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Volumn 26, Issue 21, 2010, Pages 2744-2751

Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data

Author keywords

[No Author keywords available]

Indexed keywords

PROTEIN;

EID: 77958498250     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btq510     Document Type: Article
Times cited : (216)

References (38)
  • 1
    • 12344323594 scopus 로고    scopus 로고
    • Conserved network motifs allow protein-protein interaction prediction
    • Albert,I. and Albert,R. (2004) Conserved network motifs allow protein-protein interaction prediction. Bioinformatics, 20, 3346-3352.
    • (2004) Bioinformatics , vol.20 , pp. 3346-3352
    • Albert, I.1    Albert, R.2
  • 2
    • 0347473809 scopus 로고    scopus 로고
    • Gaining condence in high-throughput protein interaction networks
    • Bader,J.S. et al. (2004) Gaining condence in high-throughput protein interaction networks. Nat. Biotechnol., 22, 78-85.
    • (2004) Nat. Biotechnol. , vol.22 , pp. 78-85
    • Bader, J.S.1
  • 3
    • 0042378381 scopus 로고    scopus 로고
    • Laplacian eigenmaps for dimensionality reduction and data representation
    • Belkin,M. and Niyogi,P. (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput., 15, 1373-1396.
    • (2003) Neural Comput. , vol.15 , pp. 1373-1396
    • Belkin, M.1    Niyogi, P.2
  • 4
    • 1442329655 scopus 로고    scopus 로고
    • Functional classication of proteins for the prediction of cellular function from a protein-protein interaction network
    • Brun,C. et al. (2003) Functional classication of proteins for the prediction of cellular function from a protein-protein interaction network. Genome Biol., 5,R6.
    • (2003) Genome Biol. , vol.5
    • Brun, C.1
  • 5
    • 24144466886 scopus 로고    scopus 로고
    • Discovering reliable protein interactions from high-throughput experimental data using network topology
    • Chen,J. et al. (2005) Discovering reliable protein interactions from high-throughput experimental data using network topology. Artif. Intel. Med., 35, 37-47.
    • (2005) Artif. Intel. Med. , vol.35 , pp. 37-47
    • Chen, J.1
  • 6
    • 34250188800 scopus 로고    scopus 로고
    • Increasing condence of protein-protein interactomes
    • Chen,J. et al. (2006) Increasing condence of protein-protein interactomes. Genome Inform., 17, 284-297.
    • (2006) Genome Inform. , vol.17 , pp. 284-297
    • Chen, J.1
  • 7
    • 33745619564 scopus 로고    scopus 로고
    • Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions
    • Chua,H.N. et al. (2006) Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Bioinformatics, 22, 1623-1630.
    • (2006) Bioinformatics , vol.22 , pp. 1623-1630
    • Chua, H.N.1
  • 8
    • 48149084438 scopus 로고    scopus 로고
    • Increasing the reliability of protein interactomes
    • Chua,H.N. andWong,L. (2008) Increasing the reliability of protein interactomes. Drug Discov. Today, 13, 652-658.
    • (2008) Drug Discov. Today , vol.13 , pp. 652-658
    • Chua, H.N.1    Wong, L.2
  • 9
    • 61949235526 scopus 로고    scopus 로고
    • Dense graphlet statistics of protein interaction and random networks
    • Colak,R. et al. (2009) Dense graphlet statistics of protein interaction and random networks. Pac. Symp. Biocomput., 178-189.
    • (2009) Pac. Symp. Biocomput. , pp. 178-189
    • Colak, R.1
  • 10
    • 34147121646 scopus 로고    scopus 로고
    • Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae
    • Collins,S.R. et al. (2007) Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae. Mol. Cell Proteom., 6, 439-450.
    • (2007) Mol. Cell Proteom. , vol.6 , pp. 439-450
    • Collins, S.R.1
  • 11
    • 0742304254 scopus 로고    scopus 로고
    • Prediction of protein function using protein-protein interaction data
    • Deng,M.H. et al. (2003) Prediction of protein function using protein-protein interaction data. J. Comput. Biol., 10, 947-960.
    • (2003) J. Comput. Biol. , vol.10 , pp. 947-960
    • Deng, M.H.1
  • 12
    • 0037948870 scopus 로고    scopus 로고
    • Hessian eigenmaps: locally linear embedding techniques for high-dimensional data
    • Donoho,D.L. and Grimes,C. (2003) Hessian eigenmaps: locally linear embedding techniques for high-dimensional data. Proc. Natl Acad. Sci. USA, 100, 5591-5596.
    • (2003) Proc. Natl Acad. Sci. USA , vol.100 , pp. 5591-5596
    • Donoho, D.L.1    Grimes, C.2
  • 13
    • 77958506065 scopus 로고    scopus 로고
    • Bridging structural biology and genomics: assessing protein interaction data with known complexes
    • Edwards,A.M. et al. (2004) Bridging structural biology and genomics: assessing protein interaction data with known complexes. Drug Discov. Today, 9, S32-S40.
    • (2004) Drug Discov. Today , vol.9
    • Edwards, A.M.1
  • 15
    • 33644555054 scopus 로고    scopus 로고
    • Proteome survey revealsmodularity of the yeast cellmachinery
    • Gavin,A.C. et al. (2006) Proteome survey revealsmodularity of the yeast cellmachinery, Nature, 440, 631-636.
    • (2006) Nature , vol.440 , pp. 631-636
    • Gavin, A.C.1
  • 16
    • 34547787599 scopus 로고    scopus 로고
    • A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality
    • Hart,G.T. et al. (2007) A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality. BMC Bioinformatics, 8, 236.
    • (2007) BMC Bioinformatics , vol.8 , pp. 236
    • Hart, G.T.1
  • 17
    • 41949139082 scopus 로고    scopus 로고
    • Fitting a geometric graph to a protein-protein interaction network
    • Higham,D.J. et al. (2008) Fitting a geometric graph to a protein-protein interaction network. Bioinformatics, 24, 1093-1099.
    • (2008) Bioinformatics , vol.24 , pp. 1093-1099
    • Higham, D.J.1
  • 18
    • 33645453254 scopus 로고    scopus 로고
    • Global landscape of protein complexes in the yeast Saccharomyces cerevisiae
    • Krogan,N.J. et al. (2006) Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature, 440, 637-643.
    • (2006) Nature , vol.440 , pp. 637-643
    • Krogan, N.J.1
  • 19
    • 31544473466 scopus 로고    scopus 로고
    • Incremental nonlinear dimensionality reduction by manifold learning
    • Law,M.H.C. and Jain,A.K. (2006) Incremental nonlinear dimensionality reduction by manifold learning. IEEE T Pattern Anal., 28, 377-391.
    • (2006) IEEE T Pattern Anal. , vol.28 , pp. 377-391
    • Law, M.H.C.1    Jain, A.K.2
  • 20
    • 46749130932 scopus 로고    scopus 로고
    • PRINCESS, a protein interaction condence evaluation system with multiple data sources
    • Li,D. et al. (2008) PRINCESS, a protein interaction condence evaluation system with multiple data sources. Mol. Cell Proteom., 7, 1043-1052.
    • (2008) Mol. Cell Proteom. , vol.7 , pp. 1043-1052
    • Li, D.1
  • 21
    • 33745854283 scopus 로고    scopus 로고
    • Riemannian manifold learning for nonlinear dimensionality reduction
    • Lin,T. et al. (2006) Riemannian manifold learning for nonlinear dimensionality reduction. Comput. Vision Eccv, Pt 1, Proc., 3951, 44-55.
    • (2006) Comput. Vision Eccv, Pt 1, Proc. , vol.3951 , pp. 44-55
    • Lin, T.1
  • 22
    • 67650742114 scopus 로고    scopus 로고
    • Assessing and predicting protein interactions using both local and global network topological metrics
    • Liu,G.M. et al. (2008) Assessing and predicting protein interactions using both local and global network topological metrics. Genome Inform. Ser., 21, 138-149.
    • (2008) Genome Inform. Ser. , vol.21 , pp. 138-149
    • Liu, G.M.1
  • 23
    • 0034628487 scopus 로고    scopus 로고
    • Guilt-by-association goes global
    • Oliver,S. (2000) Guilt-by-association goes global. Nature, 403, 601-603.
    • (2000) Nature , vol.403 , pp. 601-603
    • Oliver, S.1
  • 24
    • 24044434446 scopus 로고    scopus 로고
    • Filtering high-throughput protein-protein interaction data using a combination of genomic features
    • Patil,A. and Nakamura,H. (2005) Filtering high-throughput protein-protein interaction data using a combination of genomic features. BMC Bioinformatics, 6,100.
    • (2005) BMC Bioinformatics , vol.6 , pp. 100
    • Patil, A.1    Nakamura, H.2
  • 25
    • 33846672214 scopus 로고    scopus 로고
    • Biological network comparison using graphlet degree distribution
    • Przulj,N. (2007) Biological network comparison using graphlet degree distribution. Bioinformatics, 23, E177-E183.
    • (2007) Bioinformatics , vol.23
    • Przulj, N.1
  • 26
    • 12344273375 scopus 로고    scopus 로고
    • Modeling interactome: scale-free or geometric?
    • Przulj,N. et al. (2004) Modeling interactome: scale-free or geometric? Bioinformatics, 20, 3508-3515.
    • (2004) Bioinformatics , vol.20 , pp. 3508-3515
    • Przulj, N.1
  • 27
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • Roweis,S.T. and Saul,L.K. (2000) Nonlinear dimensionality reduction by locally linear embedding. Science, 290, 2323.
    • (2000) Science , vol.290 , pp. 2323
    • Roweis, S.T.1    Saul, L.K.2
  • 28
    • 27144530248 scopus 로고    scopus 로고
    • Towards a proteome-scale map of the human protein-protein interaction network
    • Rual,J.F. et al. (2005) Towards a proteome-scale map of the human protein-protein interaction network. Nature, 437, 1173-1178.
    • (2005) Nature , vol.437 , pp. 1173-1178
    • Rual, J.F.1
  • 29
    • 0242500972 scopus 로고    scopus 로고
    • Construction of reliable protein-protein interaction networks with a new interaction generality measure
    • Saito,R. et al. (2003) Construction of reliable protein-protein interaction networks with a new interaction generality measure. Bioinformatics, 19, 756-763.
    • (2003) Bioinformatics , vol.19 , pp. 756-763
    • Saito, R.1
  • 30
    • 2342517502 scopus 로고    scopus 로고
    • Think globally t locally: unsupervised learning of low dimensional manifolds
    • Saul,L.K. and Roweis,S.T. (2004) Think globally, t locally: unsupervised learning of low dimensional manifolds. J. Mach. Learn. Res., 4, 119-155.
    • (2004) J. Mach. Learn. Res. , vol.4 , pp. 119-155
    • Saul, L.K.1    Roweis, S.T.2
  • 31
    • 33947252154 scopus 로고    scopus 로고
    • Network-based prediction of protein function
    • Sharan,R. et al. (2007) Network-based prediction of protein function.Mol. Syst. Biol., 3, 88.
    • (2007) Mol. Syst. Biol. , vol.3 , pp. 88
    • Sharan, R.1
  • 32
    • 0037432528 scopus 로고    scopus 로고
    • How reliable are experimental protein-protein interaction data?
    • Sprinzak,E. et al. (2003) How reliable are experimental protein-protein interaction data? J. Mol. Biol., 327, 919-923.
    • (2003) J. Mol. Biol. , vol.327 , pp. 919-923
    • Sprinzak, E.1
  • 33
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • Tenenbaum,J.B. et al. (2000) A global geometric framework for nonlinear dimensionality reduction. Science, 290, 2319.
    • (2000) Science , vol.290 , pp. 2319
    • Tenenbaum, J.B.1
  • 34
    • 4444378917 scopus 로고    scopus 로고
    • Biochemical characterization of protein complexes from the Helicobacter pylori protein interaction map - strategies for complex formation and evidence for novel interactions within type IV secretion systems
    • Terradot,L. et al. (2004) Biochemical characterization of protein complexes from the Helicobacter pylori protein interaction map - strategies for complex formation and evidence for novel interactions within type IV secretion systems. Mol. Cell Proteom., 3, 809-819.
    • (2004) Mol. Cell Proteom. , vol.3 , pp. 809-819
    • Terradot, L.1
  • 35
    • 0036601150 scopus 로고    scopus 로고
    • Computational methods for the prediction of protein interactions
    • Valencia,A. and Pazos,F. (2002) Computational methods for the prediction of protein interactions. Curr. Opin. Struc. Biol., 12, 368-373.
    • (2002) Curr. Opin. Struc. Biol. , vol.12 , pp. 368-373
    • Valencia, A.1    Pazos, F.2
  • 36
    • 33744949513 scopus 로고    scopus 로고
    • Unsupervised learning of image manifolds by semidenite programming
    • Weinberger,K.Q. and Saul,L.K. (2006) Unsupervised learning of image manifolds by semidenite programming. Int. J. Comput. Vision, 70, 77-90.
    • (2006) Int. J. Comput. Vision , vol.70 , pp. 77-90
    • Weinberger, K.Q.1    Saul, L.K.2
  • 37
    • 75749099276 scopus 로고    scopus 로고
    • Protein interactome analysis for countering pathogen drug resistance
    • Wong,L.S. and Liu,G.M. (2010) Protein interactome analysis for countering pathogen drug resistance. J. Comput. Sci. Technol., 25, 124-130.
    • (2010) J. Comput. Sci. Technol. , vol.25 , pp. 124-130
    • Wong, L.S.1    Liu, G.M.2
  • 38
    • 68549104123 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction with local spline embedding
    • Xiang,S.M. et al. (2009) Nonlinear dimensionality reduction with local spline embedding. IEEE T Knowl. Data En., 21, 1285-1298.
    • (2009) IEEE T Knowl. Data En. , vol.21 , pp. 1285-1298
    • Xiang, S.M.1


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