Inferring cluster-based networks from differently stimulated multiple time-course gene expression data

Bioinformatics

Volumn 26, Issue 8, 2010, Pages 1073-1081

  Shiraishi, Yuichi ^a   Kimura, Shuhei ^b   Okada, Mariko ^a  

^a RIKEN Center for Integrative Medical Sciences   (Japan)

^b TOTTORI UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CLUSTER ANALYSIS; DNA MICROARRAY; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORK; GENETIC DATABASE; GENETICS; HUMAN; METHODOLOGY; TUMOR CELL LINE;

CELL LINE, TUMOR; CLUSTER ANALYSIS; DATABASES, GENETIC; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS;

EID: 77951942705     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btq094     Document Type: Article

References (43)

1. Akaike,H. (1974) A new look at the statistical model identification. IEEE Trans. Automat. Contr., 19, 716-723.
2. Amit,I. et al. (2007) A module of negative feedback regulators defines growth factor signaling. Nat. Genet., 39, 503-512.
3. Beal,M.J. et al. (2005) A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics, 21, 349-356.
4. Beissbarth,T. and Speed,T.P. (2004) GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics, 20, 1464-1465.
5. Bock,C. and Lengauer,T. (2008) Computational epigenetics. Bioinformatics, 24, 1-10.
6. Bromberg,K.D. et al. (2008) Design logic of a cannabinoid receptor signaling network that triggers neurite outgrowth. Science, 320, 903-909.
7. Ferrazzi,F. et al. (2005) Random walk models for Bayesian clustering of gene expression profiles. Appl. Bioinformatics, 4, 263-276.
8. Fraley,C. and Raftery,A.E. (2002) Model-based clustering, discriminant analysis, and density estimation. J. Am. Stat. Assoc., 97, 611-631.
9. Ghahramani,Z. and Hinton,G.E. (1996) Parameter estimation for linear dynamical systems. Technical report CRG-TR-96-2, Department of Computer Science, University of Toronto.
10. Harvey,A.C. (1989) Forecasting, structural time series models and the Kalman filter. Cambridge University Press, New York.
11. Hirose,O. et al. (2008) Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models. Bioinformatics, 24, 932-942.
12. Imoto,S. et al. (2002) Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression. In Pacific Symposium on Biocomputing, pp. 175-186.
13. Inoue,L.Y. et al. (2007) Cluster-based network model for time-course gene expression data. Biostatistics, 8, 507-525.
14. Irizarry,R.A. et al. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4, 249-264.
15. Kao,S. et al. (2001) Identification of the mechanisms regulating the differential activation of the mapk cascade by epidermal growth factor and nerve growth factor in PC12 cells. J. Biol. Chem., 276, 18169-18177.
16. Kikuchi,S. et al. (2003) Dynamic modeling of genetic networks using genetic algorithm and S-system. Bioinformatics, 19, 643-650.
17. Kim,S. et al. (2003) Inferring gene networks from time series microarray data using dynamic Bayesian networks. Brief. Bioinform., 4, 228-235.
18. Kimura,S. et al. (2005) Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm. Bioinformatics, 21, 1154-1163.
19. Kimura,S. et al. (2009) Genetic network inference as a series of discrimination tasks. Bioinformatics, 25, 918-925.
20. Krzanowski,W.J. and Lai,Y.T. (1988) A criterion for determining the number of groups in a data set using sum-of-squares clustering. Biometrics, 44, 23-34.
21. Luan,Y. and Li,H. (2003) Clustering of time-course gene expression data using a mixedeffects model with B-splines. Bioinformatics, 19, 474-482.
22. Magni,P. et al. (2008) TimeClust: a clustering tool for gene expression time series. Bioinformatics, 24, 430-432.
23. Martin,S. et al. (2007) Boolean dynamics of genetic regulatory networks inferred from microarray time series data. Bioinformatics, 23, 866-874.
24. Miller,B.H. et al. (2007) Circadian and CLOCK-controlled regulation of the mouse transcriptome and cell proliferation. Proc. Natl Acad. Sci. USA, 104, 3342-3347.
25. Miller-Jensen,K. et al. (2007) Common effector processing mediates cell-specific responses to stimuli. Nature, 448, 604-608.
26. Nagashima,T. et al. (2007) Quantitative transcriptional control of ErbB receptor signaling undergoes graded to biphasic response for cell differentiation. J. Biol. Chem., 282, 4045-4056.
27. Perrin,B.E. et al. (2003) Gene networks inference using dynamic Bayesian networks. Bioinformatics, 19, 138-148.
28. Ramoni,M.F. et al. (2002) Cluster analysis of gene expression dynamics. Proc. Natl Acad. Sci. USA, 99, 9121-9126.
29. Rangel,C. et al. (2004) Modeling T-cell activation using gene expression profiling and state-space models. Bioinformatics, 20, 1361-1372.
30. Roweis,S. and Ghahramani,Z. (1999) A unifying review of linear Gaussian models. Neural Comput., 11, 305-345.
31. Sachs,K. et al. (2005) Causal protein-signaling networks derived from multiparameter single-cell data. Science, 308, 523-529.
32. Schwarz,G. (1978) Estimating the dimension of a model. Ann. Stat., 6, 461-464.
33. Segal,E. et al. (2005) Learning module networks. J. Mach. Learn. Res., 6, 557-588.
34. Sugar,C.A. and James,G.M. (2003) Finding the number of clusters in a dataset: an information-theoretic approach. J. Am. Stat. Assoc., 98, 750-763.
35. Tibshirani,R. et al. (2001) Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. Ser. B, 63, 411-423.
36. Toh,H. and Horimoto,K. (2002) Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling. Bioinformatics, 18, 287-297.
37. Ueda,N. et al. (2000) SMEM algorithm for mixture models. Neural Comput., 12, 2109-2128.
38. Wingender,E. et al. (2000) TRANSFAC: an integrated system for gene expression regulation. Nucleic Acids Res., 28, 316-319.
39. Wu,F.X. et al. (2005) Dynamic model-based clustering for time-course gene expression data. J. Bioinform. Comput. Biol., 3, 821-836.
40. Yamaguchi,R. et al. (2007) Finding module-based gene networks with state-space models - mining high-dimensional and short time-course gene expression data. IEEE Signal Process. Mag., 24, 37-46.
41. York,R.D. et al. (2000) Role of phosphoinositide 3-kinase and endocytosis in nerve growth factor-induced extracellular signal-regulated kinase activation via Ras and Rap1. Mol. Cell. Biol., 20, 8069-8083.
42. Zhong,S. and Ghosh,J. (2003)Aunified framework for model-based clustering. J.Mach. Learn. Res., 4, 1001-1037.
43. 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, 71-79.