Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection

Bioinformatics

Volumn 19, Issue SUPPL. 2, 2003, Pages

  Tamada, Yoshinori ^a,c   Kim, SunYong ^a   Bannai, Hideo ^a   Imoto, Seiya ^a   Tashiro, Kousuke ^b   Kuhara, Satoru ^b   Miyano, Satoru ^a  

^a THE UNIVERSITY OF TOKYO   (Japan)

^b KYUSHU UNIVERSITY   (Japan)

^c KYOTO UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

TRANSCRIPTION FACTOR;

ALGORITHM; BAYES THEOREM; CONFERENCE PAPER; DNA MICROARRAY; DNA SEQUENCE; GENE EXPRESSION; GENETIC MODEL; HUMAN; MATHEMATICAL ANALYSIS; MONTE CARLO METHOD; NONHUMAN; PRIORITY JOURNAL; PROMOTER REGION; SACCHAROMYCES CEREVISIAE; STATISTICAL ANALYSIS;

SACCHAROMYCES CEREVISIAE;

EID: 3242891560     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btg1082     Document Type: Conference Paper

References (36)

1. Akutsu, T., Kuhara, S., Maruyama, O. and Miyano, S. (2003) Identification of gene regulatory networks by strategic gene disruptions and gene overexpressions. Theor. Comput. Sci., 298, 235-251.
2. (Preliminary version has appeared in Proc. 9th ACM-SIAM Symp. Discrete Algorithms, 56, 695-702, 1998)
3. Akutsu, T., Miyano, S. and Kuhara, S. (1999) Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. Pac. Symp. Biocomput., 4, 17-28.
4. Akutsu, T., Miyano, S. and Kuhara, S. (2000a) Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function. J. Comp. Biol., 7, 331-344.
5. Akutsu, T., Miyano, S. and Kuhara, S. (2000b) Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics, 16, 727-734.
6. Bannai, H., Inenaga, S., Shinohara, A., Takeda, M. and Miyano, S. (2002) A string pattern regression algorithm and its application to pattern discovery in long introns. Genome Informatics, 13, 3-11.
7. Bannai, H., Inenaga, S., Shinohara, A., Takeda, M. and Miyano, S. (2003) Efficiently finding regulatory elements using correlation with gene expression, submitted for publication
8. Boros, J., Lim, F.L., Darieva, Z., Pic-Taylor, A., Harman, R., Morgan, B.A. and Sharrocks, A.D. (2003) Molecular determinants of the cell-cycle regulated Mcm1p-Fkh2p transcription factor complex. Nucleic Acids Res., 31, 2279-2288.
9. Brazma, A., Jonassen, I., Vilo, J. and Ukkonen, E. (1998) Predicting gene regulatory elements in silica on a genomic scale. Genome Res., 8, 1202-1215.
10. Bussemaker, H.J., Li, H. and Siggia, E.D. (2001) Regulatory element detection using correlation with expression. Nat. Genet., 27, 167-171.
11. Chen, T., He, H.L. and Church, G.M. (1999) Modeling gene expression with differential equations. Pac. Symp. Biocomput., 4, 29-40.
12. Cooper, G. and Herskovits, E. (1992) A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309-347.
13. Davison, A.C. (1986) Approximate predictive likelihood. Biometrika, 73, 323-332.
14. De Hoon, M.J.L., Imoto, S., Kobayashi, K., Ogasawara, N. and Miyano, S. (2003) Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations. Pac. Symp. Biocomput., 8, 17-28.
15. Friedman, N. and Goldszmidt, M. (1998) Learning Bayesian Networks with Local Structure. Jordan, M.I. (ed.), Kluwer Academic Publisher, pp. 421-459.
16. Friedman, N., Linial, M., Nachman, I. and Pe'er, D. (2000) Using Bayesian network to analyze expression data. J. Comp. Biol., 7, 601-620.
17. 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. Pac. Symp. Biocomput., 6, 422-433.
18. 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. Pac. Symp. Biocomput., 7, 437-449.
19. Heckerman, D. (1998) A Tutorial on Learning with Bayesian Networks. Jordan, M.I. (ed.), Kluwer Academic Publisher, pp. 301-354.
20. Holmes, I. and Bruno, W.J. (2000) Finding regulatory elements using joint likelihoods for sequence and expression profile data. In Proceedings of Intelligent Systems for Molecular Biology. pp. 202-210.
21. Imoto, S., Goto, T. and Miyano, S. (2002) Estimation of genetic networks and functional structures between genes by using Bayesian network and nonparametric regression. Pac. Symp. Biocomput., 7, 175-186.
22. Imoto, S., Kim, S., Goto, T., Aburatani, S., Tashiro, K., Kuhara, S. and Miyano, S. (2003a) Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. J. Bioinform. Comput. Biol., in press.
23. (Preliminary version has appeared in Proc. 1st IEEE Computer Society Bioinformatics Conference, 219-227, 2002)
24. Imoto, S., Higuchi, T., Goto, T. and Miyano, S. (2003b) Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. In Proceedings of 2nd IEEE Computer Society Bioinformatics Conference, in press.
25. Keleş, S., van der Laan, M. and Eisen, M.B. (2002) Identification of regulatory elements using a feature selection method. Bioinformatics, 18, 1167-1175.
26. Kim, S., Imoto, S. and Miyano, S. (2003) Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. In Proceedings of the 1st International Workshop on Computational Methods in Systems Biology, Lecture Note in Computer Science, 2602, Springer, pp. 104-113.
27. Konishi, S., Ando, T. and Imoto, S. (2003) Bayesian information criteria and smoothing parameter selection in radial basis function networks, submitted for publication
28. Maki, Y., Tominaga, D., Okamoto, M., Watanabe, S. and Eguchi, Y. (2001) Development of a system for the inference of large scale genetic networks. Pac. Symp. Biocomput., 6, 446-458.
29. Mewes, H.W., Frishman, D., Güldener, U., Mannhaupt, G., Mayer, K., Mokrejs, M., Morgenstern, B., Münsterkoetter, M., Rudd, S. and Weil, B. (2002) MIPS: a database for genomes and protein sequences. Nucleic Acids Res., 30, 31-34.
30. Pe'er, D., Regev, A., Elidan, G. and Friedman, N. (2001) Inferring subnetworks from perturbed expression profiles. Bioinformatics, 17, S215-S224.
31. Pic, A., Lim, F.L., Ross, S.J., Veal, E.A., Johnson, A.L., Sultan, M.R.A., West, A.G., Johnston, L.H., Sharrocks, A.D. and Morgan, B.A. (2000) The forkhead protein Fkh2 is a component of the yeast cell cycle transcription factor SFF. EMBO, 19, 3750-3761.
32. Pilpel, Y., Sudarsanam, P. and Church, G.M. (2001) Identifying regulatory networks by combinatorial analysis of promoter elements. Nat. Genet., 29, 153-159.
33. Segal, E., Barash, Y., Simon, I., Friedman, N. and Koller, D. (2002) From promoter sequence to expression: a probabilistic framework. In Proceedings of the International Conference on Research in Computational Molecular Biology '02. pp. 273-280.
34. Shmulevich, I., Dougherty, E.R., Kim, S. and Zhang, W. (2002) Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics, 18, 261-274.
35. Suzuki, Y., Nogi, Y., Abe, A. and Fukasawa, T. (1988) GAL11 protein, an auxiliary transcription activator for genes encoding galactose-metabolizing enzymes in Saccharomyces cerevisiae. Mol. Cell. Biol., 8, 4991-4999.
36. Tinerey, L. and Kadane, J.B. (1986) Accurate approximations for posterior moments and marginal densities. J. Amer. Statist. Assoc., 81, 82-86.