메뉴 건너뛰기




Volumn 6, Issue 3, 2012, Pages 1209-1235

Network inference and biological dynamics

(2)  Oates, Chris J a   Mukherjee, Sach a  

a NONE

Author keywords

Biological dynamics; Network inference; Variable selection

Indexed keywords


EID: 84866463900     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/11-AOAS532     Document Type: Article
Times cited : (65)

References (65)
  • 1
    • 70449375094 scopus 로고    scopus 로고
    • Learning gene regulatory networks from gene expression measurements using nonparametric molecular kinetics
    • Äijö, T. and Lähdesmäki, H. (2009). Learning gene regulatory networks from gene expression measurements using nonparametric molecular kinetics. Bioinformatics 25 2937-2944.
    • (2009) Bioinformatics , vol.25 , pp. 2937-2944
    • Äijö, T.1    Lähdesmäki, H.2
  • 2
    • 77954484005 scopus 로고    scopus 로고
    • Revealing differences in gene network inference algorithms on the network level by ensemble methods
    • Altay, G. and Emmert-Streib, F. (2010). Revealing differences in gene network inference algorithms on the network level by ensemble methods. Bioinformatics 26 1738-1744.
    • (2010) Bioinformatics , vol.26 , pp. 1738-1744
    • Altay, G.1    Emmert-Streib, F.2
  • 4
    • 33847055114 scopus 로고    scopus 로고
    • How to infer gene networks from expression profiles
    • Article No. 78
    • Bansal, M., Belcastro, V. and Ambesi-Impiombato, A. (2007). How to infer gene networks from expression profiles. Mol. Sys. Bio. 3 Article No. 78.
    • (2007) Mol. Sys. Bio , vol.3
    • Bansal, M.1    Belcastro, V.2    Ambesi-Impiombato, A.3
  • 5
    • 34548388925 scopus 로고    scopus 로고
    • Inference of gene networks from temporal gene expression profiles
    • Bansal, M. and di Bernardo, D. (2007). Inference of gene networks from temporal gene expression profiles. IET Syst. Biol. 1 306-312.
    • (2007) IET Syst. Biol , vol.1 , pp. 306-312
    • Bansal, M.1    di Bernardo, D.2
  • 6
    • 13844253637 scopus 로고    scopus 로고
    • A Bayesian approach to reconstructing genetic regulatory networks with hidden factors
    • Beal, M. J., Falciani, F., Ghahramani, Z., Rangel, C. and Wild, D. L. (2005). A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics 21 349-356.
    • (2005) Bioinformatics , vol.21 , pp. 349-356
    • Beal, M.J.1    Falciani, F.2    Ghahramani, Z.3    Rangel, C.4    Wild, D.L.5
  • 7
    • 79957439381 scopus 로고    scopus 로고
    • Causal network inference via group sparse regularization
    • Mathematical Reviews (MathSciNet): MR2840690 Digital Object Identifier: doi:10.1109/TSP.2011.2129515
    • Bolstad, A., Van Veen, B. D. and Nowak, R. (2011). Causal network inference via group sparse regularization. IEEE Trans. Signal Process. 59 2628-2641. Mathematical Reviews (MathSciNet): MR2840690 Digital Object Identifier: doi:10.1109/TSP.2011.2129515
    • (2011) IEEE Trans. Signal Process , vol.59 , pp. 2628-2641
    • Bolstad, A.1    van Veen, B.D.2    Nowak, R.3
  • 8
    • 54249124501 scopus 로고    scopus 로고
    • Learning biological networks: From modules to dynamics
    • Bonneau, R. (2008). Learning biological networks: From modules to dynamics. Nat. Chem. Bio. 4 658-664.
    • (2008) Nat. Chem. Bio , vol.4 , pp. 658-664
    • Bonneau, R.1
  • 10
    • 63249114107 scopus 로고    scopus 로고
    • Systems biology strikes gold
    • Zentralblatt MATH: 1241.93026
    • Camacho, D. M. and Collins, J. J. (2009). Systems biology strikes gold. Cell 137 24-26. Zentralblatt MATH: 1241.93026
    • (2009) Cell , vol.137 , pp. 24-26
    • Camacho, D.M.1    Collins, J.J.2
  • 12
    • 43249126563 scopus 로고    scopus 로고
    • Identifiability of chemical reaction networks
    • Mathematical Reviews (MathSciNet): MR2403645 Zentralblatt MATH: 1145.92040 Digital Object Identifier:, doi:10.1007/s10910-007-9307-x
    • Craciun, G. and Pantea, C. (2008). Identifiability of chemical reaction networks. J. Math. Chem. 44 244-259. Mathematical Reviews (MathSciNet): MR2403645 Zentralblatt MATH: 1145.92040 Digital Object Identifier: doi:10.1007/s10910-007-9307-x
    • (2008) J. Math. Chem , vol.44 , pp. 244-259
    • Craciun, G.1    Pantea, C.2
  • 16
    • 84862287246 scopus 로고    scopus 로고
    • Exact Bayesian structure learning from uncertain interventions
    • March 21-24, 2007, San Juan, Puerto Rico. Journal of Machine Learning Research, Workshop and Conference Proceedings
    • Eaton, D. and Murphy, K. (2007). Exact Bayesian structure learning from uncertain interventions. In Proceedings of 11th Conference on Artificial Intelligence and Statistics, March 21-24, 2007, San Juan, Puerto Rico. Journal of Machine Learning Research, Workshop and Conference Proceedings, Vol. 2: AISTATS 2007 107-114.
    • (2007) Proceedings of 11th Conference On Artificial Intelligence and Statistic , vol.2007 , pp. 107-114
    • Eaton, D.1    Murphy, K.2
  • 17
    • 49549100459 scopus 로고    scopus 로고
    • Learning causal Bayesian network structures from experimental data
    • Mathematical Reviews (MathSciNet): MR2524009 Zentralblatt MATH: 05564531 Digital Object Identifier:, doi:10.1198/016214508000000193
    • Ellis, B. and Wong, W. H. (2008). Learning causal Bayesian network structures from experimental data. J. Amer. Statist. Assoc. 103 778-789. Mathematical Reviews (MathSciNet): MR2524009 Zentralblatt MATH: 05564531 Digital Object Identifier: doi:10.1198/016214508000000193
    • (2008) J. Amer. Statist. Assoc , vol.103 , pp. 778-789
    • Ellis, B.1    Wong, W.H.2
  • 18
    • 0037119587 scopus 로고    scopus 로고
    • Stochastic gene expression in a single cell
    • Elowitz, M. B., Levine, A. J. and Siggia, E. D. (2002). Stochastic gene expression in a single cell. Science 297 1129-1131.
    • (2002) Science , vol.297 , pp. 1129-1131
    • Elowitz, M.B.1    Levine, A.J.2    Siggia, E.D.3
  • 19
    • 33646023117 scopus 로고    scopus 로고
    • An introduction to ROC analysis
    • Fawcett, T. (2005). An introduction to ROC analysis. Pattern Recognition Letters 27 861-874.
    • (2005) Pattern Recognition Letters , vol.27 , pp. 861-874
    • Fawcett, T.1
  • 20
    • 45849134070 scopus 로고    scopus 로고
    • Sparse inverse covariance estimation with the graphical lasso
    • Friedman, J., Hastie, T. and Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9 432-441.
    • (2008) Biostatistics , vol.9 , pp. 432-441
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 21
    • 0037262841 scopus 로고    scopus 로고
    • Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks
    • Friedman, J. and Koller, D. (2003). Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks. Mach. Learn. 50 95-125.
    • (2003) Mach. Learn , vol.50 , pp. 95-125
    • Friedman, J.1    Koller, D.2
  • 22
    • 0033707946 scopus 로고    scopus 로고
    • Using Bayesian networks to analyze expression data
    • Friedman, N., Linial, M. and Nachman, I. et al. (2000). Using Bayesian networks to analyze expression data. J. Comp. Bio. 7 601-620.
    • (2000) J. Comp. Bio , vol.7 , pp. 601-620
    • Friedman, N.1    Linial, M.2    Nachman, I.3
  • 23
    • 79951965650 scopus 로고    scopus 로고
    • Improvements in the reconstruction of time-varying gene regulatory networks: Dynamic programming and regularization by information sharing among genes
    • Grzegorczyk, M. and Husmeier, D. (2011). Improvements in the reconstruction of time-varying gene regulatory networks: Dynamic programming and regularization by information sharing among genes. Bioinformatics 27 693-699.
    • (2011) Bioinformatics , vol.27 , pp. 693-699
    • Grzegorczyk, M.1    Husmeier, D.2
  • 24
    • 68149164746 scopus 로고    scopus 로고
    • Reverse engineering of gene regulatory networks: A comparative study
    • Hache, H., Lehrach, H. and Herwig, R. (2009). Reverse engineering of gene regulatory networks: A comparative study. EURASIP J. Bioinform. Syst. Biol. 617281.
    • (2009) EURASIP J. Bioinform. Syst. Biol , pp. 617281
    • Hache, H.1    Lehrach, H.2    Herwig, R.3
  • 25
    • 34250747348 scopus 로고    scopus 로고
    • Shotgun stochastic search for "large p" regression
    • Mathematical Reviews (MathSciNet): MR2370849 Digital Object Identifier: doi:10.1198/016214507000000121
    • Hans, C., Dobra, A. and West, M. (2007). Shotgun stochastic search for "large p" regression. J. Amer. Statist. Assoc. 102 507-516. Mathematical Reviews (MathSciNet): MR2370849 Digital Object Identifier: doi:10.1198/016214507000000121
    • (2007) J. Amer. Statist. Assoc , vol.102 , pp. 507-516
    • Hans, C.1    Dobra, A.2    West, M.3
  • 26
    • 61349180117 scopus 로고    scopus 로고
    • Gene regulatory network inference: Data integration in dynamic models-a review
    • Hecker, M., Lambeck, S., Toepfer, S., van Someren, E. and Guthke, R. (2009). Gene regulatory network inference: Data integration in dynamic models-a review. BioSystems 96 86-103.
    • (2009) BioSystems , vol.96 , pp. 86-103
    • Hecker, M.1    Lambeck, S.2    Toepfer, S.3    van Someren, E.4    Guthke, R.5
  • 28
    • 34447637158 scopus 로고    scopus 로고
    • Seeing the wood for the trees: A critical evaluation of methods to estimate the parameters of stochastic differential equations
    • Hurn, A., Jeisman, J. and Lindsay, K. (2007). Seeing the wood for the trees: A critical evaluation of methods to estimate the parameters of stochastic differential equations. Journal of Financial Econometrics 5 390.
    • (2007) Journal of Financial Econometrics , vol.5 , pp. 390
    • Hurn, A.1    Jeisman, J.2    Lindsay, K.3
  • 29
    • 0037716676 scopus 로고    scopus 로고
    • Building with a scaffold: Emerging strategies for high to low level cellular modelling
    • Ideker, T. and Lauffenburger, D. (2003). Building with a scaffold: Emerging strategies for high to low level cellular modelling. Trends in Biotechnology 21 255-262.
    • (2003) Trends In Biotechnology , vol.21 , pp. 255-262
    • Ideker, T.1    Lauffenburger, D.2
  • 30
    • 0842309206 scopus 로고    scopus 로고
    • Inferring gene networks from time series microarray data using dynamic Bayesian networks
    • Kim, S. Y., Imoto, S. and Miyano, S. (2003). Inferring gene networks from time series microarray data using dynamic Bayesian networks. Briefings in Bioinformatics 4 228-235.
    • (2003) Briefings In Bioinformatics , vol.4 , pp. 228-235
    • Kim, S.Y.1    Imoto, S.2    Miyano, S.3
  • 31
    • 84857233123 scopus 로고    scopus 로고
    • Nonparametric Bayesian sparse factor models with application to gene expression modeling
    • Mathematical Reviews (MathSciNet): MR2849785 Zentralblatt MATH: 1223.62013 Digital Object Identifier:, doi:10.1214/10-AOAS435 Project Euclid: euclid.aoas/1310562732
    • Knowles, D. and Ghahramani, Z. (2011). Nonparametric Bayesian sparse factor models with application to gene expression modeling. Ann. Appl. Stat. 5 1534-1552. Mathematical Reviews (MathSciNet): MR2849785 Zentralblatt MATH: 1223.62013 Digital Object Identifier: doi:10.1214/10-AOAS435 Project Euclid: euclid.aoas/1310562732
    • (2011) Ann. Appl. Stat , vol.5 , pp. 1534-1552
    • Knowles, D.1    Ghahramani, Z.2
  • 32
    • 77956517638 scopus 로고    scopus 로고
    • Sparsistent learning of varying-coefficient models with structural changes
    • Kolar, M., Song, L. and Xing, E. P. (2009). Sparsistent learning of varying-coefficient models with structural changes. NIPS 22 1006-1014.
    • (2009) NIPS , vol.22 , pp. 1006-1014
    • Kolar, M.1    Song, L.2    Xing, E.P.3
  • 33
    • 18444379363 scopus 로고    scopus 로고
    • Bayesian analysis of single-molecule experimental data
    • Mathematical Reviews (MathSciNet): MR2137252 Zentralblatt MATH: 05188696 Digital Object Identifier:, doi:10.1111/j.1467-9876.2005.00509.x
    • Kou, S. C., Xie, X. S. and Liu, J. S. (2005). Bayesian analysis of single-molecule experimental data. J. Roy. Statist. Soc. Ser. C 54 469-506. Mathematical Reviews (MathSciNet): MR2137252 Zentralblatt MATH: 05188696 Digital Object Identifier: doi:10.1111/j.1467-9876.2005.00509.x
    • (2005) J. Roy. Statist. Soc. Ser. C , vol.54 , pp. 469-506
    • Kou, S.C.1    Xie, X.S.2    Liu, J.S.3
  • 34
    • 77957930628 scopus 로고    scopus 로고
    • Statistical inference of the time-varying structure of gene- regulation networks
    • Lèbre, S., Becq, J. and Devaux, F. et al. (2010). Statistical inference of the time-varying structure of gene- regulation networks. BMC Systems Biology 4 130.
    • (2010) BMC Systems Biology , vol.4 , pp. 130
    • Lèbre, S.1    Becq, J.2    Devaux, F.3
  • 35
    • 67449095889 scopus 로고    scopus 로고
    • Computational methods for discovering gene networks from expression data
    • Lee, W.-P. and Tzou, W.-S. (2009). Computational methods for discovering gene networks from expression data. Brief. Bioinformatics 10 408-423.
    • (2009) Brief. Bioinformatics , vol.10 , pp. 408-423
    • Lee, W.-P.1    Tzou, W.-S.2
  • 36
    • 77952510758 scopus 로고    scopus 로고
    • Identifying functional mechanisms of gene and protein regulatory networks in response to a broader range of environmental stresses
    • Li, C. W. and Chen, B. S. (2010). Identifying functional mechanisms of gene and protein regulatory networks in response to a broader range of environmental stresses. Comp. and Func. Genomics 408705.
    • (2010) Comp. and Func. Genomics , pp. 408705
    • Li, C.W.1    Chen, B.S.2
  • 37
    • 80053436505 scopus 로고    scopus 로고
    • Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis
    • Zentralblatt MATH: 1022.68519
    • Li, Z., Li, P., Krishnan, A. and Liu, J. (2011). Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis. Bioinformatics 27 2686-2691. Zentralblatt MATH: 1022.68519
    • (2011) Bioinformatics , vol.27 , pp. 2686-2691
    • Li, Z.1    Li, P.2    Krishnan, A.3    Liu, J.4
  • 38
    • 59649110273 scopus 로고    scopus 로고
    • Generating realistic in silico gene networks for performance assessment of reverse engineering methods
    • Marbach, D., Schaffter, T., Mattiussi, C. and Floreano, D. (2009). Generating realistic in silico gene networks for performance assessment of reverse engineering methods. J. Comput. Biol. 16 229-239.
    • (2009) J. Comput. Biol , vol.16 , pp. 229-239
    • Marbach, D.1    Schaffter, T.2    Mattiussi, C.3    Floreano, D.4
  • 39
    • 38449088751 scopus 로고    scopus 로고
    • Inferring cellular networks-A review
    • Markowetz, F. and Spang, R. (2007). Inferring cellular networks-A review. BMC Bioinformatics 8(Suppl. 6) S5.
    • (2007) BMC Bioinformatics , vol.8 , Issue.SUPPL. 6
    • Markowetz, F.1    Spang, R.2
  • 40
    • 0031029852 scopus 로고    scopus 로고
    • Stochastic mechanisms in gene expression
    • McAdams, H. H. and Arkin, A. (1997). Stochastic mechanisms in gene expression. PNAS 94 814-819.
    • (1997) PNAS , vol.94 , pp. 814-819
    • McAdams, H.H.1    Arkin, A.2
  • 41
    • 33747163541 scopus 로고    scopus 로고
    • High-dimensional graphs and variable selection with the lasso
    • Mathematical Reviews (MathSciNet): MR2278363 Zentralblatt MATH: 1113.62082 Digital Object Identifier:, doi:10.1214/009053606000000281 Project Euclid: euclid.aos/1152540754
    • Meinshausen, N. and Bühlmann, P. (2006). High-dimensional graphs and variable selection with the lasso. Ann. Statist. 34 1436-1462. Mathematical Reviews (MathSciNet): MR2278363 Zentralblatt MATH: 1113.62082 Digital Object Identifier: doi:10.1214/009053606000000281 Project Euclid: euclid.aos/1152540754
    • (2006) Ann. Statist , vol.34 , pp. 1436-1462
    • Meinshausen, N.1    Bühlmann, P.2
  • 42
    • 62949105539 scopus 로고    scopus 로고
    • Network benchmarking: A happy marriage between systems and synthetic biology
    • Minty, J. J., Varedi, K. S. M. and Nina, L. X. (2009). Network benchmarking: A happy marriage between systems and synthetic biology. Chemistry and Biology 16 239-241.
    • (2009) Chemistry and Biology , vol.16 , pp. 239-241
    • Minty, J.J.1    Varedi, K.S.M.2    Nina, L.X.3
  • 43
    • 77956537377 scopus 로고    scopus 로고
    • On reverse engineering of gene interaction networks using time course data with repeated measurements
    • Morrissey, E. R., Juárez, M. A., Denby, K. J. and Burroughs, N. J. (2010). On reverse engineering of gene interaction networks using time course data with repeated measurements. Bioinformatics 26 2305-2312.
    • (2010) Bioinformatics , vol.26 , pp. 2305-2312
    • Morrissey, E.R.1    Juárez, M.A.2    Denby, K.J.3    Burroughs, N.J.4
  • 44
    • 55749093996 scopus 로고    scopus 로고
    • Network inference using informative priors
    • Mukherjee, S. and Speed, T. P. (2008). Network inference using informative priors. PNAS 105 14313-14318.
    • (2008) PNAS , vol.105 , pp. 14313-14318
    • Mukherjee, S.1    Speed, T.P.2
  • 45
    • 36549040919 scopus 로고    scopus 로고
    • Ensemble learning of genetic networks from time-series expression data
    • Nam, D., Yoon, S. H. and Kim, J. F. (2007). Ensemble learning of genetic networks from time-series expression data. Bioinformatics 23 3225-3231.
    • (2007) Bioinformatics , vol.23 , pp. 3225-3231
    • Nam, D.1    Yoon, S.H.2    Kim, J.F.3
  • 48
    • 34249862287 scopus 로고    scopus 로고
    • Learning causal networks from systems biology time course data: An effective model selection procedure for the vector autoregressive process
    • Opgen-Rhein, R. and Strimmer, K. (2007). Learning causal networks from systems biology time course data: An effective model selection procedure for the vector autoregressive process. BMC Bioinformatics 8(Suppl. 2) S3.
    • (2007) BMC Bioinformatics , vol.8 , Issue.SUPPL. 2
    • Opgen-Rhein, R.1    Strimmer, K.2
  • 49
    • 20344389483 scopus 로고    scopus 로고
    • Models of stochastic gene expression
    • Paulsson, J. (2005). Models of stochastic gene expression. Physics of Life Reviews 2 157-175.
    • (2005) Physics of Life Reviews , vol.2 , pp. 157-175
    • Paulsson, J.1
  • 50
    • 77649325496 scopus 로고    scopus 로고
    • Causal inference in statistics: An overview
    • Mathematical Reviews (MathSciNet): MR2545291 Zentralblatt MATH: 05719273 Digital Object Identifier:, doi:10.1214/09-SS057 Project Euclid: euclid.ssu/1255440554
    • Pearl, J. (2009). Causal inference in statistics: An overview. Stat. Surv. 3 96-146. Mathematical Reviews (MathSciNet): MR2545291 Zentralblatt MATH: 05719273 Digital Object Identifier: doi:10.1214/09-SS057 Project Euclid: euclid.ssu/1255440554
    • (2009) Stat. Surv , vol.3 , pp. 96-146
    • Pearl, J.1
  • 52
    • 34249856850 scopus 로고    scopus 로고
    • Bayesian model-based inference of transcription factor activity
    • Rogers, S., Khanin, R. and Girolami, M. (2007). Bayesian model-based inference of transcription factor activity. BMC Bioinformatics 8(Suppl. 2) S2.
    • (2007) BMC Bioinformatics , vol.8 , Issue.SUPPL. 2
    • Rogers, S.1    Khanin, R.2    Girolami, M.3
  • 53
    • 0036212767 scopus 로고    scopus 로고
    • Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors
    • Schoeberl, B., Eichler-Jonsson, C., Gilles, E. D. and Müller, G. (2002). Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat. Biotechnol. 20 370-375.
    • (2002) Nat. Biotechnol , vol.20 , pp. 370-375
    • Schoeberl, B.1    Eichler-Jonsson, C.2    Gilles, E.D.3    Müller, G.4
  • 54
    • 0000042837 scopus 로고    scopus 로고
    • Evaluating functional network inference using simulations of complex biological systems
    • Zentralblatt MATH: 1177.62126
    • Smith, V. A., Jarvis, E. D. and Hartemink, A. J. (2002). Evaluating functional network inference using simulations of complex biological systems. Bioinformatics 18 S216-S224. Zentralblatt MATH: 1177.62126
    • (2002) Bioinformatics , vol.18
    • Smith, V.A.1    Jarvis, E.D.2    Hartemink, A.J.3
  • 55
    • 0036790975 scopus 로고    scopus 로고
    • Intrinsic and extrinsic contributions to stochasticity in gene expression
    • Swain, P. S., Elowitz, M. B. and Siggia, E. D. (2002). Intrinsic and extrinsic contributions to stochasticity in gene expression. PNAS 99 12795-12800.
    • (2002) PNAS , vol.99 , pp. 12795-12800
    • Swain, P.S.1    Elowitz, M.B.2    Siggia, E.D.3
  • 56
    • 3543138827 scopus 로고    scopus 로고
    • Bifurcation analysis of the regulatory modules of the mammalian G1/S transition
    • Swat, M., Kel, A. and Herzel, H. (2004). Bifurcation analysis of the regulatory modules of the mammalian G1/S transition. Bioinformatics 20 1506-1511.
    • (2004) Bioinformatics , vol.20 , pp. 1506-1511
    • Swat, M.1    Kel, A.2    Herzel, H.3
  • 59
    • 33749825955 scopus 로고    scopus 로고
    • Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks
    • Werhli, A. V., Grzegorczyk, M. and Husmeier, D. (2006). Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks. Bioinformatics 22 2523-2531.
    • (2006) Bioinformatics , vol.22 , pp. 2523-2531
    • Werhli, A.V.1    Grzegorczyk, M.2    Husmeier, D.3
  • 60
    • 33750294583 scopus 로고    scopus 로고
    • Chapman & Hall/CRC, Boca Raton, FL, Mathematical Reviews (MathSciNet): MR2222876
    • Wilkinson, D. J. (2006). Stochastic Modelling for Systems Biology. Chapman & Hall/CRC, Boca Raton, FL. Mathematical Reviews (MathSciNet): MR2222876
    • (2006) Stochastic Modelling For Systems Biology
    • Wilkinson, D.J.1
  • 61
    • 58549110252 scopus 로고    scopus 로고
    • Stochastic modelling for quantitative description of heterogeneous biological systems
    • Wilkinson, D. J. (2009). Stochastic modelling for quantitative description of heterogeneous biological systems. Nature Reviews Genetics 10 122-133.
    • (2009) Nature Reviews Genetics , vol.10 , pp. 122-133
    • Wilkinson, D.J.1
  • 64
    • 0002817906 scopus 로고
    • On assessing prior distributions and Bayesian regression analysis with g-prior distributions
    • North-Holland, Amsterdam, Mathematical Reviews (MathSciNet): MR881437 Zentralblatt MATH: 0655.62071
    • Zellner, A. (1986). On assessing prior distributions and Bayesian regression analysis with g-prior distributions. In Bayesian Inference and Decision Techniques. Stud. Bayesian Econometrics Statist. 6 233-243. North-Holland, Amsterdam. Mathematical Reviews (MathSciNet): MR881437 Zentralblatt MATH: 0655.62071
    • (1986) Bayesian Inference and Decision Techniques. Stud. Bayesian Econometrics Statist , pp. 233-243
    • Zellner, A.1
  • 65
    • 73149088616 scopus 로고    scopus 로고
    • Granger causality vs. dynamic Bayesian network inference: A comparative study
    • Zou, C. and Feng, J. (2009). Granger causality vs. dynamic Bayesian network inference: A comparative study. BMC Bioinformatics 10 12.
    • (2009) BMC Bioinformatics , vol.10 , pp. 12
    • Zou, C.1    Feng, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.