메뉴 건너뛰기




Volumn 96, Issue 1, 2009, Pages 86-103

Gene regulatory network inference: Data integration in dynamic models-A review

Author keywords

Biological modelling; Knowledge integration; Reverse engineering; Systems biology

Indexed keywords

ALGORITHM; BIOENGINEERING; DATA PROCESSING; DNA; EXPERIMENTAL STUDY; GENE EXPRESSION; GENOME; LITERATURE REVIEW; NUMERICAL MODEL; PROTEIN; RECONSTRUCTION;

EID: 61349180117     PISSN: 03032647     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biosystems.2008.12.004     Document Type: Article
Times cited : (628)

References (123)
  • 1
    • 0032616683 scopus 로고    scopus 로고
    • Identification of genetic networks from a small number of gene expression patterns under the Boolean network model
    • Akutsu T., Miyano S., and Kuhara S. Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. Proceeding of the Pacific Symposium on Biocomputing (1999) 17-28
    • (1999) Proceeding of the Pacific Symposium on Biocomputing , pp. 17-28
    • Akutsu, T.1    Miyano, S.2    Kuhara, S.3
  • 2
    • 0030986188 scopus 로고    scopus 로고
    • The hardwiring of development: organization and function of genomic regulatory systems
    • Arnone M.I., and Davidson E.H. The hardwiring of development: organization and function of genomic regulatory systems. Development 124 (1997) 1851-1864
    • (1997) Development , vol.124 , pp. 1851-1864
    • Arnone, M.I.1    Davidson, E.H.2
  • 3
    • 33645307955 scopus 로고    scopus 로고
    • Inference of gene regulatory networks and compound mode of action from time course gene expression profiles
    • Bansal M., Gatta G.D., and di Bernardo D. Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics 22 7 (2006) 815-822
    • (2006) Bioinformatics , vol.22 , Issue.7 , pp. 815-822
    • Bansal, M.1    Gatta, G.D.2    di Bernardo, D.3
  • 7
    • 15944361900 scopus 로고    scopus 로고
    • Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data
    • Bernard A., and Hartemink A.J. Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data. Proceeding of the Pacific Symposium on Biocomputing (2005) 459-470
    • (2005) Proceeding of the Pacific Symposium on Biocomputing , pp. 459-470
    • Bernard, A.1    Hartemink, A.J.2
  • 8
    • 85194562496 scopus 로고    scopus 로고
    • Birkmeier, B., 2006. Integrating Prior Knowledge into the Fitness Function of an Evolutionary Algorithm for Deriving Gene Regulatory Networks (Master Thesis). University of Skövde, Sweden.
    • Birkmeier, B., 2006. Integrating Prior Knowledge into the Fitness Function of an Evolutionary Algorithm for Deriving Gene Regulatory Networks (Master Thesis). University of Skövde, Sweden.
  • 9
    • 33747813561 scopus 로고    scopus 로고
    • The inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo
    • Bonneau R., Reiss D.J., Shannon P., Facciotti M., Hood L., Baliga N.S., and Thorsson V. The inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol. 7 5 (2006) R36
    • (2006) Genome Biol. , vol.7 , Issue.5
    • Bonneau, R.1    Reiss, D.J.2    Shannon, P.3    Facciotti, M.4    Hood, L.5    Baliga, N.S.6    Thorsson, V.7
  • 11
    • 46249112705 scopus 로고    scopus 로고
    • Boolean network models of cellular regulation: prospects and limitations
    • Bornholdt S. Boolean network models of cellular regulation: prospects and limitations. J. R. Soc. Interf. 5 (2008) S85-S94
    • (2008) J. R. Soc. Interf. , vol.5
    • Bornholdt, S.1
  • 12
    • 0033655775 scopus 로고    scopus 로고
    • Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements
    • Butte A., and Kohane I. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Proceeding of the Pacific Symposium on Biocomputing (2000) 418-429
    • (2000) Proceeding of the Pacific Symposium on Biocomputing , pp. 418-429
    • Butte, A.1    Kohane, I.2
  • 14
    • 0035060857 scopus 로고    scopus 로고
    • Identifying gene regulatory networks from experimental data
    • Chen T., Filkov V., and Skiena S. Identifying gene regulatory networks from experimental data. Parallel Comput. 27 1-2 (2001) 141-162
    • (2001) Parallel Comput. , vol.27 , Issue.1-2 , pp. 141-162
    • Chen, T.1    Filkov, V.2    Skiena, S.3
  • 15
    • 41349112109 scopus 로고    scopus 로고
    • Rank-based edge reconstruction for scale-free genetic regulatory networks
    • Chen G., Larsen P., Almasri E., and Dai Y. Rank-based edge reconstruction for scale-free genetic regulatory networks. BMC Bioinform. 9 (2008) 75
    • (2008) BMC Bioinform. , vol.9 , pp. 75
    • Chen, G.1    Larsen, P.2    Almasri, E.3    Dai, Y.4
  • 17
    • 34547763812 scopus 로고    scopus 로고
    • A stochastic differential equation model for transcriptional regulatory networks
    • Climescu-Haulica A., and Quirk M.D. A stochastic differential equation model for transcriptional regulatory networks. BMC Bioinform. 8 Suppl. 5 (2007) S4
    • (2007) BMC Bioinform. , vol.8 , Issue.SUPPL. 5
    • Climescu-Haulica, A.1    Quirk, M.D.2
  • 18
    • 0036207347 scopus 로고    scopus 로고
    • Modeling and simulation of genetic regulatory systems: a literature review
    • De Jong H. Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9 (2002) 67-103
    • (2002) J. Comput. Biol. , vol.9 , pp. 67-103
    • De Jong, H.1
  • 20
    • 0033736476 scopus 로고    scopus 로고
    • Genetic network inference: from co-expression clustering to reverse engineering
    • D'haeseleer P., Liang S., and Somogyi R. Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16 8 (2000) 707-726
    • (2000) Bioinformatics , vol.16 , Issue.8 , pp. 707-726
    • D'haeseleer, P.1    Liang, S.2    Somogyi, R.3
  • 25
    • 85145635829 scopus 로고    scopus 로고
    • Identifying gene regulatory networks from gene expression data
    • Aluru (Ed), CRC Press, Chapman & Hall pp. 27.1-27.29
    • Filkov V. Identifying gene regulatory networks from gene expression data. In: Aluru (Ed). Handbook of Computational Molecular Biology (2005), CRC Press, Chapman & Hall pp. 27.1-27.29
    • (2005) Handbook of Computational Molecular Biology
    • Filkov, V.1
  • 26
    • 0032545933 scopus 로고    scopus 로고
    • Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans
    • Fire A., Xu S., Montgomery M.K., Kostas S.A., Driver S.E., and Mello C.C. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 391 (1998) 806-811
    • (1998) Nature , vol.391 , pp. 806-811
    • Fire, A.1    Xu, S.2    Montgomery, M.K.3    Kostas, S.A.4    Driver, S.E.5    Mello, C.C.6
  • 27
    • 0033707946 scopus 로고    scopus 로고
    • Using Bayesian networks to analyze expression data
    • Friedman N., Linial M., Nachman I., and Peer D. Using Bayesian networks to analyze expression data. J. Comput. Biol. 7 6 (2000) 601-620
    • (2000) J. Comput. Biol. , vol.7 , Issue.6 , pp. 601-620
    • Friedman, N.1    Linial, M.2    Nachman, I.3    Peer, D.4
  • 28
    • 0038048325 scopus 로고    scopus 로고
    • Inferring genetic networks and identifying compound mode of action via expression profiling
    • Gardner T.S., di Bernardo D., Lorenz D., and Collins J.J. Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301 (2003) 102-105
    • (2003) Science , vol.301 , pp. 102-105
    • Gardner, T.S.1    di Bernardo, D.2    Lorenz, D.3    Collins, J.J.4
  • 29
    • 14844286390 scopus 로고    scopus 로고
    • Reverse-engineering transcription control networks
    • Gardner T.S., and Faith J.J. Reverse-engineering transcription control networks. Phys. Life Rev. 2 (2005) 65-88
    • (2005) Phys. Life Rev. , vol.2 , pp. 65-88
    • Gardner, T.S.1    Faith, J.J.2
  • 30
    • 34548538013 scopus 로고    scopus 로고
    • Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge
    • Geier F., Timmer J., and Fleck C. Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge. BMC Syst. Biol. 1 (2007) 11
    • (2007) BMC Syst. Biol. , vol.1 , pp. 11
    • Geier, F.1    Timmer, J.2    Fleck, C.3
  • 31
    • 0036798238 scopus 로고    scopus 로고
    • Judging the quality of gene expression-based clustering methods using gene annotation
    • Gibbons F.D., and Roth F.P. Judging the quality of gene expression-based clustering methods using gene annotation. Genome Res. 12 10 (2002) 574-581
    • (2002) Genome Res. , vol.12 , Issue.10 , pp. 574-581
    • Gibbons, F.D.1    Roth, F.P.2
  • 33
    • 34249853738 scopus 로고    scopus 로고
    • Computational and experimental approaches for modeling gene regulatory networks
    • Goutsias J., and Lee N.H. Computational and experimental approaches for modeling gene regulatory networks. Curr. Pharm. Des. 13 14 (2007) 1415-1436
    • (2007) Curr. Pharm. Des. , vol.13 , Issue.14 , pp. 1415-1436
    • Goutsias, J.1    Lee, N.H.2
  • 35
    • 17444430052 scopus 로고    scopus 로고
    • Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection
    • Guthke R., Möller U., Hoffmann M., Thies F., and Töpfer S. Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection. Bioinformatics 21 8 (2005) 1626-1634
    • (2005) Bioinformatics , vol.21 , Issue.8 , pp. 1626-1634
    • Guthke, R.1    Möller, U.2    Hoffmann, M.3    Thies, F.4    Töpfer, S.5
  • 40
    • 0003846041 scopus 로고    scopus 로고
    • A Tutorial on Learning with Bayesian Networks
    • MSR-TR-95-06
    • Heckerman, D., 1996. A Tutorial on Learning with Bayesian Networks. Microsoft Research Tech. Report, MSR-TR-95-06.
    • (1996) Microsoft Research Tech. Report
    • Heckerman, D.1
  • 43
    • 0344464762 scopus 로고    scopus 로고
    • Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks
    • Husmeier D. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 19 17 (2003) 2271-2282
    • (2003) Bioinformatics , vol.19 , Issue.17 , pp. 2271-2282
    • Husmeier, D.1
  • 46
    • 46949103774 scopus 로고    scopus 로고
    • Bayesian variable selection and data integration for biological regulatory networks
    • Jensen S.T., Chen G., and Stoeckert C. Bayesian variable selection and data integration for biological regulatory networks. Ann. Appl. Stat. 1 (2007) 612-633
    • (2007) Ann. Appl. Stat. , vol.1 , pp. 612-633
    • Jensen, S.T.1    Chen, G.2    Stoeckert, C.3
  • 47
    • 0034609791 scopus 로고    scopus 로고
    • The large-scale organization of metabolic networks
    • Jeong H., Tombor B., Albert R., Oltvai Z.N., and Barabási A.L. The large-scale organization of metabolic networks. Nature 407 6804 (2000) 651-654
    • (2000) Nature , vol.407 , Issue.6804 , pp. 651-654
    • Jeong, H.1    Tombor, B.2    Albert, R.3    Oltvai, Z.N.4    Barabási, A.L.5
  • 48
    • 6344241652 scopus 로고    scopus 로고
    • Conservation and coevolution in the scale-free human gene coexpression network
    • Jordan I.K., Mariño-Ramírez L., Wolf Y.I., and Koonin E.V. Conservation and coevolution in the scale-free human gene coexpression network. Mol. Biol. Evol. 21 11 (2004) 2058-2070
    • (2004) Mol. Biol. Evol. , vol.21 , Issue.11 , pp. 2058-2070
    • Jordan, I.K.1    Mariño-Ramírez, L.2    Wolf, Y.I.3    Koonin, E.V.4
  • 49
    • 19544379881 scopus 로고    scopus 로고
    • Stochasticity in gene expression: from theories to phenotypes
    • Kaern M., Elston T.C., Blake W.J., and Collins J.J. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6 6 (2005) 451-464
    • (2005) Nat. Rev. Genet. , vol.6 , Issue.6 , pp. 451-464
    • Kaern, M.1    Elston, T.C.2    Blake, W.J.3    Collins, J.J.4
  • 50
    • 0014489272 scopus 로고
    • Metabolic stability and epigenesis in randomly constructed genetic nets
    • Kauffman S.A. Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22 (1969) 437-467
    • (1969) J. Theor. Biol. , vol.22 , pp. 437-467
    • Kauffman, S.A.1
  • 51
    • 34249025772 scopus 로고    scopus 로고
    • The end of the microarray Tower of Babel: will universal standards lead the way?
    • Kawasaki E.S. The end of the microarray Tower of Babel: will universal standards lead the way?. J. Biomed. Tech. 17 3 (2006) 200-206
    • (2006) J. Biomed. Tech. , vol.17 , Issue.3 , pp. 200-206
    • Kawasaki, E.S.1
  • 52
    • 24644470505 scopus 로고    scopus 로고
    • Ontological analysis of gene expression data: current tools, limitations, and open problems
    • Khatri P., and Draghici S. Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics 21 18 (2005) 3587-3595
    • (2005) Bioinformatics , vol.21 , Issue.18 , pp. 3587-3595
    • Khatri, P.1    Draghici, S.2
  • 54
    • 44449110026 scopus 로고    scopus 로고
    • Molecular discrimination of responders and nonresponders to anti-TNFalpha therapy in rheumatoid arthritis by etanercept
    • Koczan D., Drynda S., Hecker M., Drynda A., Guthke R., Kekow J., and Thiesen H.J. Molecular discrimination of responders and nonresponders to anti-TNFalpha therapy in rheumatoid arthritis by etanercept. Arthritis Res. Ther. 10 3 (2008) R50
    • (2008) Arthritis Res. Ther. , vol.10 , Issue.3
    • Koczan, D.1    Drynda, S.2    Hecker, M.3    Drynda, A.4    Guthke, R.5    Kekow, J.6    Thiesen, H.J.7
  • 55
    • 38349055557 scopus 로고    scopus 로고
    • Indeterminacy of reverse engineering of Gene Regulatory Networks: the curse of gene elasticity
    • Krishnan A., Giuliani A., and Tomita M. Indeterminacy of reverse engineering of Gene Regulatory Networks: the curse of gene elasticity. PLoS ONE 2 6 (2007) e562
    • (2007) PLoS ONE , vol.2 , Issue.6
    • Krishnan, A.1    Giuliani, A.2    Tomita, M.3
  • 56
    • 36349005434 scopus 로고    scopus 로고
    • A statistical method to incorporate biological knowledge for generating testable novel gene regulatory interactions from microarray experiments
    • Larsen P., Almasri E., Chen G., and Dai Y. A statistical method to incorporate biological knowledge for generating testable novel gene regulatory interactions from microarray experiments. BMC Bioinform. 8 (2007) 317
    • (2007) BMC Bioinform. , vol.8 , pp. 317
    • Larsen, P.1    Almasri, E.2    Chen, G.3    Dai, Y.4
  • 57
    • 10044240790 scopus 로고    scopus 로고
    • Using prior knowledge to improve genetic network reconstruction from microarray data
    • Le P.P., Bahl A., and Ungar L.H. Using prior knowledge to improve genetic network reconstruction from microarray data. Silico Biol. 4 3 (2004) 335-353
    • (2004) Silico Biol. , vol.4 , Issue.3 , pp. 335-353
    • Le, P.P.1    Bahl, A.2    Ungar, L.H.3
  • 61
    • 33947305781 scopus 로고    scopus 로고
    • ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context
    • Margolin A., Nemenman I., Basso K., Wiggins C., Stolovitzky G., Favera R., and Califano A. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform. 7 Suppl. 1 (2006) S7
    • (2006) BMC Bioinform. , vol.7 , Issue.SUPPL. 1
    • Margolin, A.1    Nemenman, I.2    Basso, K.3    Wiggins, C.4    Stolovitzky, G.5    Favera, R.6    Califano, A.7
  • 62
    • 27744489138 scopus 로고    scopus 로고
    • Non-transcriptional pathway features reconstructed from secondary effects of RNA interference
    • Markowetz F., Bloch J., and Spang R. Non-transcriptional pathway features reconstructed from secondary effects of RNA interference. Bioinformatics 21 21 (2005) 4026-4032
    • (2005) Bioinformatics , vol.21 , Issue.21 , pp. 4026-4032
    • Markowetz, F.1    Bloch, J.2    Spang, R.3
  • 63
    • 38449088751 scopus 로고    scopus 로고
    • Inferring cellular networks-a review
    • Markowetz F., and Spang R. Inferring cellular networks-a review. BMC Bioinform. 8 Suppl. 6 (2007) S5
    • (2007) BMC Bioinform. , vol.8 , Issue.SUPPL. 6
    • Markowetz, F.1    Spang, R.2
  • 64
    • 34248572623 scopus 로고    scopus 로고
    • Boolean dynamics of genetic regulatory networks inferred from microarray time series data
    • Martin S., Zhang Z., Martino A., and Faulon J.L. Boolean dynamics of genetic regulatory networks inferred from microarray time series data. Bioinformatics 23 7 (2007) 866-874
    • (2007) Bioinformatics , vol.23 , Issue.7 , pp. 866-874
    • Martin, S.1    Zhang, Z.2    Martino, A.3    Faulon, J.L.4
  • 65
    • 4644336023 scopus 로고    scopus 로고
    • Revealing the world of RNA interference
    • Mello C.C., and Conte Jr. D. Revealing the world of RNA interference. Nature 431 (2004) 338-342
    • (2004) Nature , vol.431 , pp. 338-342
    • Mello, C.C.1    Conte Jr., D.2
  • 66
    • 2942694772 scopus 로고    scopus 로고
    • Artificial gene networks for objective comparison of analysis algorithms
    • Mendes P., Sha W., and Ye K. Artificial gene networks for objective comparison of analysis algorithms. Bioinformatics 19 Suppl. 2 (2003) ii122-ii129
    • (2003) Bioinformatics , vol.19 , Issue.SUPPL. 2
    • Mendes, P.1    Sha, W.2    Ye, K.3
  • 67
    • 84898940253 scopus 로고    scopus 로고
    • From coexpression to coregulation: an approach to inferring transcriptional regulation among gene classes from large-scale expression data
    • Stolla S.A., Leen T.K., and Muller K.R. (Eds), MIT Press, Cambridge, MA
    • Mjolsness E., Mann T., Castano R., and Wold B. From coexpression to coregulation: an approach to inferring transcriptional regulation among gene classes from large-scale expression data. In: Stolla S.A., Leen T.K., and Muller K.R. (Eds). Advances in Neural Information Processing Systems vol. 12 (2000), MIT Press, Cambridge, MA 928-934
    • (2000) Advances in Neural Information Processing Systems , vol.12 , pp. 928-934
    • Mjolsness, E.1    Mann, T.2    Castano, R.3    Wold, B.4
  • 68
    • 61349168877 scopus 로고    scopus 로고
    • Performance of data resampling methods for robust class discovery based on clustering
    • Moeller U., and Radke D. Performance of data resampling methods for robust class discovery based on clustering. Intell. Data Anal. 10 2 (2006) 139-162
    • (2006) Intell. Data Anal. , vol.10 , Issue.2 , pp. 139-162
    • Moeller, U.1    Radke, D.2
  • 69
    • 0242574982 scopus 로고    scopus 로고
    • Parameter estimation in biochemical pathways: a comparison of global optimization methods
    • Moles C.G., Mendes P., and Banga J.R. Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res. 13 11 (2003) 2467-2474
    • (2003) Genome Res. , vol.13 , Issue.11 , pp. 2467-2474
    • Moles, C.G.1    Mendes, P.2    Banga, J.R.3
  • 70
    • 49549104230 scopus 로고    scopus 로고
    • SIRENE: supervised inference of regulatory networks
    • Mordelet F., and Vert J.P. SIRENE: supervised inference of regulatory networks. Bioinformatics 24 16 (2008) i76-82
    • (2008) Bioinformatics , vol.24 , Issue.16
    • Mordelet, F.1    Vert, J.P.2
  • 71
    • 4243465365 scopus 로고
    • Non-uniqueness and inversions in cluster analysis
    • Morgan B.J.T., and Roy A.P.G. Non-uniqueness and inversions in cluster analysis. Appl. Stat. 44 (1995) 117-134
    • (1995) Appl. Stat. , vol.44 , pp. 117-134
    • Morgan, B.J.T.1    Roy, A.P.G.2
  • 72
    • 2442718023 scopus 로고    scopus 로고
    • Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks
    • Nariai N., Kim S., Imoto S., and Miyano S. Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks. Proceeding of the Pacific Symposium on Biocomputing (2004) 336-347
    • (2004) Proceeding of the Pacific Symposium on Biocomputing , pp. 336-347
    • Nariai, N.1    Kim, S.2    Imoto, S.3    Miyano, S.4
  • 75
    • 34848903220 scopus 로고    scopus 로고
    • From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data
    • Opgen-Rhein R., and Strimmer K. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst. Biol. 1 (2007) 37
    • (2007) BMC Syst. Biol. , vol.1 , pp. 37
    • Opgen-Rhein, R.1    Strimmer, K.2
  • 76
    • 0035999977 scopus 로고    scopus 로고
    • A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments
    • Pan W. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 18 4 (2002) 546-554
    • (2002) Bioinformatics , vol.18 , Issue.4 , pp. 546-554
    • Pan, W.1
  • 77
    • 0034659815 scopus 로고    scopus 로고
    • Proteomics to study genes and genomes
    • Pandey A., and Mann M. Proteomics to study genes and genomes. Nature 405 6788 (2000) 837-846
    • (2000) Nature , vol.405 , Issue.6788 , pp. 837-846
    • Pandey, A.1    Mann, M.2
  • 78
    • 33646901020 scopus 로고    scopus 로고
    • Reverse engineering the gap gene network of Drosophila melanogaster
    • Perkins T.J., Jaeger J., Reinitz J., and Glass L. Reverse engineering the gap gene network of Drosophila melanogaster. PLoS Comput. Biol. 2 5 (2006) e51
    • (2006) PLoS Comput. Biol. , vol.2 , Issue.5
    • Perkins, T.J.1    Jaeger, J.2    Reinitz, J.3    Glass, L.4
  • 80
    • 33646364037 scopus 로고    scopus 로고
    • Identification of metabolic system parameters using global optimization methods
    • Polisetty P.K., Voit E.O., and Gatzke E.P. Identification of metabolic system parameters using global optimization methods. Theor. Biol. Med. Model. 3 (2006) 4
    • (2006) Theor. Biol. Med. Model. , vol.3 , pp. 4
    • Polisetty, P.K.1    Voit, E.O.2    Gatzke, E.P.3
  • 81
    • 0036898577 scopus 로고    scopus 로고
    • Microarray data normalization and transformation
    • Quackenbush J. Microarray data normalization and transformation. Nat. Genet. 32 Suppl. (2002) 496-501
    • (2002) Nat. Genet. , vol.32 , Issue.SUPPL , pp. 496-501
    • Quackenbush, J.1
  • 82
    • 35048877671 scopus 로고    scopus 로고
    • Quantitative evaluation of established clustering methods for gene expression data
    • Radke D., and Möller U. Quantitative evaluation of established clustering methods for gene expression data. Lect. Notes Comput. Sci. 3337 (2004) 399-408
    • (2004) Lect. Notes Comput. Sci. , vol.3337 , pp. 399-408
    • Radke, D.1    Möller, U.2
  • 86
    • 15944367731 scopus 로고    scopus 로고
    • Reconstructing biological networks using conditional correlation analysis
    • Rice J.J., Tu Y., and Stolovitzky G. Reconstructing biological networks using conditional correlation analysis. Bioinformatics 21 6 (2005) 765-773
    • (2005) Bioinformatics , vol.21 , Issue.6 , pp. 765-773
    • Rice, J.J.1    Tu, Y.2    Stolovitzky, G.3
  • 87
    • 32044474451 scopus 로고    scopus 로고
    • A hybrid approach for efficient and robust parameter estimation in biochemical pathways
    • Rodriguez-Fernandez M., Mendes P., and Banga J.R. A hybrid approach for efficient and robust parameter estimation in biochemical pathways. Biosystems 83 2-3 (2006) 248-265
    • (2006) Biosystems , vol.83 , Issue.2-3 , pp. 248-265
    • Rodriguez-Fernandez, M.1    Mendes, P.2    Banga, J.R.3
  • 88
    • 0345928693 scopus 로고    scopus 로고
    • Building and analysing genome-wide gene disruption networks
    • Rung J., Schlitt T., Brazma A., Freivalds K., and Vilo J. Building and analysing genome-wide gene disruption networks. Bioinformatics 18 Suppl. 2 (2002) S202-S210
    • (2002) Bioinformatics , vol.18 , Issue.SUPPL. 2
    • Rung, J.1    Schlitt, T.2    Brazma, A.3    Freivalds, K.4    Vilo, J.5
  • 89
    • 0034863951 scopus 로고    scopus 로고
    • Inferring a system of differential equations for a gene regulatory network by using genetic programming
    • IEEE Press
    • Sakamoto E., and Iba H. Inferring a system of differential equations for a gene regulatory network by using genetic programming. Proceedings of the IEEE Congress on Evolutionary Computation (2001), IEEE Press 720-726
    • (2001) Proceedings of the IEEE Congress on Evolutionary Computation , pp. 720-726
    • Sakamoto, E.1    Iba, H.2
  • 91
    • 0037941585 scopus 로고    scopus 로고
    • Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data
    • Segal E., Shapira M., Regev A., Pe'er D., Botstein D., Koller D., and Friedman N. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34 2 (2003) 166-176
    • (2003) Nat. Genet. , 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
  • 93
    • 0036578795 scopus 로고    scopus 로고
    • Network motifs in the transcriptional regulation network of Escherichia coli
    • Shen-Orr S.S., Milo R., Mangan S., and Alon U. Network motifs in the transcriptional regulation network of Escherichia coli. Nat. Genet. 31 1 (2002) 64-68
    • (2002) Nat. Genet. , vol.31 , Issue.1 , pp. 64-68
    • Shen-Orr, S.S.1    Milo, R.2    Mangan, S.3    Alon, U.4
  • 94
    • 34547844096 scopus 로고    scopus 로고
    • Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data
    • Soranzo N., Bianconi G., and Altarini C. Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data. Bioinformatics 23 13 (2007) 1640-1647
    • (2007) Bioinformatics , vol.23 , Issue.13 , pp. 1640-1647
    • Soranzo, N.1    Bianconi, G.2    Altarini, C.3
  • 97
    • 0042526388 scopus 로고    scopus 로고
    • The mutual information: detecting and evaluating dependencies between variables
    • Steuer R., Kurths J., Daub C.O., Weise J., and Selbig J. The mutual information: detecting and evaluating dependencies between variables. Bioinformatics 18 Suppl. 2 (2002) S231-S240
    • (2002) Bioinformatics , vol.18 , Issue.SUPPL. 2
    • Steuer, R.1    Kurths, J.2    Daub, C.O.3    Weise, J.4    Selbig, J.5
  • 98
    • 36249019789 scopus 로고    scopus 로고
    • Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference
    • Stolovitzky G., Monroe D., and Califano A. Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference. Ann. NY Acad. Sci. 1115 (2007) 1-22
    • (2007) Ann. NY Acad. Sci. , vol.1115 , pp. 1-22
    • Stolovitzky, G.1    Monroe, D.2    Califano, A.3
  • 99
    • 0141993704 scopus 로고    scopus 로고
    • A gene-coexpression network for global discovery of conserved genetic modules
    • Stuart J.M., Segal E., Koller D., and Kim S.K. A gene-coexpression network for global discovery of conserved genetic modules. Science 302 5643 (2003) 249-255
    • (2003) Science , vol.302 , Issue.5643 , pp. 249-255
    • Stuart, J.M.1    Segal, E.2    Koller, D.3    Kim, S.K.4
  • 100
    • 3242891560 scopus 로고    scopus 로고
    • Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection
    • Tamada Y., Kim S., Bannai H., Imoto S., Tashiro K., Kuhara S., and Miyano S. Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection. Bioinformatics 19 Suppl. 2 (2003) ii227-ii236
    • (2003) Bioinformatics , vol.19 , Issue.SUPPL. 2
    • Tamada, Y.1    Kim, S.2    Bannai, H.3    Imoto, S.4    Tashiro, K.5    Kuhara, S.6    Miyano, S.7
  • 101
    • 0037687416 scopus 로고    scopus 로고
    • Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling
    • Tegner J., Yeung M.K.S., Hasty J., and Collins J.J. Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc. Natl. Acad. Sci. U.S.A. 100 10 (2003) 5944-5949
    • (2003) Proc. Natl. Acad. Sci. U.S.A. , vol.100 , Issue.10 , pp. 5944-5949
    • Tegner, J.1    Yeung, M.K.S.2    Hasty, J.3    Collins, J.J.4
  • 102
    • 0015823097 scopus 로고
    • Boolean formalization of genetic control circuits
    • Thomas R. Boolean formalization of genetic control circuits. J. Theor. Biol. 42 3 (1973) 563-585
    • (1973) J. Theor. Biol. , vol.42 , Issue.3 , pp. 563-585
    • Thomas, R.1
  • 103
  • 104
    • 1342286937 scopus 로고    scopus 로고
    • Studying the conditions for learning dynamic Bayesian networks to discover genetic regulatory networks
    • Van Berlo R.J.P., van Someren E.P., and Reinders M.J.T. Studying the conditions for learning dynamic Bayesian networks to discover genetic regulatory networks. Simul.: Trans. Soc. Model. Simul. Int. 79 12 (2003) 689-702
    • (2003) Simul.: Trans. Soc. Model. Simul. Int. , vol.79 , Issue.12 , pp. 689-702
    • Van Berlo, R.J.P.1    van Someren, E.P.2    Reinders, M.J.T.3
  • 105
    • 33845410054 scopus 로고    scopus 로고
    • Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments
    • Van Riel N.A.W. Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments. Brief. Bioinform. (2006) 364-374
    • (2006) Brief. Bioinform. , pp. 364-374
    • Van Riel, N.A.W.1
  • 113
    • 58149268119 scopus 로고    scopus 로고
    • Modelling metabolic networks using power-laws and S-systems
    • Voit E.O. Modelling metabolic networks using power-laws and S-systems. Essays Biochem. 45 (2008) 29-40
    • (2008) Essays Biochem. , vol.45 , pp. 29-40
    • Voit, E.O.1
  • 114
    • 0034104824 scopus 로고    scopus 로고
    • Coarse-grained reverse engineering of genetic regulatory networks
    • Wahde M., and Hertz J. Coarse-grained reverse engineering of genetic regulatory networks. Biosystems 55 (2000) 129-136
    • (2000) Biosystems , vol.55 , pp. 129-136
    • Wahde, M.1    Hertz, J.2
  • 115
    • 33750016109 scopus 로고    scopus 로고
    • Inferring gene regulatory networks from multiple microarray datasets
    • Wang Y., Joshi T., Zhang X.S., Xu D., and Chen L. Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics 22 19 (2006) 2413-2420
    • (2006) Bioinformatics , vol.22 , Issue.19 , pp. 2413-2420
    • Wang, Y.1    Joshi, T.2    Zhang, X.S.3    Xu, D.4    Chen, L.5
  • 116
    • 1842584947 scopus 로고    scopus 로고
    • Applied bioinformatics for the identification of regulatory elements
    • Wasserman W.W., and Sandelin A. Applied bioinformatics for the identification of regulatory elements. Nat. Rev. Genet. 5 (2004) 276-287
    • (2004) Nat. Rev. Genet. , vol.5 , pp. 276-287
    • Wasserman, W.W.1    Sandelin, A.2
  • 118
    • 85194535272 scopus 로고    scopus 로고
    • Werhli, A.V., Husmeier, D., 2007. Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge. Stat. Appl. Genet. Mol. Biol., 6:Article 15.
    • Werhli, A.V., Husmeier, D., 2007. Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge. Stat. Appl. Genet. Mol. Biol., 6:Article 15.
  • 120
    • 85194583267 scopus 로고    scopus 로고
    • International Workshop on Gene Regulatory Network Inference, Jena, Personal Communication
    • Westra, R, 2008. International Workshop on Gene Regulatory Network Inference, Jena, Personal Communication.
    • (2008)
    • Westra, R.1
  • 121
    • 0037197936 scopus 로고    scopus 로고
    • Reverse engineering gene networks using singular value decomposition and robust regression
    • Yeung M.K.S., Tegner J., and Collins J.J. Reverse engineering gene networks using singular value decomposition and robust regression. Proc. Natl. Acad. Sci. U.S.A. 99 9 (2002) 6163-6168
    • (2002) Proc. Natl. Acad. Sci. U.S.A. , vol.99 , Issue.9 , pp. 6163-6168
    • Yeung, M.K.S.1    Tegner, J.2    Collins, J.J.3
  • 122
    • 85194555170 scopus 로고    scopus 로고
    • Yong-A-Poi, J., 2008. Adaptive least Absolute Regression Network Analysis Improves Genetic Network Reconstruction by Employing Prior Knowledge (Master Thesis). Delft University of Technology, The Netherlands.
    • Yong-A-Poi, J., 2008. Adaptive least Absolute Regression Network Analysis Improves Genetic Network Reconstruction by Employing Prior Knowledge (Master Thesis). Delft University of Technology, The Netherlands.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.