A combined expression-interaction model for inferring the temporal activity of transcription factors

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4955 LNBI, Issue , 2008, Pages 82-97

  Shi, Yanxin ^a   Simon, Itamar ^b   Mitchell, Tom ^a   Bar Joseph, Ziv ^a  

^a Carnegie Mellon University   (United States)

^b HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVITY LEVELS; COMPUTATIONAL MOLECULAR BIOLOGY; EXPRESSION LEVELS; HEIDELBERG (CO); INTERACTION MODELING; INTERNATIONAL CONFERENCES; MRNA LEVELS; POST TRANSCRIPTIONAL MODIFICATIONS; REAL DATA; REGULATORY NETWORKS; SWITCHING MODELS;

BIOCHEMISTRY; BIOLOGY; FLOW INTERACTIONS; HUMAN REHABILITATION ENGINEERING; MOLECULAR BIOLOGY; MOLECULAR STRUCTURE; PROTEINS; TRANSCRIPTION FACTORS;

TRANSCRIPTION;

EID: 47249102475     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-78839-3_8     Document Type: Conference Paper

References (36)

1. Spellman, P.T., et al.: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell. 9, 3273-3297 (2004)
2. Panda, S., et al.: Coordinated transcription of key pathways in the mouse by the circadian clock. Cell 109(3), 307-320 (2002)
3. Nau, G., et al.: Human macrophage activation programs induced by bacterial pathogens. PNAS 99, 1503-1508 (2002)
4. Arbeitman, M., et al.: Gene expression during the life cycle of drosophila melanogaster. Science 298, 2270-2275 (2002)
5. Theuns, J., et al.: Transcriptional regulation of Alzheimer's disease genes: implications for susceptibility. Hum. Mol. Genet. 9, 2383-2394 (2000)
6. Beer, M., et al.: Predicting gene expression from sequence. Cell 117(2), 185-198 (2004)
7. Zou, M., et al.: A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 21, 71-79 (2005)
8. Tanay, A., et al.: Computational expansion of genetic networks. Bioinformatics 17, S270-S278 (2001)
9. D'haeseleer, P., et al.: Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16, 707-726 (2000)
10. Segal, E., et al.: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34(2), 166-176 (2003)
11. Sabatti, et al.: Bayesian sparse hidden components analysis for transcription regulation networks. Bioinformatics 22, 739-746 (2006)
12. Ernst, J., et al.: Reconstructing dynamic regulatory maps. Nature-EMBO Molecular Systems Biology 3, 74 (2007)
13. Ideker, T., et al.: Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292, 929-934 (2001)
14. Kannan, et al.: A Bayesian Model That Links Microarray mRNA Measurements to Mass Spectrometry Protein Measurements. In: Speed, T., Huang, H. (eds.) RECOMB 2007. LNCS (LNBI), vol. 4453, pp. 325-338. Springer, Heidelberg (2007)
15. Bose, S., et al.: Genetic factors that regulate the attenuation of the general stress response of yeast. Genet. 169, 1215-1226 (2005)
16. Washburn, M.P., et al.: Protein pathway and complex clustering of correlated mRNA and protein expression analyses in Saccharomycs cerevisiae. PNAS 100, 3107-3112 (2003)
17. Nachman, I., et al.: Inferring quantitative models of regulatory networks from expression data. Bioinformatics 20(Suppl 1), I248-I256 (2004)
18. Ghahramani, Z., et al.: Factorial hidden Markov models. Machine Learning 29, 245-273 (1997)
19. Wang, L., et al.: Group SCAD regression analysis for microarray time course gene expression data. Bioinformatics 23, 1486-1494 (2007)
20. Harbison, C.T., et al.: Transcriptional regulatory code of a eukaryotic genome. Nature 431, 99-104 (2004)
21. Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Royal Statist. Soc. B. 58, 267-288 (1996)
22. Hu, Z., et al.: Genetic reconstruction of a functional transcriptional regulatory network. Nat. Genet. 39, 683-687 (2007)
23. Xing, E., et al.: A generalized mean field algorithm for variational inference in exponential families. In: Proceedings of UAI (2003)
24. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)
25. Murphy, K.: Dynamic bayesian networks: Representation, inference and learning. Ph.D. Thesis, University of California, Berkeley (2002)
26. Coleman, T.F., et al.: An interior trust region approach for nonlinear minimization subject to bounds. SIAM Journal on Optimization 6, 418-445 (1996)
27. Supporting website, http://www.cs.cmu.edu/∼yanxins/ptmm
28. MacIsaac, K., et al.: An improved map of conserved regulatory sites for Saccharomyces cerevisiae. BMC Bioinformatics 7, 113 (2006)
29. Workman, C.T., et al.: A systems approach to mapping DNA damage response pathways. Science 312, 1054-1059 (2006)
30. Garreau, H., et al.: Hyperphosphorylation of Msn2p and Msn4p in response to heat shock and the diauxic shift is inhibited by cAMP in Saccharomyces cerevisiae. Microbiology 146, 2113-2120 (2000)
31. Gorner, W., et al.: Nuclear localization of the C2H2 zinc finger protein Msn2p is regulated by stress and protein kinase A activity. Genes. Dev. 12, 586-597 (1998)
32. Moll, T., et al.: The role of phosphorylation and the CDC28 protein kinase in cell cycle-regulated nuclear import of the S. cerevisiae transcription factor SWI5. Cell 66, 743-758 (1991)
33. Pic-Taylor, A., et al.: Regulation of cell cycle-specific gene expression through cyclin-dependent kinase-mediated phosphorylation of the forkhead transcription factor Fkh2p. Mol. Cell. Biol. 24, 10036-10046 (2004)
34. Tsang, J.S., et al.: Phosphorylation influences the binding of the yeast RAP1 protein to the upstream activating sequence of the PGK gene. Nucl. Acids Res. 18, 7331-7337 (1990)
35. Mitchell, T., et al.: Hidden process models. In: Proceedings of ICML (2006)
36. Shi, Y., et al.: Continuous hidden process model for time series expression experiments. Bioinformatics 23, I459-I467 (2007)