메뉴 건너뛰기




Volumn 24, Issue 1, 2007, Pages 37-46

Finding Module-Based Gene Networks with State-Space Models

Author keywords

[No Author keywords available]

Indexed keywords


EID: 85032779470     PISSN: 10535888     EISSN: None     Source Type: Journal    
DOI: 10.1109/MSP.2007.273053     Document Type: Article
Times cited : (36)

References (21)
  • 1
    • 39149107070 scopus 로고    scopus 로고
    • State-space approach with the maximum likelihood principle to identify the system generating time-course gene expression data of yeast
    • R. Yamaguchi and T. Higuchi, “State-space approach with the maximum likelihood principle to identify the system generating time-course gene expression data of yeast,” Int. J. Data Mining Bioinformatics, vol. 1, no. 1, pp. 77–87, 2006.
    • (2006) Int. J. Data Mining Bioinformatics , vol.1 , Issue.1 , pp. 77-87
    • Yamaguchi, R.1    Higuchi, T.2
  • 2
    • 0003578943 scopus 로고
    • Forecasting, Structural Time Series Models and the Kalman Filter
    • New York: Cambridge Univ. Press
    • A.C. Harvey, Forecasting, Structural Time Series Models and the Kalman Filter. New York: Cambridge Univ. Press, 1989.
    • (1989)
    • Harvey, A.C.1
  • 3
    • 84986753417 scopus 로고
    • An approach to time series smoothing and forecasting using the EM algorithm
    • R.H. Shumway and D.S. Stoffer, “An approach to time series smoothing and forecasting using the EM algorithm,” J. Time Series Anal., vol. 3, no. 4, pp. 253-264,1982.
    • (1982) J. Time Series Anal. , vol.3 , Issue.4 , pp. 253-264
    • Shumway, R.H.1    Stoffer, D.S.2
  • 4
    • 0003789099 scopus 로고    scopus 로고
    • Smoothness Priors Analysis of Time Series
    • New York: Springer-Verlag
    • G. Kitagawa and W. Gersch, Smoothness Priors Analysis of Time Series. New York: Springer-Verlag, 1996.
    • (1996)
    • Kitagawa, G.1    Gersch, W.2
  • 6
    • 2442691914 scopus 로고    scopus 로고
    • Modeling gene expression from microarray expression data with state-space equations
    • City, State
    • F.X. Wu, W.J. Zhang, and A.J. Kusalic, “Modeling gene expression from microarray expression data with state-space equations,” in Proc. Pacific Symp. Biocomputing, City, State, vol. 9,2004, pp. 581–592.
    • (2004) Proc. Pacific Symp. Biocomputing , vol.9 , pp. 581-592
    • Wu, F.X.1    Zhang, W.J.2    Kusalic, A.J.3
  • 7
    • 84898615550 scopus 로고    scopus 로고
    • System identification of gene expression time-series based on a linear dynamical system model with variational Bayesian estimation
    • (in Japanese)
    • N. Yukinawa, J. Yoshimoto, S. Oba, and S. Ishii, “System identification of gene expression time-series based on a linear dynamical system model with variational Bayesian estimation,” (in Japanese), Inf. Process. Soc. Japan, Trans. Math. Modeling and Its Applicat., vol. 46, no. 10, pp. 57–65, 2005.
    • (2005) Inf. Process. Soc. Japan, Trans. Math. Modeling and Its Applicat. , vol.46 , Issue.10 , pp. 57-65
    • Yukinawa, N.1    Yoshimoto, J.2    Oba, S.3    Ishii, S.4
  • 9
    • 0037941585 scopus 로고    scopus 로고
    • Module networks: Identifying regulatory modules and their condition-specific regulators from gene expression data
    • E. Segal, M. Shapira, A. Regev, D. Pe’er, D. Botstein, D. Koller, and N. Friedman, “Module networks: Identifying regulatory modules and their condition-specific regulators from gene expression data,” Nat. Genetics, vol. 34, no. 2, pp. 166–176, 2003.
    • (2003) Nat. Genetics , vol.34 , Issue.2 , pp. 166-176
    • Segal, E.1    Shapira, M.2    Regev, A.3    Pe’er, D.4    Botstein, D.5    Koller, D.6    Friedman, N.7
  • 10
    • 0002629270 scopus 로고
    • Maximum likelihood from incomplete data via the EM algorithm
    • A. Dempster, N. Laird, and D. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. Royal Statistical Soc., ser. B, vol. 39, no. 1, pp. 1-38,1977.
    • (1977) J. Royal Statistical Soc., ser. B , vol.39 , Issue.1 , pp. 1-38
    • Dempster, A.1    Laird, N.2    Rubin, D.3
  • 11
    • 0031742022 scopus 로고    scopus 로고
    • Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization
    • P.T. Spellman, G. Sherlock, M.Q. Zhang, V.R. Iyer, K. Anders, M.B. Eisen, P.O. Brown, D. Botstein, and B. Futcher, “Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization,” Mol. Biol. Cell, vol. 9, no. 12. pp. 3273- 3297,1998.
    • (1998) Mol. Biol. Cell , vol.9 , Issue.12 , pp. 3273-3297
    • Spellman, P.T.1    Sherlock, G.2    Zhang, M.Q.3    Iyer, V.R.4    Anders, K.5    Eisen, M.B.6    Brown, P.O.7    Botstein, D.8    Futcher, B.9
  • 12
    • 0000120766 scopus 로고
    • Estimating the dimension of a model
    • G. Schwarz, “Estimating the dimension of a model,” Ann. Statist., vol. 6, no. 2, pp. 461-464,1978.
    • (1978) Ann. Statist. , vol.6 , Issue.2 , pp. 461-464
    • Schwarz, G.1
  • 13
    • 0003278032 scopus 로고    scopus 로고
    • Inferring parameters and structure of latent variable models by variational Bayes
    • H. Attias, “Inferring parameters and structure of latent variable models by variational Bayes,” in Proc. 15th Conf. Uncertainty Artificial Intelligence, 1999, pp. 21–30.
    • (1999) Proc. 15th Conf. Uncertainty Artificial Intelligence , pp. 21-30
    • Attias, H.1
  • 14
    • 84899003086 scopus 로고    scopus 로고
    • Propagation algorithms for variational Bayesian learning
    • Z. Ghahramani and M.J. Beal, “Propagation algorithms for variational Bayesian learning,” Adv. Neural Inform. Process. Syst., vol. 13, no. 1, pp. 507–513, 2001.
    • (2001) Adv. Neural Inform. Process. Syst. , vol.13 , Issue.1 , pp. 507-513
    • Ghahramani, Z.1    Beal, M.J.2
  • 16
    • 0141515892 scopus 로고    scopus 로고
    • Dynamic mixed models for irregularly observed time series
    • Univ. Sao Paulo, Brazil: Univ. San Paulo Press
    • R.H. Shumway, “Dynamic mixed models for irregularly observed time series,” Resenhas-Reviews of the Institute of Mathematics and Statistics, Univ. Sao Paulo, Brazil: Univ. San Paulo Press, vol. 4, no. 4, pp. 433-456. 2000.
    • (2000) Resenhas-Reviews of the Institute of Mathematics and Statistics , vol.4 , Issue.4 , pp. 433-456
    • Shumway, R.H.1
  • 17
    • 85024429815 scopus 로고
    • A new approach to linear filtering and prediction problems
    • R.E. Kalman, “A new approach to linear filtering and prediction problems,” Trans. Amer. Soc. Mech. Eng., J. Basic Eng., vol. 82, no. 1, pp. 35–45, 1960.
    • (1960) Trans. Amer. Soc. Mech. Eng., J. Basic Eng. , vol.82 , Issue.1 , pp. 35-45
    • Kalman, R.E.1
  • 18
    • 0041720000 scopus 로고    scopus 로고
    • An algorithm for estimating parameters of state-space models
    • L.S.-Y. Wu, J.S. Pai, and J.R.M. Hosking, “An algorithm for estimating parameters of state-space models,” Stat. Prob. Lett., vol. 28, no. 2, pp. 99–106, 1996.
    • (1996) Stat. Prob. Lett. , vol.28 , Issue.2 , pp. 99-106
    • Wu, L.S.-Y.1    Pai, J.S.2    Hosking, J.R.M.3
  • 19
    • 0033707946 scopus 로고    scopus 로고
    • Using Bayesian network to analyze expression data
    • N. Friedman, M. Linial, I. Nachman, and D. Pe’er., “Using Bayesian network to analyze expression data,”J. Comp. Biol., vol. 7, no. 3–4, pp. 601-620,2000.
    • (2000) J. Comp. Biol. , vol.7 , Issue.3-4 , pp. 601-620
    • Friedman, N.1    Linial, M.2    Nachman, I.3    Pe’er, D.4
  • 20
    • 0036184629 scopus 로고    scopus 로고
    • Probabilistic Boolean networks: A rule-based uncertainty model for gene regulatory networks
    • I. Shmulevich, E.R. Dougherty, S. Kim, and W. Zhang, “Probabilistic Boolean networks: A rule-based uncertainty model for gene regulatory networks,” Bioinformatics, vol. 18, no. 2, pp. 261-274,2002.
    • (2002) Bioinformatics , vol.18 , Issue.2 , pp. 261-274
    • Shmulevich, I.1    Dougherty, E.R.2    Kim, S.3    Zhang, W.4
  • 21
    • 3242875300 scopus 로고    scopus 로고
    • Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks
    • S. Imoto, T. Higuchi, T. Goto, K. Tashiro, S. Kuhara, and S. Miyano, “Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks,” J. Bioinform. Comp. Biol., vol. 2, no. 1, pp. 77-98,2004.
    • (2004) J. Bioinform. Comp. Biol. , vol.2 , Issue.1 , pp. 77-98
    • Imoto, S.1    Higuchi, T.2    Goto, T.3    Tashiro, K.4    Kuhara, S.5    Miyano, S.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.