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Volumn 9, Issue 1, 2010, Pages

Granger causality analysis of human cell-cycle gene expression profiles

Author keywords

functional relationships; gene expression; Granger causality

Indexed keywords

ARTICLE; BIVARIATE ANALYSIS; CELL CYCLE; GENE EXPRESSION PROFILING; GENETIC VARIABILITY; GRANGER CAUSALITY ANALYSIS; HUMAN; STATISTICAL ANALYSIS; STATISTICAL SIGNIFICANCE; DNA MICROARRAY; GENE; METHODOLOGY;

EID: 77956412225     PISSN: None     EISSN: 15446115     Source Type: Journal    
DOI: 10.2202/1544-6115.1555     Document Type: Article
Times cited : (15)

References (31)
  • 1
    • 0742305866 scopus 로고    scopus 로고
    • Network biology: Understanding the cell's functional organization
    • Barabasi, A-L. and Oltvai, Z.N. (2004) Network biology: understanding the cell's functional organization. Nature Reviews Genetics 5, 101-113.
    • (2004) Nature Reviews Genetics , vol.5 , pp. 101-113
    • Barabasi, A.-L.1    Oltvai, Z.N.2
  • 2
    • 17144456808 scopus 로고    scopus 로고
    • Positive feedback in eukaryotic gene networks: Cell differentiation by graded to binary response conversion
    • Becksei, A et al. (2001) Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion. EMBO Journal 20, 2528-2535
    • (2001) EMBO Journal , vol.20 , pp. 2528-2535
    • Becksei, A.1
  • 3
    • 0001677717 scopus 로고
    • Controlling the false discovery rate: A practical and powerful approach to multiple testing
    • Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57, 289-300.
    • (1995) Journal of the Royal Statistical Society Series B , vol.57 , pp. 289-300
    • Benjamini, Y.1    Hochberg, Y.2
  • 4
    • 3042795832 scopus 로고    scopus 로고
    • Beta oscillations in a large-scale sensorimotor cortical network: Directional influences revealed by Granger causality
    • Brovelli, A. et al. (2004). Beta oscillations in a large-scale sensorimotor cortical network: Directional influences revealed by Granger causality. Proc. Nat. Acad. Sci. (USA) 101, 9849-9854.
    • (2004) Proc. Nat. Acad. Sci. (USA) , vol.101 , pp. 9849-9854
    • Brovelli, A.1
  • 5
    • 0037119587 scopus 로고    scopus 로고
    • Stochastic Gene Expression in a Single Cell
    • Elowitz, M.B. et al. (2002) Stochastic Gene Expression in a Single Cell. Science, 297(5584), 1183-6.
    • (2002) Science , vol.297 , Issue.5584 , pp. 1183-6
    • Elowitz, M.B.1
  • 6
    • 0842288337 scopus 로고    scopus 로고
    • Inferring Cellular Networks Using Probabilistic Graphical Models
    • Friedman, N. (2004). Inferring Cellular Networks Using Probabilistic Graphical Models, Science 303, 799-805.
    • (2004) Science , vol.303 , pp. 799-805
    • Friedman, N.1
  • 7
    • 33750247101 scopus 로고    scopus 로고
    • Linking Stochastic Dynamics to Population Distribution: An Analytical Framework of Gene Expression
    • Friedman, et al. (2006) Linking Stochastic Dynamics to Population Distribution: An Analytical Framework of Gene Expression. Phys. Rev. Lett. 97, 168302.
    • (2006) Phys. Rev. Lett. , vol.97 , pp. 168302
    • Friedman1
  • 8
    • 34547852213 scopus 로고    scopus 로고
    • Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method
    • Fujita et al. (2007a) Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method. Bioinformatics 23(13), 1623-1630.
    • (2007) Bioinformatics , vol.23 , Issue.13 , pp. 1623-1630
    • Fujita1
  • 9
    • 35748964479 scopus 로고    scopus 로고
    • Modeling gene expression regulatory networks with the sparse vector autoregressive model
    • Fujita et al. (2007b) Modeling gene expression regulatory networks with the sparse vector autoregressive model. BMC SystBiol.1, 39.
    • (2007) BMC SystBiol.1 , vol.39
    • Fujita1
  • 10
    • 54949116637 scopus 로고    scopus 로고
    • Modeling Nonlinear Gene Regulatory Networks from Time Series Gene Expression Data
    • Fujita et al. (2008) Modeling Nonlinear Gene Regulatory Networks from Time Series Gene Expression Data. J. Bioinformatics and Computational Biology 6(5), 961-979.
    • (2008) J. Bioinformatics and Computational Biology , vol.6 , Issue.5 , pp. 961-979
    • Fujita1
  • 11
    • 0038048325 scopus 로고    scopus 로고
    • Inferring genetic networks and identifying compound mode of action via expression profiling
    • Gardner, T.S. et al. (2003). Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301, 102-105.
    • (2003) Science , vol.301 , pp. 102-105
    • Gardner, T.S.1
  • 12
    • 0000351727 scopus 로고
    • Investigating Causal Relations by Econometric Models and Cross-spectral Methods
    • Granger, C.W.J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica 37(3), 424-438.
    • (1969) Econometrica , vol.37 , Issue.3 , pp. 424-438
    • Granger, C.W.J.1
  • 13
    • 66349096611 scopus 로고    scopus 로고
    • High-Frequency, Long-Range Coupling between Prefrontal and Visual Cortex during Attention
    • Gregoriou, G.G. et al. (2009) High-Frequency, Long-Range Coupling Between Prefrontal and Visual Cortex During Attention. Science 324, 1207-1210.
    • (2009) Science , vol.324 , pp. 1207-1210
    • Gregoriou, G.G.1
  • 14
    • 44949262939 scopus 로고    scopus 로고
    • Uncovering Interactions in the Frequency Domain
    • Guo, S. et al. (2008). Uncovering Interactions in the Frequency Domain. PLoS Comput. Biol. 4(5): e1000087.
    • (2008) PLoS Comput. Biol. , vol.4 , Issue.5
    • Guo, S.1
  • 16
    • 19544379881 scopus 로고    scopus 로고
    • Stochasticity in gene expression: From theories to phenotypes
    • Kaern, M. et al. (2005) Stochasticity in gene expression: from theories to phenotypes. Nature Reviews Genetics. 6, 451-464.
    • (2005) Nature Reviews Genetics , vol.6 , pp. 451-464
    • Kaern, M.1
  • 17
    • 0036500993 scopus 로고    scopus 로고
    • Systems Biology: A Brief Overview
    • Kitano, H. (2002). Systems Biology: A Brief Overview. Science 295, 1662-1664.
    • (2002) Science , vol.295 , pp. 1662-1664
    • Kitano, H.1
  • 18
    • 66349115724 scopus 로고    scopus 로고
    • Grouped graphical Granger modeling for gene expression regulatory networks discovery
    • Lozano et al. (2009) Grouped graphical Granger modeling for gene expression regulatory networks discovery, Bioinformatics 25, i110-i118.
    • (2009) Bioinformatics , vol.25
    • Lozano1
  • 20
    • 0037174670 scopus 로고    scopus 로고
    • Network Motifs: Simple Building Blocks of Complex Networks
    • Milo, R. et al. (2002) Network Motifs: Simple Building Blocks of Complex Networks. Science 298, 824-827.
    • (2002) Science , vol.298 , pp. 824-827
    • Milo, R.1
  • 21
    • 33847348163 scopus 로고    scopus 로고
    • Causality and pathway search in microarray time series experiment
    • Mukhopadhyay, N.D. and Chatterjee, S. (2007) Causality and pathway search in microarray time series experiment. Bioinformatics. 23(4), 442-9.
    • (2007) Bioinformatics , vol.23 , Issue.4 , pp. 442-9
    • Mukhopadhyay, N.D.1    Chatterjee, S.2
  • 22
    • 41349085815 scopus 로고    scopus 로고
    • Comment on causality and pathway search in microarray time series experiment
    • Nagarajan, R. and Upreti, M. (2008) Comment on causality and pathway search in microarray time series experiment. Bioinformatics, 24(7), 1029-1032.
    • (2008) Bioinformatics , vol.24 , Issue.7 , pp. 1029-1032
    • Nagarajan, R.1    Upreti, M.2
  • 23
    • 65849504774 scopus 로고    scopus 로고
    • A note on inferring acyclic network structures using Granger causality tests
    • Nagarajan, R. (2009) A note on inferring acyclic network structures using Granger causality tests. Int. J. Biostatistics 5, 1.
    • (2009) Int. J. Biostatistics , vol.5 , pp. 1
    • Nagarajan, R.1
  • 24
    • 59549099144 scopus 로고    scopus 로고
    • Power-Law Signatures and Patchiness in Genechip Oligonucleotide Microarrays
    • Springer-Verlag
    • Nagarajan, R. (2009) Power-Law Signatures and Patchiness in Genechip Oligonucleotide Microarrays. Studies in Computational Intelligence, Springer-Verlag, 359-377.
    • (2009) Studies in Computational Intelligence , pp. 359-377
    • Nagarajan, R.1
  • 25
    • 33746361200 scopus 로고    scopus 로고
    • Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations
    • Okoniewski MJ, Miller CJ (2006) Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations. BMC Bioinformatics 7, 276.
    • (2006) BMC Bioinformatics , vol.7 , pp. 276
    • Okoniewski, M.J.1    Miller, C.J.2
  • 26
    • 33746093509 scopus 로고    scopus 로고
    • Theories and measures of consciousness: An extended framework
    • Seth ,et al.
    • Seth et al. (2006) Theories and measures of consciousness: An extended framework. Proc. Natl. Acad. Sci. USA 103, 10799-10804.
    • (2006) Proc. Natl. Acad. Sci. USA , vol.103 , pp. 10799-10804
  • 27
    • 50449099617 scopus 로고    scopus 로고
    • A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks
    • Sridharan, D. et al. (2008) A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc. Natl. Acad. Sci. USA 105(34), 12569-12574
    • (2008) Proc. Natl. Acad. Sci. USA , vol.105 , Issue.34 , pp. 12569-12574
    • Sridharan, D.1
  • 28
    • 0037195138 scopus 로고    scopus 로고
    • Quantitative noise analysis for gene expression microarray experiments
    • Tu, Y. et al. (2002) Quantitative noise analysis for gene expression microarray experiments, Proc Natl Acad Sci USA 99(22), 14031-14036.
    • (2002) Proc Natl Acad Sci USA , vol.99 , Issue.22 , pp. 14031-14036
    • Tu, Y.1
  • 29
    • 1642287458 scopus 로고    scopus 로고
    • Universality and flexibility in gene expression from bacteria to human
    • Ueda, H.R. et al. (2004) Universality and flexibility in gene expression from bacteria to human. Proc. Natl. Acad. Sci. USA 16(101), 3765-9
    • (2004) Proc. Natl. Acad. Sci. USA , vol.16 , Issue.101 , pp. 3765-9
    • Ueda, H.R.1
  • 30
    • 0035985177 scopus 로고    scopus 로고
    • Identification of genes periodically expressed in the human cell cycle and their expression in tumors
    • Whitfield, M.L. et al. (2002) Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell. 13(6), 1977-2000.
    • (2002) Mol. Biol. Cell. , vol.13 , Issue.6 , pp. 1977-2000
    • Whitfield, M.L.1
  • 31
    • 67650927399 scopus 로고    scopus 로고
    • Granger causality vs. dynamic Bayesian network inference: A comparative study
    • Zu, C and Feng, J. (2009) Granger causality vs. dynamic Bayesian network inference: a comparative study. BMC Bioinformatics 10, 122.
    • (2009) BMC Bioinformatics , vol.10 , pp. 122
    • Zu, C.1    Feng, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.