Constructing non-stationary Dynamic Bayesian Networks with a flexible lag choosing mechanism

BMC Bioinformatics

Volumn 11, Issue SUPPL. 6, 2010, Pages

  Jia, Yi ^a   Huan, Jun ^a  

^a University of Kansas   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DYNAMIC BAYESIAN NETWORKS; GENE EXPRESSION DATA; GENE REGULATORY NETWORKS; NON-STATIONARY DYNAMICS; NON-STATIONARY TIME SERIES; PREDICTION ACCURACY; STRUCTURAL INFORMATION; STRUCTURE PREDICTION;

BAYESIAN NETWORKS; FORECASTING; GENE EXPRESSION; RANDOM PROCESSES;

DYNAMICS;

ARABIDOPSIS;

ANIMAL; ARABIDOPSIS; ARTICLE; BAYES THEOREM; DROSOPHILA; GENE REGULATORY NETWORK; GENETIC DATABASE; GENETICS; GENOMICS; MACROPHAGE; METABOLISM; METHODOLOGY;

ANIMALS; ARABIDOPSIS; BAYES THEOREM; DATABASES, GENETIC; DROSOPHILA; GENE REGULATORY NETWORKS; GENOMICS; MACROPHAGES;

EID: 77957713914     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-11-27     Document Type: Article

References (37)

1. Robinson JW, Hartemink AJ. Non-stationary dynamic Bayesian networks. Procedding of Advances in Neural Information Processing Systems Conference 2008,
2. Grzegorczy M, Husmeier D, Edwards KD, Ghazal P, Millar AJ. Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler. Bioinformatics 2008, 24:2071-2078. 10.1093/bioinformatics/btn367, 18664467.
3. Mas P. Circadian clock function in Arabidopsis thaliana: time beyond transcription. Trends Cell Biol 2008, 18:273-181. 10.1016/j.tcb.2008.03.005, 18468438.
4. Nobile A, Fearnside AT. Bayesian finite mixtures with an unknown number of components: The allocation sampler. Statistics and Computing 2007, 17:147-162.
5. Zou M, Conzen SD. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 2004, 21:71-79. 10.1093/bioinformatics/bth463, 15308537.
6. McAdams HH, Arkin A. Stochastic mechanisms in gene expression. Proc Natl Acad Sci U S A 1997, 94(3):814-819. 10.1073/pnas.94.3.814, 19596, 9023339.
7. Friedman N, Linial M, Nachman I, Pe'er D. Using Bayesian networks to analyze expression data. Journal of Computational Biology 2000, 7(3-4):601-620. 10.1089/106652700750050961, 11108481.
8. Murphy K, Mian S. Modeling gene expression data using dynamic Bayesian networks. Technical Report 1999,
9. Husmeier D. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 2003, 19:2271-2282. 10.1093/bioinformatics/btg313, 14630656.
10. Hartemink AJ, Gifford DK, Jaakkola TS, Young RA. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Proceedings of Pacific Symposium on Biocomputing 2001, 422-433.
11. Yu J, Smith VA, Wang PP, Hartemink AJ, Jarvis ED. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 2004, 20:3594-3603. 10.1093/bioinformatics/bth448, 15284094.
12. Imoto S, Goto T, Miyano S. Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression. Proceedings of Pacific Symposium on Biocomputing 2002, 175-186.
13. Imoto S, Higuchi T, Goto T, Tashiro K, Kuhara S, Miyano S. Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. Computer Society Bioinformatics Conference (CSB'03) 2003, 104.
14. Kim SY, Imoto S, Miyano S. Inferring gene networks from time series microarray data using dynamic Bayesian networks. Brief Bioinform 2003, 4:228-235. 10.1093/bib/4.3.228, 14582517.
15. Nariai N, Kim SY, Imoto S, Miyano S. Using protein-protein interactions for refining gene networks estimated from Microarray data by Bayesian networks. Pacific Symposium on Biocomputing 2004, 9:336-347.
16. Bernard A, Hartemink AJ. Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data. Proceedings of Pacific Symposium on Biocomputing 2005, 459-70.
17. Heckerman D, Geiger D, Chickering DM. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 1995, 20(3):197-243.
18. Yu H, Luscombe NM, Qian J, Gerstein M. Genomic analysis of gene expression relationships in transcriptional regulatory networks. Trends Genet 2003, 19:422-7. 10.1016/S0168-9525(03)00175-6, 12902159.
19. Green PJ. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 1995, 82:711-732.
20. Chib S, Greenberg E. Understanding the Metropolis Hasting Algorithm. Amer. Statist 1995, 49:327-335.
21. Werhli AV, Grzegorczyk M, Husmeier D. Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks. Bioinformatics 2006, 22(20):2523-2531. 10.1093/bioinformatics/btl391, 16844710.
22. JD J, Kerr I, Stark G. Jak-STAT pathways and transcriptional activation in response to IFNs and other extracellular signaling proteins. Science 1994, 264:1415-1421. 10.1126/science.8197455, 8197455.
23. Raza S, Robertson KA, Lacaze PA, Page D, Enright AJ, Ghazal P, Freeman TC. A logic-based diagram of signalling pathways central to macrophage activation. BMC Syst Biol 2008, 2:36. 10.1186/1752-0509-2-36, 2383880, 18433497.
24. Salome PA, McClung CR. The Arabidopsis thaliana Clock. Journal of Biological Rhythms 2004, 19(5):425-435. 10.1177/0748730404268112, 15534322.
25. Covington MF, Panda S, Liu XL, Strayer CA, Wagner DR, Kay SA. ELF3 Modulates Resetting of the Circadian Clock in Arabidopsis. The Plant Cell 2001, 13:1305-1315. 10.2307/3871297, 135573, 11402162.
26. Hall A, Kozma-Bognar L, Reka T, Nagy F, Millar AJ. Conditional circadian regulation of PHYTOCHROME A gene expression. Plant Physiol. 2001, 127(4):1808-18. 10.1104/pp.010294, 133584, 11743124.
27. Mizuno T, Nakamichi N. Pseudo-Response Regulators (PRRs) or True Oscillator Components (TOCs). Plant Cell Physiol. 2005, 46(5):677-685. 10.1093/pcp/pci087, 15767264.
28. Zhao W, Serpedin E, Dougherty ER. Inferring gene regulatory networks from time series data using the minimum description length principle. Bioinformatics 2006, 22(17):2129-2135. 10.1093/bioinformatics/btl364, 16845143.
29. Honda K, Takaoka A, Taniguchi T. Type I Interferon Gene Induction by the Interferon Regulatory Factor Family of Transcription Factors. Immunity 2006, 25:349-360. 10.1016/j.immuni.2006.08.009, 16979567.
30. Para A, Farre EM, Imaizumi T, Pruneda-Paz JL, Harmon FG, Kay SA. PRR3 Is a vascular regulator of TOC1 stability in the Arabidopsis circadian clock. Plant Cell 2007, 19(11):3462-73. 10.1105/tpc.107.054775, 2174887, 18055606.
31. Arbeitman MN, Furlong EEM, Imam F, Johnson E, Null BH, Baker BS, Krasnow MA, Scott MP, Davis RW, White KP. Gene Expression During the Life Cycle of Drosophila melanogaster. Science 2002, 297(5590):2270-2275. 10.1126/science.1072152, 12351791.
32. Benedict CA, Banks TA, Senderowicz L, Ko M, Britt WJ, Angulo A, Ghazal P, Ware CF. Lymphotoxins and Cytomegalovirus cooperatively Induce Interferon-b Establishing Host-Virus Detente. Immunity 2001, 15:617-626. 10.1016/S1074-7613(01)00222-9, 11672543.
33. Zhao W, Serpedin E, Dougherty ER. Inferring gene regulatory networks from time series data using the minimum description length principle. Bioinformatics 2006, 22(17):2129-2135. 10.1093/bioinformatics/btl364, 16845143.
34. Guo F, Hanneke S, Pu W, Xing EP. Recovering temporally rewiring networks: A model-based approach. ICML 2007, 24.
35. Duan H, Nguyen HT. Distinct Posttranscriptional Mechanisms Regulate the Activity of the Zn Finger Transcription Factor Lame duck during Drosophila Myogenesis. Mol Cell Biol 2006, 26(4):1414-1423. 10.1128/MCB.26.4.1414-1423.2006, 1367186, 16449652.
36. Cripps RM, Black BL, Zhao B, Lien CL, Schulz RA, Olson EN. The myogenic regulatory gene Mef2 is a direct target for transcriptional activation by Twist during Drosophila myogenesis. Genes Dev. 1998, 12(3):422-34. 10.1101/gad.12.3.422, 316486, 9450935.
37. Sandmann T, Jensen LJ, Jakobsen JS, Karzynski MM, Eichenlaub MP, Bork P, Furlong EE. DA temporal map of transcription factor activity: mef2 directly regulates target genes at all stages of muscle development. Dev Cell 2006, 10(6):797-807. 10.1016/j.devcel.2006.04.009, 16740481.