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Volumn , Issue , 2010, Pages 2512-2515

AP-based consensus clustering for gene expression time series

Author keywords

[No Author keywords available]

Indexed keywords

AFFINITY PROPAGATION; CONSENSUS CLUSTERING; DATA SETS; GENE EXPRESSION DATASETS; GENE EXPRESSION TIME SERIES; NUMBER OF CLUSTERS; PRIORI KNOWLEDGE; TIME INTERVAL; UNSUPERVISED APPROACHES;

EID: 78149491560     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2010.615     Document Type: Conference Paper
Times cited : (7)

References (12)
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  • 2
    • 36448965202 scopus 로고    scopus 로고
    • An improved algorithm for clustering gene expression data
    • DOI 10.1093/bioinformatics/btm418
    • S. Bandyopadhyay, A. Mukhopadhyay, and U. Maulik. An improved algorithm for clustering gene expression data. Bioinformatics, 23(21):2859-2865, 2007. (Pubitemid 350162891)
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    • Bandyopadhyay, S.1    Mukhopadhyay, A.2    Maulik, U.3
  • 4
    • 33847172327 scopus 로고    scopus 로고
    • Clustering by passing messages between data points
    • B. J. Frey and D. Dueck. Clustering by passing messages between data points. Science, 315(5814):972-976, 2007.
    • (2007) Science , vol.315 , Issue.5814 , pp. 972-976
    • Frey, B.J.1    Dueck, D.2
  • 6
    • 54949112147 scopus 로고    scopus 로고
    • An unsupervised conditional random fields approach for clustering gene expression time series
    • C.-T. Li, Y. Yuan, and R. Wilson. An unsupervised conditional random fields approach for clustering gene expression time series. Bioinformatics, 24(21):2467-2473, 2008.
    • (2008) Bioinformatics , vol.24 , Issue.21 , pp. 2467-2473
    • Li, C.-T.1    Yuan, Y.2    Wilson, R.3
  • 7
    • 3042686005 scopus 로고    scopus 로고
    • Bayesian mixture model based clustering of replicated microarray data
    • DOI 10.1093/bioinformatics/bth068
    • M. Medvedovic, K. Yeung, and R. Bumgarner. Bayesian mixture model based clustering of replicated microarray data. Bioinformatics, 20(8):1222-1232, 2004. (Pubitemid 38807578)
    • (2004) Bioinformatics , vol.20 , Issue.8 , pp. 1222-1232
    • Medvedovic, M.1    Yeung, K.Y.2    Bumgarner, R.E.3
  • 8
    • 0038724494 scopus 로고    scopus 로고
    • Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data
    • S. Monti, P. Tamayo, J. Mesirov, and T. Golub. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learning, 52(1-2):91-118, 2003.
    • (2003) Machine Learning , vol.52 , Issue.1-2 , pp. 91-118
    • Monti, S.1    Tamayo, P.2    Mesirov, J.3    Golub, T.4
  • 9
    • 33747890494 scopus 로고    scopus 로고
    • A mixture model with random-effects components for clustering correlated gene-expression profiles
    • S. K. Ng, G. J. McLachlan, K.Wang, L. B.-T. Jones, and S.-W. Ng. A mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics, 22(14):1745-1752, 2006.
    • (2006) Bioinformatics , vol.22 , Issue.14 , pp. 1745-1752
    • Ng, S.K.1    McLachlan, G.J.2    Wang, K.3    Jones, L.B.-T.4    Ng, S.-W.5
  • 11
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    • An approach for clustering gene expression data with error information
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  • 12
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    • Clustering gene-expression data with repeated measurements
    • K. Y. Yeung, M. Medvedovic, and R. E. Bumgarner. Clustering gene-expression data with repeated measurements. Genome Biology, 4:R34, 2003.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.