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Volumn 9, Issue , 2010, Pages 757-764

Inference of sparse networks with unobserved variables. Application to gene regulatory networks

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN METHODS; GENE REGULATORY NETWORKS; NETWORK INFERENCE; NOISE LEVELS; PARAMETER RANGE; PRINCIPLE COMPONENT ANALYSIS; SIMULATED MODEL; SPARSE NETWORK; SPARSITY CONSTRAINTS; UNDERLYING NETWORKS;

EID: 84861167247     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (5)

References (17)
  • 1
    • 0242295767 scopus 로고    scopus 로고
    • Bayesian factor regression models in the large p, small n paradigm
    • West M. (2002) Bayesian factor regression models in the large p, small n paradigm. Bayesian Stat, 7, pp. 723-732
    • (2002) Bayesian Stat , vol.7 , pp. 723-732
    • West, M.1
  • 2
    • 62549125109 scopus 로고    scopus 로고
    • High-dimensional sparse factor modelling: Applications in gene expression genomics
    • Carvalho C. et al. (2008) High-dimensional sparse factor modelling: Applications in gene expression genomics. JASA, 103, 484, pp. 1438-1456
    • (2008) JASA , vol.103 , Issue.484 , pp. 1438-1456
    • Carvalho, C.1
  • 3
    • 29144442380 scopus 로고    scopus 로고
    • Multi-way clustering of microarray data using probabilistic sparse matrix factorization
    • Dueck D., Morris Q.,Frey, B. (2005) Multi-way clustering of microarray data using probabilistic sparse matrix factorization. Bioinformatics, 21, pp. 144-151.
    • (2005) Bioinformatics , vol.21 , pp. 144-151
    • Dueck, D.1    Morris, Q.2    Frey, B.3
  • 4
    • 48849086355 scopus 로고    scopus 로고
    • Optimal solutions for sparse principal component analysis
    • d'Aspremont A, Bach F., Ghaoui L. (2007) Optimal Solutions for Sparse Principal Component Analysis. JMLR 9, pp. 1269-1294
    • (2007) JMLR , vol.9 , pp. 1269-1294
    • D'Aspremont, A.1    Bach, F.2    Ghaoui, L.3
  • 5
    • 56449124518 scopus 로고    scopus 로고
    • Expectation-maximization for sparse and non-negative PCA
    • Helsinki, Finland
    • Sigg C., Buhmann J. (2008) Expectation- Maximization for Sparse and Non-Negative PCA. ICML, Helsinki, Finland
    • (2008) ICML
    • Sigg, C.1    Buhmann, J.2
  • 6
    • 84862287828 scopus 로고    scopus 로고
    • Sparse matrix factorization for analyzing gene expression patterns
    • Srebro N. and Jaakkola T. (2001) Sparse Matrix Factorization for Analyzing Gene Expression Patterns, NIPS
    • (2001) NIPS
    • Srebro, N.1    Jaakkola, T.2
  • 7
    • 11244318119 scopus 로고    scopus 로고
    • Minreg: Inferring an active regulator set
    • Pe'er D., Regev A., and Tanay A. (2002) Minreg: Inferring an active regulator set. Bioinformatics, 18, 258-267
    • (2002) Bioinformatics , vol.18 , pp. 258-267
    • Pe'Er, D.1    Regev, A.2    Tanay, A.3
  • 9
    • 30844440076 scopus 로고    scopus 로고
    • K-SVD and its non-negative variant for dictionary design
    • Aharon M., Elad M., Bruckstein A. (2005) K-SVD and its non-negative variant for dictionary design. Wavelets XI 5914, pp. 591411-591424.
    • (2005) Wavelets XI , vol.5914 , pp. 591411-591424
    • Aharon, M.1    Elad, M.2    Bruckstein, A.3
  • 10
    • 41549101939 scopus 로고    scopus 로고
    • Model selection through sparse maximum likelihood estimation
    • Banerjee, O., Ghaoui, L. E. & d'Aspremont, A. (2007) Model selection through sparse maximum likelihood estimation JMLR, 9, pp. 485-516
    • (2007) JMLR , vol.9 , pp. 485-516
    • Banerjee, O.1    Ghaoui, L.E.2    D'Aspremont, A.3
  • 11
    • 17344381835 scopus 로고    scopus 로고
    • A variational technique for source localization based on a sparse signal reconstruction perspective
    • Orlando, Florida
    • Cetin M., Malioutov D., Willsky A. (2002) A Variational Technique for Source Localization based on a Sparse Signal Reconstruction Perspective. ICASSP, 3, pp. 2965-2968, Orlando, Florida
    • (2002) ICASSP , vol.3 , pp. 2965-2968
    • Cetin, M.1    Malioutov, D.2    Willsky, A.3
  • 12
    • 57349174008 scopus 로고    scopus 로고
    • Enhancing sparsity by reweighted ℓ1 minimization
    • Candès E., Wakin M. and Boyd S. (2007) Enhancing sparsity by reweighted ℓ1 minimization. J. Fourier Anal. Appl., 14, pp. 877-905.
    • (2007) J. Fourier Anal. Appl. , vol.14 , pp. 877-905
    • Candès, E.1    Wakin, M.2    Boyd, S.3
  • 15
    • 84865435255 scopus 로고    scopus 로고
    • Linear programming analysis of loopy belief propagation for weighted matching
    • Sanghavi S., Malioutov D., Willsky A. (2007) Linear programming analysis of loopy belief propagation for weighted matching, NIPS
    • (2007) NIPS
    • Sanghavi, S.1    Malioutov, D.2    Willsky, A.3
  • 16
    • 33645769260 scopus 로고    scopus 로고
    • An improved map of conserved regulatory sites for Saccharomyces cerevisiae
    • doi: 10.1186/1471-2105-7-113
    • MacIsaac K.D., at. al. (2006) An improved map of conserved regulatory sites for Saccharomyces cerevisiae. BMC Bioinformatics, 7:113. doi: 10.1186/1471-2105-7-113.
    • (2006) BMC Bioinformatics , vol.7 , pp. 113
    • MacIsaac, K.D.1
  • 17
    • 63149129627 scopus 로고    scopus 로고
    • Correlation signature of the macroscopic states of the gene regulatory network in cancer
    • Slavov N., Dawson KA. (2009) Correlation Signature of the Macroscopic States of the Gene Regulatory Network in Cancer, PNAS, 106, 11, pp. 4079-4084
    • (2009) PNAS , vol.106 , Issue.11 , pp. 4079-4084
    • Slavov, N.1    Dawson, K.A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.