Bayesian Inference of Signaling Network Topology in a Cancer Cell Line

Bioinformatics

Volumn 28, Issue 21, 2012, Pages 2804-2810

  Hill, Steven M ^a,b   Lu, Yiling ^c   Molina, Jennifer ^c   Heiser, Laura M ^d   Spellman, Paul T ^e   Speed, Terence P ^f,g   Gray, Joe W ^e   Mills, Gordon B ^c   Mukherjee, Sach ^a,b  

^a NETHERLANDS CANCER INSTITUTE   (Netherlands)

^b UNIVERSITY OF WARWICK   (United Kingdom)

^c UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^d LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

^e OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

^f WALTER AND ELIZA HALL INSTITUTE OF MEDICAL RESEARCH   (Australia)

^g UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AREA UNDER THE CURVE; ARTICLE; BAYES THEOREM; BREAST TUMOR; CELL COMMUNICATION; CHEMICAL STRUCTURE; COMPUTER SIMULATION; FEMALE; HUMAN; METABOLISM; PATHOLOGY; PROBABILITY; SIGNAL TRANSDUCTION; STATISTICAL MODEL; TUMOR CELL LINE; VALIDATION STUDY;

AREA UNDER CURVE; BAYES THEOREM; BREAST NEOPLASMS; CELL COMMUNICATION; CELL LINE, TUMOR; COMPUTER SIMULATION; FEMALE; HUMANS; MODELS, MOLECULAR; MODELS, STATISTICAL; PROBABILITY; SIGNAL TRANSDUCTION;

EID: 84868020742     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bts514     Document Type: Article

References (34)

1. Aijo, T. and Lahdesmaki, H. (2009) Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. Bioinformatics, 25, 2937-33.
2. Bender, C. et al. (2010) Dynamic deterministic effects propagation networks: learning signalling pathways from longitudinal protein array data. Bioinformatics, 26, i596-i602.
3. Cantone, I. et al. (2009) A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell, 137, 172-33.
4. Casella, G. et al. (2009) Consistency of Bayesian procedures for variable selection. Ann. Stat., 37, 1207-33.
5. Ciaccio, M.F. et al. (2010) Systems analysis of EGF receptor signaling dynamics with microwestern arrays. Nat. Methods, 7, 148-33.
6. Denison, D.G.T. et al. (2002) Bayesian Methods for Nonlinear Classification and Regression. Wiley, London.
7. Forte Deltell, A. (2011) Objective Bayes criteria for variable selection. PhD thesis, University of Valencia, Spain.
8. Friedman, N. et al. (1998) Learning the structure of dynamic probabilistic networks. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, San Francisco, CA, pp. 139-33.
9. Friedman, N. et al. (2000) Using Bayesian networks to analyze expression data. J. Comp. Biol., 7, 601-33.
10. Geiger, D. and Heckerman, D. (1994) Learning Gaussian networks. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI). San Francisco, CA, Morgan Kaufmann, pp. 235-33.
11. Grzegorczyk, M. and Husmeier, D. (2011) Non-homogeneous dynamic Bayesian networks for continuous data. Mach. Learn., 83, 355-33.
12. Hill, S.M. (2012) Sparse graphical models for cancer signalling. PhD thesis, University of Warwick, UK
13. Hill, S.M. et al. (2012) Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology. BMC Bioinformatics, 13, 94.
14. Husmeier, D. (2003) Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics, 19, 2271-33.
15. Knowles, D.A. and Ghahramani, Z. (2011) Nonparametric Bayesian sparse factor models with application to gene expression modelling. Ann. Appl. Stat., 5, 1534-33.
16. Kohn, R. et al. (2001) Nonparametric regression using linear combinations of basis functions. Stat. Comput., 11, 313-33.
17. Lebre, S. (2009) Inferring dynamic genetic networks with low order independencies. Stat. Appl. Genet. Mol. Biol., 8, 9.
18. Lee, M.J. et al. (2012) Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell, 149, 780-33.
19. Madigan, D. et al. (1995) Bayesian graphical models for discrete data. Int. Stat. Rev., 63, 215-33.
20. Meinshausen, N. and Buhlmann, P. (2006) High dimensional graphs and variable selection with the Lasso. Ann. Stat., 34, 1436-33.
21. Mukherjee, S. and Speed, T.P. (2008) Network inference using informative priors. Proc. Natl Acad. Sci. USA, 105, 14313-33.
22. Murphy, K.P. (2002) Dynamic Bayesian networks: representation, inference and learning. PhD thesis, Computer Science, University of California, Berkeley, CA.
23. Neve, R.M. et al. (2006) A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell, 10, 515-33.
24. Opgen-Rhein, R. and Strimmer, K. (2006) Using regularized dynamic correlation to infer gene dependency networks from time-series microarray data.In Proceedings of the Fourth International Workshop on Computational Systems Biology, WCSB 2006. pp. 73-76. Tampere, Finland. Tampere University of Technology.
25. Pawson, T. and Warner, N. (2007) Oncogenic re-wiring of cellular signaling pathways. Oncogene, 26, 1268-33.
26. Rau, A. et al. (2010) An empirical Bayesian method for estimating biological networks from temporal microarray data. Stat. Appl. Genet. Mol. Biol., 9, Article 9.
27. Robinson, J.W. and Hartemink, A.J. (2010) Learning non-stationary dynamic Bayesian networks. J. Mach. Learn. Res., 11, 3647-33.
28. Sachs, K. et al. (2005) Causal protein-signaling networks derived from multipara-meter single-cell data. Science, 308, 523-33.
29. Tibes, R. et al. (2006) Reverse phase protein array: validation of a novel proteomic technology and utility for analysis of primary leukemia specimens and hemato-poietic stem cells. Mol. Cancer Ther., 5, 2512-33.
30. Tibshirani, R. (1996) Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B, 58, 267-33.
31. Werhli, A. and Husmeier, D. (2008) Gene regulatory network reconstruction by Bayesian integration of prior knowledge and/or different experimental conditions. J. Bioinf. Comput. Biol., 6, 543-33.
32. Werhli, A.V. and Husmeier, D. (2007) Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge. Stat. Appl. Genet. Mol. Biol., 6, 15.
33. Xu, T.-R. et al. (2010) Inferring signaling pathway topologies from multiple perturbation measurements of specific biochemical species. Sci. Signal., 3, ra20.
34. Zellner, A. (1986) On assessing prior distributions and Bayesian regression analysis with g-prior distributions. In Goel, P.K. and Zellner, A. (eds.) Bayesian Inference and Decision Techniques\Essays in Honor of Bruno de Finetti. North-Holland, Amsterdam, pp. 233-33.