics; 2005 May 16; 6():119. PubMed ID: 15904487
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  • 46. Identification of hierarchical and overlapping functional modules in PPI networks.
    Wang J, Ren J, Li M, Wu FX.
    IEEE Trans Nanobioscience; 2012 Dec 16; 11(4):386-93. PubMed ID: 22955967
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  • 47. Comparing protein interaction networks via a graph match-and-split algorithm.
    Narayanan M, Karp RM.
    J Comput Biol; 2007 Sep 16; 14(7):892-907. PubMed ID: 17803369
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  • 48. From Function to Interaction: A New Paradigm for Accurately Predicting Protein Complexes Based on Protein-to-Protein Interaction Networks.
    Xu B, Guan J.
    IEEE/ACM Trans Comput Biol Bioinform; 2014 Sep 16; 11(4):616-27. PubMed ID: 26356332
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  • 49. Structure discovery in PPI networks using pattern-based network decomposition.
    Bachman P, Liu Y.
    Bioinformatics; 2009 Jul 15; 25(14):1814-21. PubMed ID: 19447784
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  • 50. A method based on local density and random walks for complexes detection in protein interaction networks.
    Yu L, Gao L, Li K.
    J Bioinform Comput Biol; 2010 Dec 15; 8 Suppl 1():47-62. PubMed ID: 21155019
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  • 51. Detecting protein complexes in a PPI network: a gene ontology based multi-objective evolutionary approach.
    Mukhopadhyay A, Ray S, De M.
    Mol Biosyst; 2012 Nov 15; 8(11):3036-48. PubMed ID: 22990765
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  • 52. A hybrid graph-theoretic method for mining overlapping functional modules in large sparse protein interaction networks.
    Zhang S, Liu HW, Ning XM, Zhang XS.
    Int J Data Min Bioinform; 2009 Nov 15; 3(1):68-84. PubMed ID: 19432377
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  • 53. A density-based clustering approach for identifying overlapping protein complexes with functional preferences.
    Hu L, Chan KC.
    BMC Bioinformatics; 2015 May 27; 16():174. PubMed ID: 26013799
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  • 54. Fitting a geometric graph to a protein-protein interaction network.
    Higham DJ, Rasajski M, Przulj N.
    Bioinformatics; 2008 Apr 15; 24(8):1093-9. PubMed ID: 18344248
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  • 55. Identifying protein complex by integrating characteristic of core-attachment into dynamic PPI network.
    Shen X, Yi L, Jiang X, He T, Yang J, Xie W, Hu P, Hu X.
    PLoS One; 2017 Apr 15; 12(10):e0186134. PubMed ID: 29045465
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  • 56. Employing functional interactions for characterisation and detection of sparse complexes from yeast PPI networks.
    Srihari S, Leong HW.
    Int J Bioinform Res Appl; 2012 Apr 15; 8(3-4):286-304. PubMed ID: 22961456
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  • 57. Peeling the yeast protein network.
    Wuchty S, Almaas E.
    Proteomics; 2005 Feb 15; 5(2):444-9. PubMed ID: 15627958
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  • 58. A Central Edge Selection Based Overlapping Community Detection Algorithm for the Detection of Overlapping Structures in Protein⁻Protein Interaction Networks.
    Zhang F, Ma A, Wang Z, Ma Q, Liu B, Huang L, Wang Y.
    Molecules; 2018 Oct 13; 23(10):. PubMed ID: 30322177
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  • 59. Detecting functional modules in the yeast protein-protein interaction network.
    Chen J, Yuan B.
    Bioinformatics; 2006 Sep 15; 22(18):2283-90. PubMed ID: 16837529
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  • 60. Sampling strategy for protein complex prediction using cluster size frequency.
    Tatsuke D, Maruyama O.
    Gene; 2013 Apr 10; 518(1):152-8. PubMed ID: 23235119
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