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Volumn , Issue , 2010, Pages 277-300

Some Useful Mathematical Tools to Transform Microarray Data into Interactive Molecular Networks

Author keywords

Bayesian networks; Data evaluation; Hierarchical clustering; Microarrays; Principal component analysis

Indexed keywords


EID: 84856338626     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9783527630271.ch13     Document Type: Chapter
Times cited : (5)

References (89)
  • 1
    • 41549159731 scopus 로고    scopus 로고
    • Evolutionary approaches for the reverse-engineering of gene regulatory networks: a study on a biologically realistic dataset
    • Auliac, C., Frouin, V., Gidrol, X., and d'Alché-buc, F. (2008) Evolutionary approaches for the reverse-engineering of gene regulatory networks: a study on a biologically realistic dataset. BMC Bioinformatics, 9, 91.
    • (2008) BMC Bioinformatics , vol.9 , pp. 91
    • Auliac, C.1    Frouin, V.2    Gidrol, X.3    d'Alché-buc, F.4
  • 2
    • 33747882889 scopus 로고    scopus 로고
    • Analysis of variance of microarray data
    • Ayroles, J.F. and Gibson, G. (2006) Analysis of variance of microarray data. Methods in Enzymology, 411, 214-233.
    • (2006) Methods in Enzymology , vol.411 , pp. 214-233
    • Ayroles, J.F.1    Gibson, G.2
  • 3
    • 11144332281 scopus 로고    scopus 로고
    • Learning graphical models with Mercer kernels
    • in Advances in Neural Information Processing Systems 15 (eds. S. Becker, S. Thrun, and K. Obermayer), MIT Press, Cambridge, MA
    • Bach, F.R. and Jordan, M.I. (2002) Learning graphical models with Mercer kernels, in Advances in Neural Information Processing Systems 15 (eds. S. Becker, S. Thrun, and K. Obermayer), MIT Press, Cambridge, MA, pp. 1033-1040.
    • (2002) , pp. 1033-1040
    • Bach, F.R.1    Jordan, M.I.2
  • 4
    • 15944361900 scopus 로고    scopus 로고
    • Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data
    • in Proceedings of the Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, and L.Hunter),World Scientific, Singapore
    • Bernard, A. and Hartemink, A.J. (2005) Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data, in Proceedings of the Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, and L.Hunter),World Scientific, Singapore, pp. 459-470.
    • (2005) , pp. 459-470
    • Bernard, A.1    Hartemink, A.J.2
  • 5
    • 0001926525 scopus 로고
    • Theory refinement of Bayesian networks
    • in Proceedings of the 7th Annual Conference on Uncertainty in Artificial Intelligence (eds. B. D'Ambrosio and P. Smets), Morgan Kaufmann, San Francisco, CA
    • Buntine, W.L. (1991) Theory refinement of Bayesian networks, in Proceedings of the 7th Annual Conference on Uncertainty in Artificial Intelligence (eds. B. D'Ambrosio and P. Smets), Morgan Kaufmann, San Francisco, CA, pp. 52-60.
    • (1991) , pp. 52-60
    • Buntine, W.L.1
  • 6
    • 42149187635 scopus 로고    scopus 로고
    • Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees
    • Chen, X. and Blanchette, M. (2008) Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees. BMC Bioinformatics, 8, S2.
    • (2008) BMC Bioinformatics , vol.8
    • Chen, X.1    Blanchette, M.2
  • 7
    • 0031345619 scopus 로고    scopus 로고
    • Learning belief networks from data: an information theory based approach
    • in Proceedings of the Sixth International Conference on Information and Knowledge Management, ACM Press, New York
    • Cheng, J., Bell, D.A., and Liu, W. (1997) Learning belief networks from data: an information theory based approach, in Proceedings of the Sixth International Conference on Information and Knowledge Management, ACM Press, New York, pp. 325-331.
    • (1997) , pp. 325-331
    • Cheng, J.1    Bell, D.A.2    Liu, W.3
  • 8
    • 0001019707 scopus 로고    scopus 로고
    • Learning Bayesian networks is NP-complete
    • in Learning from Data: AI and Statistics V (eds. D. Fisher and H-.J. Lenz), Springer, NewYork
    • Chickering, D.M. (1996) Learning Bayesian networks is NP-complete, in Learning from Data: AI and Statistics V (eds. D. Fisher and H-.J. Lenz), Springer, NewYork, pp. 121-130.
    • (1996) , pp. 121-130
    • Chickering, D.M.1
  • 10
    • 34249832377 scopus 로고
    • A Bayesian method for the induction of probabilistic networks from data
    • Cooper, G.F. and Herskovits, E. (1992) A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309-7.
    • (1992) Machine Learning , vol.9 , pp. 309-307
    • Cooper, G.F.1    Herskovits, E.2
  • 11
    • 0007047929 scopus 로고    scopus 로고
    • Causal discovery from a mixture of experimental and observational data
    • in Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (eds. K. Laskey and H. Prade), Morgan Kaufman, San Francisco, CA
    • Cooper, G.F. and Yoo, C. (1999) Causal discovery from a mixture of experimental and observational data, in Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (eds. K. Laskey and H. Prade), Morgan Kaufman, San Francisco, CA, pp. 116-125.
    • (1999) , pp. 116-125
    • Cooper, G.F.1    Yoo, C.2
  • 12
    • 34948848213 scopus 로고    scopus 로고
    • Conditions under which conditional independence and scoring methods lead to identical selection of Bayesian network models
    • in Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA
    • Cowell, R.G. (2001) Conditions under which conditional independence and scoring methods lead to identical selection of Bayesian network models, in Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp. 91-97.
    • (2001) , pp. 91-97
    • Cowell, R.G.1
  • 13
    • 0032612220 scopus 로고    scopus 로고
    • Linear modeling of mRNA expression levels during CNS development and injury
    • in Proceedings of the Pacific Symposium on Biocomputing (eds. R.B. Altman and K. Lauderdale), World Scientific, Singapore
    • D'haeseleer, P., Wen, X., Fuhrman, S., and Somogyi, S.R. (1999) Linear modeling of mRNA expression levels during CNS development and injury, in Proceedings of the Pacific Symposium on Biocomputing (eds. R.B. Altman and K. Lauderdale), World Scientific, Singapore, pp. 41-52.
    • (1999) , pp. 41-52
    • D'haeseleer, P.1    Wen, X.2    Fuhrman, S.3    Somogyi, S.R.4
  • 14
    • 33645510456 scopus 로고    scopus 로고
    • Computational modeling of the Plasmodium falciparum interactome reveals protein function on a genome-wide scale
    • Date, S.V. and Stoeckert, C.J. Jr. (2006) Computational modeling of the Plasmodium falciparum interactome reveals protein function on a genome-wide scale. Genome Research, 16, 542-549.
    • (2006) Genome Research , vol.16 , pp. 542-549
    • Date, S.V.1    Stoeckert Jr, C.J.2
  • 15
    • 4544310280 scopus 로고    scopus 로고
    • Hunting drug targets by systems-level modeling of gene expression profiles
    • Dejori, M., Schuermann, B., and Stetter, M. (2004)Hunting drug targets by systems-level modeling of gene expression profiles. IEEE Transactions on Nanobioscience, 3, 180-191.
    • (2004) IEEE Transactions on Nanobioscience , vol.3 , pp. 180-191
    • Dejori, M.1    Schuermann, B.2    Stetter, M.3
  • 16
    • 48249140218 scopus 로고    scopus 로고
    • Seeded Bayesian networks: Constructing genetic networks from microarray data
    • Djebbari, A. and Quackenbush, J. (2008) Seeded Bayesian networks: Constructing genetic networks from microarray data. BMC Systems Biology, 2, 57.
    • (2008) BMC Systems Biology , vol.2 , pp. 57
    • Djebbari, A.1    Quackenbush, J.2
  • 19
    • 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
  • 20
    • 0037262841 scopus 로고    scopus 로고
    • Being Bayesian about network structure: a Bayesian approach to structure discovery in Bayesian networks
    • Friedman, N. and Koller, D. (2003) Being Bayesian about network structure: a Bayesian approach to structure discovery in Bayesian networks. Machine Learning, 50, 95-7.
    • (2003) Machine Learning , vol.50 , pp. 95-97
    • Friedman, N.1    Koller, D.2
  • 21
    • 0000854197 scopus 로고    scopus 로고
    • Learning the structure of dynamic probabilistic networks
    • in Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence (eds. G.F. Cooper, and S. Moral), Morgan Kaufmann, San Francisco, CA
    • Friedman, N., Murphy, K., and Russell, S. (1998) Learning the structure of dynamic probabilistic networks, in Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence (eds. G.F. Cooper, and S. Moral), Morgan Kaufmann, San Francisco, CA, pp. 139-147.
    • (1998) , pp. 139-147
    • Friedman, N.1    Murphy, K.2    Russell, S.3
  • 23
    • 70449464202 scopus 로고    scopus 로고
    • Data Clustering: Theory, Algorithms, and Applications, ASA/SIAM Series on Statistics and Applied Probability
    • Gan, G., Ma, C., and Wu, J. (2007) Data Clustering: Theory, Algorithms, and Applications, ASA/SIAM Series on Statistics and Applied Probability, SIAM, Philadelphia, PA.
    • (2007) SIAM, Philadelphia, PA
    • Gan, G.1    Ma, C.2    Wu, J.3
  • 24
    • 54749132018 scopus 로고    scopus 로고
    • Transcriptional changes in insulinand lipid metabolism-related genes in the hippocampus of olfactory bulbectomized mice
    • Gass, P., Leonardi-Essmann, F., Zueger, M., Spanagel, R., and Gebicke-Haerter, P.J. (2008) Transcriptional changes in insulinand lipid metabolism-related genes in the hippocampus of olfactory bulbectomized mice. Journal of Neuroscience Research, 86: 3184-3193.
    • (2008) Journal of Neuroscience Research , vol.86 , pp. 3184-3193
    • Gass, P.1    Leonardi-essmann, F.2    Zueger, M.3    Spanagel, R.4    Gebicke-haerter, P.J.5
  • 25
    • 33747891871 scopus 로고    scopus 로고
    • Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks
    • Gevaert, O., De Smet, F., Timmerman, D., Moreau, Y., and De Moor, B. (2006) Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks. Bioinformatics, 22, e184-7.
    • (2006) Bioinformatics , vol.22
    • Gevaert, O.1    De Smet, F.2    Timmerman, D.3    Moreau, Y.4    De Moor, B.5
  • 26
    • 65449134730 scopus 로고    scopus 로고
    • GeNGe: systematic generation of gene regulatory networks
    • Hache, H., Wierling, C., Lehrach, H., and Herwig, R. (2009) GeNGe: systematic generation of gene regulatory networks. Bioinformatics, 25, 1205-7.
    • (2009) Bioinformatics , vol.25 , pp. 1205-1207
    • Hache, H.1    Wierling, C.2    Lehrach, H.3    Herwig, R.4
  • 28
    • 33644695322 scopus 로고    scopus 로고
    • Increasing feasibility of optimal gene network estimation
    • Hansen, A., Ott, S., and Koentges, G. (2004) Increasing feasibility of optimal gene network estimation. Genome Informatics, 15, 141-7.
    • (2004) Genome Informatics , vol.15 , pp. 141-147
    • Hansen, A.1    Ott, S.2    Koentges, G.3
  • 29
    • 0035221560 scopus 로고    scopus 로고
    • Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks
    • in Proceedings of the Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, L. Hunker, K. Lauderdale, and T.E.D. Klein), World Scientific, Singapore
    • Hartemink, A.J., Gifford, D.K., Jaakkola, T.S., and Young, R.A. (2001) Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks, in Proceedings of the Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, L. Hunker, K. Lauderdale, and T.E.D. Klein), World Scientific, Singapore, pp. 422-433.
    • (2001) , pp. 422-433
    • Hartemink, A.J.1    Gifford, D.K.2    Jaakkola, T.S.3    Young, R.A.4
  • 30
    • 0036366689 scopus 로고    scopus 로고
    • Combining location and expression data for principled discovery of genetic regulatory network models
    • in Proceedings of Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, L. Hunter, and K. Lauderdale), World Scientific, Singapore
    • Hartemink, A.J., Gifford, D.K., Jaakkola, T.S., and Young, R.A. (2002) Combining location and expression data for principled discovery of genetic regulatory network models, in Proceedings of Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, L. Hunter, and K. Lauderdale), World Scientific, Singapore, pp. 437-449.
    • (2002) , pp. 437-449
    • Hartemink, A.J.1    Gifford, D.K.2    Jaakkola, T.S.3    Young, R.A.4
  • 33
    • 0344464762 scopus 로고    scopus 로고
    • Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks
    • Husmeier, D. (2003) Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics, 19, 2271-7.
    • (2003) Bioinformatics , vol.19 , pp. 2271-2277
    • Husmeier, D.1
  • 34
    • 0036372453 scopus 로고    scopus 로고
    • Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression
    • in Proceedings of Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, L. Hunter, and K. Lauderdale), World Scientific, Singapore
    • Imoto, S., Goto, T., and Miyano, S. (2002) Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression, in Proceedings of Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, L. Hunter, and K. Lauderdale), World Scientific, Singapore, pp. 175-186.
    • (2002) , pp. 175-186
    • Imoto, S.1    Goto, T.2    Miyano, S.3
  • 37
    • 33746616957 scopus 로고    scopus 로고
    • Computational strategy for discovering druggable gene networks from genomewide RNA expression profiles
    • in Proceedings of the Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, L.Hunter, T. Murray, and T.E. Klein), World Scientific, Singapore
    • Imoto, S., Tamada, Y., Araki, H., Yasuda, K., Print, C.G., Charnock-Jones, S.D., Sanders, D., Savoie, C.J., Tashiro, K., Kuhara, S., and Miyano, S. (2006) Computational strategy for discovering druggable gene networks from genomewide RNA expression profiles, in Proceedings of the Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, L.Hunter, T. Murray, and T.E. Klein), World Scientific, Singapore, pp. 559-571.
    • (2006) , pp. 559-571
    • Imoto, S.1    Tamada, Y.2    Araki, H.3    Yasuda, K.4    Print, C.G.5    Charnock-jones, S.D.6    Sanders, D.7    Savoie, C.J.8    Tashiro, K.9    Kuhara, S.10    Miyano, S.11
  • 38
    • 0003946510 scopus 로고    scopus 로고
    • Principal Component Analysis
    • 2nd edn, Springer Series in Statistics, Springer, New York
    • Jolliffe, I.T. (2002) Principal Component Analysis, 2nd edn, Springer Series in Statistics, Springer, New York. Jung, Y., Park, H., Du, D.-Z., and Drake, B.L. (2003) A decision criterion for the optimal number of clusters in hierarchical clustering. Journal of Global Optimization, 25, 91-111.
    • (2002) , vol.25 , pp. 91-111
    • Jolliffe, I.T.1
  • 39
    • 84867984200 scopus 로고    scopus 로고
    • A decision criterion for the optimal number of clusters in hierarchical clustering
    • Jung, Y., Park, H., Du, D.-Z., and Drake, B.L. (2003) A decision criterion for the optimal number of clusters in hierarchical clustering. Journal of Global Optimization
    • (2003) Journal of Global Optimization
    • Jung, Y.1    Park, H.2    Du, D.-Z.3    Drake, B.L.4
  • 40
    • 0035032570 scopus 로고    scopus 로고
    • Statistical design and the analysis of gene expression microarrays
    • Kerr, M.K. and Churchill, G.A. (2001) Statistical design and the analysis of gene expression microarrays. Genetical Research, 77, 123-7.
    • (2001) Genetical Research , vol.77 , pp. 123-127
    • Kerr, M.K.1    Churchill, G.A.2
  • 42
    • 3042738945 scopus 로고    scopus 로고
    • Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data
    • Kim, S., Imoto, S., and Miyano, S. (2004) Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. BioSystems, 75, 57-65.
    • (2004) BioSystems , vol.75 , pp. 57-65
    • Kim, S.1    Imoto, S.2    Miyano, S.3
  • 43
    • 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, 228-7.
    • (2003) Briefings in Bioinformatics , vol.4 , pp. 228-227
    • Kim, S.Y.1    Imoto, S.2    Miyano, S.3
  • 45
    • 20744441547 scopus 로고    scopus 로고
    • Modularized learning of genetic interaction networks from biological annotations and mRNA expression data
    • Lee, P.H. and Lee, D. (2005) Modularized learning of genetic interaction networks from biological annotations and mRNA expression data. Bioinformatics, 21, 2739-7.
    • (2005) Bioinformatics , vol.21 , pp. 2739-2737
    • Lee, P.H.1    Lee, D.2
  • 46
    • 48749127708 scopus 로고    scopus 로고
    • Expression profiling analysis for genes related to meat quality and carcass traits during postnatal development of backfat in two pig breeds
    • Li, M., Zhu, L., Li, X., Shuai, S., Teng, X., Xiao, H., Li, Q., Chen, L., Guo, Y., and Wang, J. (2008) Expression profiling analysis for genes related to meat quality and carcass traits during postnatal development of backfat in two pig breeds. Science in China C: Life Sciences, 51, 718-7.
    • (2008) Science in China C: Life Sciences , vol.51 , pp. 718-717
    • Li, M.1    Zhu, L.2    Li, X.3    Shuai, S.4    Teng, X.5    Xiao, H.6    Li, Q.7    Chen, L.8    Guo, Y.9    Wang, J.10
  • 48
    • 34547190103 scopus 로고    scopus 로고
    • Uncovering gene regulatory networks from time-series microarray data with variational Bayesian structural expectation maximization
    • Luna, T., Huang, Y., Yin, Y., Padillo, D.P.R., and Perez, M.C.C. (2007) Uncovering gene regulatory networks from time-series microarray data with variational Bayesian structural expectation maximization. EURASIP Journal on Bioinformatics and Systems Biology, 71312.
    • (2007) EURASIP Journal on Bioinformatics and Systems Biology , pp. 71312
    • Luna, T.1    Huang, Y.2    Yin, Y.3    Padillo, D.P.R.4    Perez, M.C.C.5
  • 49
    • 58149349958 scopus 로고    scopus 로고
    • Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual information
    • Luo, W., Hankenson, K.D., and Woolf, P.J. (2008) Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual information. BMC Bioinformatics, 9, 467.
    • (2008) BMC Bioinformatics , vol.9 , pp. 467
    • Luo, W.1    Hankenson, K.D.2    Woolf, P.J.3
  • 50
    • 29344470807 scopus 로고    scopus 로고
    • Distribution-free learning of Bayesian network structure in continuous domains
    • in Proceedings of the 20th National Conference on Artificial Intelligence, AAAI, Menlo Park, CA
    • Margaritis, D. (2005) Distribution-free learning of Bayesian network structure in continuous domains, in Proceedings of the 20th National Conference on Artificial Intelligence, AAAI, Menlo Park, CA, pp. 825-830.
    • (2005) , pp. 825-830
    • Margaritis, D.1
  • 51
    • 38449088751 scopus 로고    scopus 로고
    • Inferring cellular networks - a review
    • Markowetz, F. and Spang, R. (2007) Inferring cellular networks - a review. BMC Bioinformatics, 8, S5.
    • (2007) BMC Bioinformatics , vol.8
    • Markowetz, F.1    Spang, R.2
  • 53
    • 34250115918 scopus 로고
    • An examination of procedures for determining the number of clusters in a data set
    • Milligan, G.W. and Cooper, M.C. (1985) An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50, 159-179.
    • (1985) Psychometrika , vol.50 , pp. 159-179
    • Milligan, G.W.1    Cooper, M.C.2
  • 54
    • 0004158155 scopus 로고    scopus 로고
    • Modeling gene expression data using dynamic Bayesian networks
    • University of California, Berkeley, CA
    • Murphy, K. and Mian, S. (1999) Modeling gene expression data using dynamic Bayesian networks. Technical Report, Computer Science Division, University of California, Berkeley, CA.
    • (1999) Technical Report, Computer Science Division
    • Murphy, K.1    Mian, S.2
  • 55
    • 2442718023 scopus 로고    scopus 로고
    • Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks
    • in Proceedings of the Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, L. Hunter, and T.A. Jung), World Scientific, Singapore
    • Nariai, N., Kim, S., Imoto, S., and Miyano, S. (2004) Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks, in Proceedings of the Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, L. Hunter, and T.A. Jung), World Scientific, Singapore, pp. 336-347.
    • (2004) , pp. 336-347
    • Nariai, N.1    Kim, S.2    Imoto, S.3    Miyano, S.4
  • 58
    • 2442703194 scopus 로고    scopus 로고
    • Finding optimal models for small gene networks
    • in Proceedings of the Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, L. Hunter, T.A. Jung, and T.E.D. Klein), World Scientific, Singapore
    • Ott, S., Imoto, S., and Miyano, S. (2004) Finding optimal models for small gene networks, in Proceedings of the Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, L. Hunter, T.A. Jung, and T.E.D. Klein), World Scientific, Singapore, pp. 557-567.
    • (2004) , pp. 557-567
    • Ott, S.1    Imoto, S.2    Miyano, S.3
  • 59
    • 43549125744 scopus 로고    scopus 로고
    • A network analysis of the human T-cell activation gene network identifies Jagged1 as a therapeutic target for autoimmune diseases
    • Palacios, R., Goni, J., Martinez-Forero, I., Iranzo, J., Sepulcre, J., Melero, I., and Villoslada, P. (2007) A network analysis of the human T-cell activation gene network identifies Jagged1 as a therapeutic target for autoimmune diseases. PLoS ONE, 2, e1222.
    • (2007) PLoS ONE , vol.2
    • Palacios, R.1    Goni, J.2    Martinez-forero, I.3    Iranzo, J.4    Sepulcre, J.5    Melero, I.6    Villoslada, P.7
  • 60
    • 18144442687 scopus 로고    scopus 로고
    • Inferring subnetworks from perturbed expression profiles
    • Pe'er, D., Regev, A., Elidan, G., and Friedman, N. (2001) Inferring subnetworks from perturbed expression profiles. Bioinformatics, 17, S215-7.
    • (2001) Bioinformatics , vol.17
    • Pe'er, D.1    Regev, A.2    Elidan, G.3    Friedman, N.4
  • 61
    • 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, S258-7.
    • (2002) Bioinformatics , vol.18
    • Pe'er, D.1    Regev, A.2    Tanay, A.3
  • 62
    • 0005324651 scopus 로고
    • Probabilistic Reasoning in Intelligent Systems
    • San Francisco, CA
    • Pearl, J. (1988) Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann, San Francisco, CA.
    • (1988) Morgan Kaufmann
    • Pearl, J.1
  • 64
    • 39449106623 scopus 로고    scopus 로고
    • Advancing the understanding of the embryo transcriptome co-regulation using meta-, functional, and gene network analysis tools
    • Rodriguez-Zas, S.L., Ko, Y., Adams, H.A., and Southey, B.R. (2008) Advancing the understanding of the embryo transcriptome co-regulation using meta-, functional, and gene network analysis tools. Reproduction, 135, 213-7.
    • (2008) Reproduction , vol.135 , pp. 213-217
    • Rodriguez-zas, S.L.1    Ko, Y.2    Adams, H.A.3    Southey, B.R.4
  • 65
    • 0037258201 scopus 로고    scopus 로고
    • Use of gene networks from full genome microarray libraries to identify functionally relevant drug-affected genes and gene regulation cascades
    • Savoie, C.J., Aburatani, S., Watanabe, S., Eguchi, Y., Muta, S., Imoto, S., Miyano, S., Kuhara, S., and Tashiro, K. (2003) Use of gene networks from full genome microarray libraries to identify functionally relevant drug-affected genes and gene regulation cascades. DNA Research, 10, 19-25.
    • (2003) DNA Research , vol.10 , pp. 19-25
    • Savoie, C.J.1    Aburatani, S.2    Watanabe, S.3    Eguchi, Y.4    Muta, S.5    Imoto, S.6    Miyano, S.7    Kuhara, S.8    Tashiro, K.9
  • 66
    • 34548427938 scopus 로고    scopus 로고
    • Moving toward a system genetics view of disease
    • Sieberts, S.K., and Schadt, E.E. (2007) Moving toward a system genetics view of disease. Mammalian Genome, 18, 389-401.
    • (2007) Mammalian Genome , vol.18 , pp. 389-401
    • Sieberts, S.K.1    Schadt, E.E.2
  • 67
    • 28644440465 scopus 로고    scopus 로고
    • Combined static and dynamic analysis for determining the quality of time-series expression profiles
    • Simon, I., Siegfried, Z., Ernst, J., and Bar-Joseph, Z. (2005) Combined static and dynamic analysis for determining the quality of time-series expression profiles. Nature Biotechnology, 23, 1503-7.
    • (2005) Nature Biotechnology , vol.23 , pp. 1503-1507
    • Simon, I.1    Siegfried, Z.2    Ernst, J.3    Bar-joseph, Z.4
  • 68
    • 0000042837 scopus 로고    scopus 로고
    • Evaluating functional network inference using simulations of complex biological systems
    • Smith, V.A., Jarvis, E.D., and Hartemink, A.J. (2002) Evaluating functional network inference using simulations of complex biological systems. Bioinformatics, 18, S216-7.
    • (2002) Bioinformatics , vol.18
    • Smith, V.A.1    Jarvis, E.D.2    Hartemink, A.J.3
  • 69
    • 0041627865 scopus 로고    scopus 로고
    • Influence of network topology and data collection on network inference
    • in Proceedings of the Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, L. Hunter, and T.A. Jung), World Publishing, Singapore
    • Smith, V.A., Jarvis, E.D., and Hartemink, A.J. (2003) Influence of network topology and data collection on network inference, in Proceedings of the Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, L. Hunter, and T.A. Jung), World Publishing, Singapore, pp. 164-175.
    • (2003) , pp. 164-175
    • Smith, V.A.1    Jarvis, E.D.2    Hartemink, A.J.3
  • 71
    • 0002979137 scopus 로고
    • An algorithm for fast recovery of sparse causal graphs
    • Spirtes, P., and Glymour, C. (1991) An algorithm for fast recovery of sparse causal graphs. Social Science Computing Reviews, 9, 62-7.
    • (1991) Social Science Computing Reviews , vol.9 , pp. 62-67
    • Spirtes, P.1    Glymour, C.2
  • 72
    • 34547852231 scopus 로고    scopus 로고
    • Computational modeling of Caenorhabditis elegans vulval induction
    • Sun, X., and Hong, P. (2007) Computational modeling of Caenorhabditis elegans vulval induction. Bioinformatics, 23, i499-7.
    • (2007) Bioinformatics , vol.23
    • Sun, X.1    Hong, P.2
  • 73
    • 0003021797 scopus 로고
    • A construction of Bayesian networks from databases based on an MDL principle
    • in Proceedings of the 9th Annual Conference on Uncertainty in Artificial Intelligence (eds. D. Heckerman and E.H. Mamdani), Morgan Kaufmann, San Francisco, CA
    • Suzuki, J. (1993) A construction of Bayesian networks from databases based on an MDL principle, in Proceedings of the 9th Annual Conference on Uncertainty in Artificial Intelligence (eds. D. Heckerman and E.H. Mamdani), Morgan Kaufmann, San Francisco, CA, pp. 266-273.
    • (1993) , pp. 266-273
    • Suzuki, J.1
  • 76
    • 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. (2003) Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection. Bioinformatics, 19, ii227-7.
    • (2003) Bioinformatics , vol.19
    • Tamada, Y.1    Kim, S.2    Bannai, H.3    Imoto, S.4    Tashiro, K.5    Kuhara, S.6    Miyano, S.7
  • 78
    • 4444244398 scopus 로고    scopus 로고
    • Gene interaction network suggests dioxin induces a significant linkage between aryl hydrocarbon receptor and retinoic acid receptor beta
    • Toyoshiba, H., Yamanaka, T., Sone, H., Parham, F.M., Walker, N.J.,Martinez, J., and Portier, C.J. (2004) Gene interaction network suggests dioxin induces a significant linkage between aryl hydrocarbon receptor and retinoic acid receptor beta. Environmental Health Perspectives, 112, 1217-7.
    • (2004) Environmental Health Perspectives , vol.112 , pp. 1217-1217
    • Toyoshiba, H.1    Yamanaka, T.2    Sone, H.3    Parham, F.M.4    Walker, N.J.5    Martinez, J.6    Portier, C.J.7
  • 79
    • 2542430932 scopus 로고    scopus 로고
    • Singular value decomposition and principal component analysis
    • in A Practical Approach to Microarray Data Analysis (eds. D.P. Berrar, W. Dubitzky, and M. Granzow), Kluwer, Norwell, MA
    • Wall, M.E., Rechtsteiner, A., and Rocha, L.M. (2003) Singular value decomposition and principal component analysis, in A Practical Approach to Microarray Data Analysis (eds. D.P. Berrar, W. Dubitzky, and M. Granzow), Kluwer, Norwell, MA, pp. 91-109.
    • (2003) , pp. 91-109
    • Wall, M.E.1    Rechtsteiner, A.2    Rocha, L.M.3
  • 80
    • 0000538525 scopus 로고    scopus 로고
    • Causal discovery via MML, Proceedings of the 13th International Conference on Machine Learning
    • ed. L. Saitta), Morgan Kauffman, San Francisco, CA
    • Wallace, C., Korb, K.B., and Dai, H. (1996) Causal discovery via MML, Proceedings of the 13th International Conference on Machine Learning (ed. L. Saitta), Morgan Kauffman, San Francisco, CA, pp. 516-524.
    • (1996) , pp. 516-524
    • Wallace, C.1    Korb, K.B.2    Dai, H.3
  • 81
    • 34948816667 scopus 로고    scopus 로고
    • A hybrid Bayesian network learning method for constructing gene networks
    • Wang, M., Chen, Z., and Cloutier, S. (2007) A hybrid Bayesian network learning method for constructing gene networks. Computational Biology and Chemistry, 31, 361-7.
    • (2007) Computational Biology and Chemistry , vol.31 , pp. 361-367
    • Wang, M.1    Chen, Z.2    Cloutier, S.3
  • 82
    • 14044254184 scopus 로고    scopus 로고
    • Applying two-level simulated annealing on Bayesian structure learning to infer genetic networks
    • in Proceedings of the IEEE Computational Systems Bioinformatics Conference, IEEE, New York
    • Wang, T., Touchman, J.W., and Xue, G. (2004) Applying two-level simulated annealing on Bayesian structure learning to infer genetic networks, in Proceedings of the IEEE Computational Systems Bioinformatics Conference, IEEE, New York, pp. 647-648.
    • (2004) , pp. 647-648
    • Wang, T.1    Touchman, J.W.2    Xue, G.3
  • 83
    • 0032617396 scopus 로고    scopus 로고
    • Modeling regulatory networks with weight matrices
    • in Proceedings of the Pacific Symposium on Biocomputing (eds. R.B. Altman and K. Lauderdale), World Scientific, Singapore
    • Weaver, D.C., Workman, C.T., and Stromo, G.D. (1999) Modeling regulatory networks with weight matrices, in Proceedings of the Pacific Symposium on Biocomputing (eds. R.B. Altman and K. Lauderdale), World Scientific, Singapore, pp. 112-123.
    • (1999) , pp. 112-123
    • Weaver, D.C.1    Workman, C.T.2    Stromo, G.D.3
  • 84
    • 34249774309 scopus 로고    scopus 로고
    • Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge
    • Werhli, A.V. and Husmeier, D. (2007) Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge. Statistical Applications in Genetics and Molecular Biology, 6, 15.
    • (2007) Statistical Applications in Genetics and Molecular Biology , vol.6 , pp. 15
    • Werhli, A.V.1    Husmeier, D.2
  • 86
    • 0036358442 scopus 로고    scopus 로고
    • Discovery of causal relationships in a generegulation pathway from a mixture of experimental and observational DNA microarray data
    • in Proceedings of Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, L. Hunter, and K. Lauderdale), World Scientific, Singapore
    • Yoo, C., Thorsson, V., and Cooper, G.F. (2002) Discovery of causal relationships in a generegulation pathway from a mixture of experimental and observational DNA microarray data, in Proceedings of Pacific Symposium on Biocomputing (eds. R.B. Altman, A.K. Dunker, L. Hunter, and K. Lauderdale), World Scientific, Singapore, pp 498-509.
    • (2002) , pp. 498-509
    • Yoo, C.1    Thorsson, V.2    Cooper, G.F.3
  • 88
    • 54949116534 scopus 로고    scopus 로고
    • A Bayesian network driven approach to model the transcriptional response to nitric oxide in Saccharomyces cerevisiae
    • Zhu, J., Jambhekar, A., Sarver, A., and DeRis, J. (2006) A Bayesian network driven approach to model the transcriptional response to nitric oxide in Saccharomyces cerevisiae. PLoS ONE, 1, e94.
    • (2006) PLoS ONE , vol.1
    • Zhu, J.1    Jambhekar, A.2    Sarver, A.3    DeRis, J.4
  • 89
    • 12744261506 scopus 로고    scopus 로고
    • A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data
    • Zou, M. and Conzen, S.D. (2005) A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics, 21, 71-7.
    • (2005) Bioinformatics , vol.21 , pp. 71-77
    • Zou, M.1    Conzen, S.D.2


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