Modeling genetic regulatory networks with probabilistic Boolean networks

Eurasip Book Series on Signal Processing and Communications

Volumn 2, Issue , 2005, Pages 241-279

  Shmulevich, Ilya ^a   Dougherty, Edward R ^a,b  

^a UNIVERSITY OF TEXAS   (United States)

^b TEXAS A AND M UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84873667434     PISSN: 16872789     EISSN: 16872797     Source Type: Book Series    
DOI: None     Document Type: Article

References (141)

1. I. Shmulevich, E. R. Dougherty, S. Kim, and W. Zhang, "Probabilistic Boolean networks: a rulebased uncertainty model for gene regulatory networks," Bioinformatics, vol. 18, no. 2, pp. 261- 274, 2002.
2. I. Shmulevich, E. R. Dougherty, and W. Zhang, "Gene perturbation and intervention in probabilistic Boolean networks," Bioinformatics, vol. 18, no. 10, pp. 1319-1331, 2002.
3. I. Shmulevich, E. R. Dougherty, and W. Zhang, "Control of stationary behavior in probabilistic Boolean networks by means of structural intervention," Journal of Biological Systems, vol. 10, no. 4, pp. 431-445, 2002.
4. I. Shmulevich, E. R. Dougherty, and W. Zhang, "From Boolean to probabilistic Boolean networks as models of genetic regulatory networks," Proc. IEEE, vol. 90, no. 11, pp. 1778-1792, 2002.
5. E. R. Dougherty and I. Shmulevich, "Mappings between probabilistic Boolean networks," Signal Processing, vol. 83, no. 4, pp. 799-809, 2003.
6. I. Shmulevich, I. Gluhovsky, R. Hashimoto, E. R. Dougherty, and W. Zhang, "Steady-state analysis of probabilistic Boolean networks," Comparative and Functional Genomics, vol. 4, no. 6, pp. 601-608, 2003.
7. A. Datta, A. Choudhary, M. L. Bittner, and E. R. Dougherty, "External control in Markovian genetic regulatory networks," Machine Learning, vol. 52, no. 1-2, pp. 169-191, 2003.
8. A. Datta, A. Choudhary, M. L. Bittner, and E. R. Dougherty, "External control in Markovian genetic regulatory networks: the imperfect information case," Bioinformatics, vol. 20, no. 6, pp. 924-930, 2004.
9. S. Kim, H. Li, E. R. Dougherty, et al., "Can Markov chain models mimic biological regulation?," Journal of Biological Systems, vol. 10, no. 4, pp. 337-357, 2002.
10. X. Zhou, X. Wang, and E. R. Dougherty, "Construction of genomic networks using mutualinformation clustering and reversible-jump Markov-Chain-Monte-Carlo predictor design," Signal Processing, vol. 83, no. 4, pp. 745-761, 2003.
11. R. F. Hashimoto, S. Kim, I. Shmulevich,W. Zhang,M. L. Bittner, and E. R. Dougherty, "Growing genetic regulatory networks from seed genes," Bioinformatics, vol. 20, no. 8, pp. 1241-1247, 2004.
12. I. Shmulevich, "Model selection in genomics," Environ. Health Perspect., vol. 111, no. 6, pp. A328-A329, 2003.
13. E. P. van Someren, L. F. A.Wessels, and M. J. T. Reinders, "Linear modeling of genetic networks from experimental data," in Proc. 8th International Conference on Intelligent Systems forMolecular Biology (ISMB '00), pp. 355-366, San Diego, Calif, USA, August 2000.
14. P. D'haeseleer, X. Wen, S. Fuhrman, and R. Somogyi, "Linear modeling of mRNA expression levels during CNS development and injury," in Pac. Symp. Biocomput. (PSB '99), vol. 4, pp. 41- 52, Hawaii, USA, January 1999.
15. K. Murphy and S. Mian, "Modelling gene expression data using dynamic Bayesian networks," Tech. Rep., University of California, Berkeley, Calif, USA, 1999.
16. N. Friedman, M. Linial, I. Nachman, and D. Pe'er, "Using Bayesian network to analyze expression data," J. Computational Biology, vol. 7, pp. 601-620, 2000.
17. A. J. Hartemink, D. K. Gifford, T. S. Jaakkola, and R. A. Young, "Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks," in Pac. Symp. Biocomput. (PSB '01), pp. 422-433, Hawaii, USA, January 2001.
18. E. J. Moler, D. C. Radisky, and I. S. Mian, "Integrating naive Bayes models and external knowledge to examine copper and iron homeostasis in S. cerevisiae," Physiol. Genomics, vol. 4, no. 2, pp. 127-135, 2000.
19. D. C. Weaver, C. T. Workman, and G. D. Stormo, "Modeling regulatory networks with weight matrices," in Pac. Symp. Biocomput. (PSB '99), vol. 4, pp. 112-123, Hawaii, USA, January 1999.
20. T.Mestl, E. Plahte, and S.W. Omholt, "A mathematical framework for describing and analysing gene regulatory networks," J. Theor. Biol., vol. 176, no. 2, pp. 291-300, 1995.
21. T. Chen, H. L. He, and G. M. Church, "Modeling gene expression with differential equations," in Pac. Symp. Biocomput. (PSB '99), vol. 4, pp. 29-40, Hawaii, USA, January 1999.
22. J. Goutsias and S. Kim, "A nonlinear discrete dynamical model for transcriptional regulation: construction and properties," Biophys. J., vol. 86, no. 4, pp. 1922-1945, 2004.
23. H. H. McAdams and A. Arkin, "Stochastic mechanisms in gene expression," Proc. Natl. Acad. Sci. USA, vol. 94, no. 3, pp. 814-819, 1997.
24. A. Arkin, J. Ross, and H. H. McAdams, "Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells," Genetics, vol. 149, no. 4, pp. 1633- 1648, 1998.
25. P. Smolen, D. A. Baxter, and J. H. Byrne, "Mathematical modeling of gene networks," Neuron, vol. 26, no. 3, pp. 567-580, 2000.
26. J. Hasty, D. McMillen, F. Isaacs, and J. J. Collins, "Computational studies of gene regulatory networks: in numero molecular biology," Nat. Rev. Genet., vol. 2, no. 4, pp. 268-279, 2001.
27. H. de Jong, "Modeling and simulation of genetic regulatory systems: a literature review," J. Comput. Biol., vol. 9, no. 1, pp. 69-103, 2002.
28. S. A. Kauffman, "Metabolic stability and epigenesis in Randomly constructed genetic nets," J. Theor. Biol., vol. 22, no. 3, pp. 437-467, 1969.
29. L. Glass and S. A. Kauffman, "The logical analysis of continuous, non-linear biochemical control networks," J. Theor. Biol., vol. 39, no. 1, pp. 103-129, 1973.
30. S. Huang, "Gene expression profiling, genetic networks, and cellular states: an integrating concept for tumorigenesis and drug discovery," J. Mol. Med., vol. 77, no. 6, pp. 469-480, 1999.
31. S. A. Kauffman, the Origins of Order: Self-Organization and Selection in Evolution, Oxford University Press, New York, NY, USA, 1993.
32. R. Somogyi and C. Sniegoski, "Modeling the complexity of gene networks: understand ingmultigenic and pleiotropic regulation," Complexity, vol. 1, pp. 45-63, 1996.
33. M. Aldana, S. Coppersmith, and L. P. Kadanoff, "Boolean dynamics with rand om couplings," in Perspectives and Problems in Nonlinear Science, E. Kaplan, J. E.Marsden, and K. R. Sreenivasan, Eds., Applied Mathematical Sciences Series, pp. 23-89, Springer-Verlag, New York, NY, USA, 2003.
34. J. W. Bodnar, "Programming the Drosophila embryo," J. Theor. Biol., vol. 188, no. 4, pp. 391- 445, 1997.
35. C. H. Yuh, H. Bolouri, and E. H. Davidson, "Genomic cis-regulatory logic: experimental and computational analysis of a sea urchin gene," Science, vol. 279, no. 5358, pp. 1896-1902, 1998.
36. L. Mendoza, D. Thieffry, and E. R. Alvarez-Buylla, "Genetic control of flower morphogenesis in Arabidopsis thaliana: a logical analysis," Bioinformatics, vol. 15, no. 7-8, pp. 593-606, 1999.
37. S. Huang and D. E. Ingber, "Regulation of cell cycle and gene activity patterns by cell shape: evidence for attractors in real regulatory networks and the selective mode of cellular control," to appear in InterJournal Genetics, http://www.interjournal.org.
38. G. Lahav, N. Rosenfeld, A. Sigal, et al., "Dynamics of the p53-Mdm2 feedback loop in individual cells," Nat. Genet., vol. 36, no. 2, pp. 147-150, 2004.
39. I. Shmulevich and W. Zhang, "Binary analysis and optimization-based normalization of gene expression data," Bioinformatics, vol. 18, no. 4, pp. 555-565, 2002.
40. X. Zhou, X. Wang, and E. R. Dougherty, "Binarization of microarray data on the basis of a mixture model," Mol. Cancer Ther., vol. 2, no. 7, pp. 679-684, 2003.
41. W. Zhang, I. Shmulevich, and J. Astola, Microarray Quality Control, John Wiley & Sons, New York, NY, USA, 2004.
42. Y. Chen, E. R. Dougherty, and M. Bittner, "Ratio-based decisions and the quantitative analysis of cDNA microarray images," Biomedical Optics, vol. 2, no. 4, pp. 364-374, 1997.
43. M. K. Kerr, E.H. Leiter, L. Picard, and G. A. Churchill, "Sources of variation inmicroarray experiments," in Computational and Statistical Approaches to Genomics,W. Zhang and I. Shmulevich, Eds., Kluwer Academic Publishers, Boston, Mass, USA, 2002.
44. H. H.McAdams and A. Arkin, "It's a noisy business! Genetic regulation at the nanomolar scale," Trends Genet., vol. 15, no. 2, pp. 65-69, 1999.
45. Z. Szallasi and S. Liang, "Modeling the normal and neoplastic cell cycle with "realistic Boolean genetic networks": their application for understand ing carcinogenesis and assessing therapeutic strategies," in Pac. Symp. Biocomput. (PSB '98), vol. 3, pp. 66-76, Hawaii, USA, January 1998.
46. A. Wuensche, "Genomic regulation modeled as a network with basins of attraction," in Pac. Symp. Biocomput. (PSB '98), vol. 3, pp. 89-102, Hawaii, USA, January 1998.
47. R. Thomas, D. Thieffry, and M. Kaufman, "Dynamical behaviour of biological regulatory networks-I. Biological role of feedback loops and practical use of the concept of the loopcharacteristic state," Bull. Math. Biol., vol. 57, no. 2, pp. 247-276, 1995.
48. S. Liang, S. Fuhrman, and R. Somogyi, "Reveal, a general reverse engineering algorithm for inference of genetic network architectures," in Pac. Symp. Biocomput. (PSB '98), vol. 3, pp. 18- 29, Hawaii, USA, January 1998.
49. T. Akutsu, S. Kuhara, O.Maruyama, and S.Miyano, "Identification of gene regulatory networks by strategic gene disruptions and gene overexpressions," in Proc. 9th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA '98), pp. 695-702, San Francisco, Calif, USA, January 1998.
50. T. Akutsu, S. Miyano, and S. Kuhara, "Identification of genetic networks from a small number of gene expression patterns under the Boolean network model," Pac. Symp. Biocomput., vol. 4, pp. 17-28, 1999.
51. T. Akutsu, S. Miyano, and S. Kuhara, "Inferring qualitative relations in genetic networks and metabolic pathways," Bioinformatics, vol. 16, no. 8, pp. 727-734, 2000.
52. P. D'haeseleer, S. Liang, and R. Somogyi, "Genetic network inference: from co-expression clustering to reverse engineering," Bioinformatics, vol. 16, no. 8, pp. 707-726, 2000.
53. I. Shmulevich, A. Saarinen, O. Yli-Harja, and J. Astola, "Inference of genetic regulatory networks via best-fit extensions," in Computational and Statistical Approaches to Genomics, W. Zhang and I. Shmulevich, Eds., Kluwer Academic Publishers, Boston, Mass, USA, 2002.
54. H. Lähdesmäki, I. Shmulevich, and O. Yli-Harja, "On learning gene regulatory networks under the Boolean network model," Machine Learning, vol. 52, no. 1-2, pp. 147-167, 2003.
55. E. R. Dougherty, S. Kim, and Y. Chen, "Coefficient of determination in nonlinear signal processing," Signal Processing, vol. 80, no. 10, pp. 2219-2235, 2000.
56. S. Kim, E. R. Dougherty, Y. Chen, et al., "Multivariate measurement of gene expression relationships," Genomics, vol. 67, no. 2, pp. 201-209, 2000.
57. I. Shmulevich and S. A. Kauffman, "Activities and sensitivities in Boolean network models," Phys. Rev. Lett., vol. 93, no. 4, p. 048701, 2004.
58. E. R. Dougherty, M. Bittner, Y. Chen, et al., "Nonlinear filters in genomic control," in Proc. IEEEEURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '99), Antalya, Turkey, June 1999.
59. S. Kim, E. R. Dougherty, M. L. Bittner, et al., "General nonlinear framework for the analysis of gene interaction via multivariate expression arrays," J. Biomed. Opt., vol. 5, no. 4, pp. 411-424, 2000.
60. X. Zhou, X. Wang, and E. R. Dougherty, "Gene prediction using multinomial probit regression with Bayesian gene selection," EURASIP J. Appl. Signal Process., vol. 2004, no. 1, pp. 115-124, 2004.
61. X. Zhou, X.Wang, R. Pal, I. Ivanov,M. L. Bittner, and E. R. Dougherty, "A Bayesian connectivity based approach to constructing probabilistic gene regulatory networks," Bioinformatics, vol. 20, no. 17, pp. 2918-2927, 2004.
62. E. B. Suh, E. R. Dougherty, S. Kim, et al., "Parallel computation and visualization tools for codetermination analysis of multivariate gene-expression relations," in Computational and Statistical Approaches to Genomics, W. Zhang and I. Shmulevich, Eds., Kluwer Academic Publishers, Boston, Mass, USA, 2002.
63. I. Ivanov and E. R. Dougherty, "Reduction mappings between probabilistic Boolean networks," EURASIP J. Appl. Signal Process., vol. 2004, no. 1, pp. 125-131, 2004.
64. A. L. Gartel and A. L. Tyner, "Transcriptional regulation of the p21(WAF1/CIP1) gene," Exp. Cell Res., vol. 246, no. 2, pp. 280-289, 1999.
65. E. Schneider, M. Montenarh, and P.Wagner, "Regulation of CAK kinase activity by p53," Oncogene, vol. 17, no. 21, pp. 2733-2741, 1998.
66. A. Rzhetsky, T. Koike, S. Kalachikov, et al., "A knowledge model for analysis and simulation of regulatory networks," Bioinformatics, vol. 16, no. 12, pp. 1120-1128, 2000.
67. U. Braga-Neto, R. Hashimoto, E. R. Dougherty, D. V. Nguyen, and R. J. Carroll, "Is crossvalidation better than resubstitution for ranking genes?," Bioinformatics, vol. 20, no. 2, pp. 253- 258, 2004.
68. M. Brun, E. R. Dougherty, and I. Shmulevich, "Attractors in probabilistic Boolean networks: steady-state probabilities and classification," to appear in Signal Process.
69. M. K. Cowles and B. P. Carlin, "Markov Chain Monte Carlo convergence diagnostics: a comparative study," Journal of the American Statistical Association, vol. 91, pp. 883-904, 1996.
70. A. E. Raftery and S. Lewis, "How many iterations in the Gibbs sampler?" in Bayesian Statistics, J. O. Berger, J. M. Bernardo, A. P. Dawid, and A. F. M. Smith, Eds., vol. 4, pp. 763-773, Oxford University Press, Oxford, UK, 1992.
71. T. N. Sato, Y. Qin, C. A. Kozak, and K. L. Audus, "Tie-1 and tie-2 define another class of putative receptor tyrosine kinase genes expressed in early embryonic vascular system," Proc. Natl. Acad. Sci. USA, vol. 90, no. 20, pp. 9355-9358, 1993.
72. S. Y. Cheng, H. J. Huang, M. Nagane, et al., "Suppression of glioblastoma angiogenicity and tumorigenicity by inhibition of endogenous expression of vascular endothelial growth factor," Proc. Natl. Acad. Sci. USA, vol. 93, no. 16, pp. 8502-8507, 1996.
73. S. Hayashi, M. Yamamoto, Y. Ueno, et al., "Expression of nuclear factor-kappa B, tumor necrosis factor receptor type 1, and c-Myc in human astrocytomas," Neurol. Med. Chir. (Tokyo), vol. 41, no. 4, pp. 187-195, 2001.
74. M. Arsura,M.Wu, and G. E. Sonenshein, "TGF beta 1 inhibits NF-kappa B/Rel activity inducing apoptosis of B cells: transcriptional activation of I kappa B alpha," Immunity, vol. 5, no. 1, pp. 31-40, 1996.
75. E. D. Strauch, J. Yamaguchi, B. L. Bass, and J. Y.Wang, "Bile salts regulate intestinal epithelial cell migration by nuclear factor-kappa B-induced expression of transforming growth factor-beta," J. Am. Coll. Surg., vol. 197, no. 6, pp. 974-984, 2003.
76. C. D. Johnson, Y. Balagurunathan, K. P. Lu, et al., "Genomic profiles and predictive biological networks in oxidant-induced atherogenesis," Physiol. Genomics, vol. 13, no. 3, pp. 263-275, 2003.
77. I. Shmulevich, O. Yli-Harja, J. Astola, and A. Korshunov, "On the robustness of the class of stack filters," IEEE Trans. Signal Processing, vol. 50, no. 7, pp. 1640-1649, 2002.
78. T. E. Ideker, V. Thorsson, and R. M. Karp, "Discovery of regulatory interactions through perturbation: inference and experimental design," in Pac. Symp. Biocomput. (PSB '00), vol. 5, pp. 302-313, Hawaii, USA, January 2000.
79. R. M. Karp, R. Stoughton, and K. Y. Yeung, "Algorithms for choosing differential gene expression experiments," in Proc. 3rd Annual International Conference on Computational Molecular Biology (RECOMB '99), pp. 208-217, Lyon, France, 1999.
80. Y. Maki, D. Tominaga, M. Okamoto, S.Watanabe, and Y. Eguchi, "Development of a system for the inference of large scale genetic networks," in Pac. Symp. Biocomput. (PSB '01), vol. 6, pp. 446-458, Hawaii, USA, January 2001.
81. K. Noda, A. Shinohara, M. Takeda, S. Matsumoto, S. Miyano, and S. Kuhara, "Finding genetic network from experiments by weighted network model," Genome Inform., vol. 9, pp. 141-150, 1998.
82. I. Shmulevich, M. Gabbouj, and J. Astola, "Complexity of the consistency problem for certain Post classes," IEEE Transactions on Systems,Man, and Cybernetics, Part B, vol. 31, no. 2, pp. 251- 253, 2001.
83. Y. Chen, V. Kamat, E. R. Dougherty, M. L. Bittner, P. S. Meltzer, and J.M. Trent, "Ratio statistics of gene expression levels and applications to microarray data analysis," Bioinformatics, vol. 18, no. 9, pp. 1207-1215, 2002.
84. E. J. Coyle and J. H. Lin, "Stack filters and the mean absolute error criterion," IEEE Trans. Acoust., Speech, Signal Processing, vol. 36, no. 8, pp. 1244-1254, 1988.
85. E. J. Coyle, J. H. Lin, and M. Gabbouj, "Optimal stack filtering and the estimation and structural approaches to image processing," IEEE Trans. Acoust., Speech, Signal Processing, vol. 37, no. 12, pp. 2037-2066, 1989.
86. R. Yang, L. Yin, M. Gabbouj, J. Astola, and Y. Neuvo, "Optimal weighted median filtering under structural constraints," IEEE Trans. on Signal Processing, vol. 43, no. 3, pp. 591-604, 1995.
87. E. R. Dougherty and R. P. Loce, "Precision of morphological- representation estimators for translation-invariant binary filters: increasing and nonincreasing," Signal Processing, vol. 40, no. 2-3, pp. 129-154, 1994.
88. E. R. Dougherty and Y. Chen, "Optimal and adaptive design of logical granulometric filters," Adv. Imaging Electron Phys., vol. 117, pp. 1-71, 2001.
89. E. R. Dougherty and J. Barrera, "Pattern recognition theory in nonlinear signal processing," J. Math. Imaging Vision, vol. 16, no. 3, pp. 181-197, 2002.
90. S. A. Kauffman, "The large scale structure and dynamics of gene control circuits: an ensemble approach," J. Theor. Biol., vol. 44, no. 1, pp. 167-190, 1974.
91. S. E.Harris, B. K. Sawhill, A.Wuensche, and S. Kauffman, "A model of transcriptional regulatory networks based on biases in the observed regulation rules," Complexity, vol. 7, no. 4, pp. 23-40, 2002.
92. I. Shmulevich, H. Lähdesmäki, and K. Egiazarian, "Spectral methods for testing membership in certain Post classes and the class of forcing functions," IEEE Signal Processing Lett., vol. 11, no. 2, pp. 289-292, 2004.
93. I. Shmulevich, H. Lahdesmaki, E. R. Dougherty, J. Astola, and W. Zhang, "The role of certain Post classes in Boolean network models of genetic networks," Proc. Natl. Acad. Sci. USA, vol. 100, no. 19, pp. 10734-10739, 2003.
94. E. Post, "Introduction to a general theory of elementary propositions," Amer. J. Math., vol. 43, pp. 163-185, 1921.
95. E. I. Nechiporuk, "On the complexity of circuits in some bases, containing non-trivial elements with zero weights," Prob. Cybern.,, no. 8, 1962.
96. A. A. Muchnik and S. G. Gindikin, "On the completeness of systems of unreliable elements which realize functions of the algebra of logic," Dokl. Akad. Nauk SSSR, vol. 144, no. 5, 1962.
97. M. E. Newman, D. J.Watts, and S. H. Strogatz, "Rand om graphmodels of social networks," Proc. Natl. Acad. Sci. USA, vol. 99, no. Suppl 1, pp. 2566-2572, 2002.
98. D. J. Watts and S. H. Strogatz, "Collective dynamics of "small-world" networks," Nature, vol. 393, no. 6684, pp. 440-442, 1998.
99. P. Kleihues and W. K. Cavenee, Eds., World Health Organization Classification of Tumours: Pathology and Genetics of Tumours of Nervous System, Oxford University Press, Oxford, UK, 2000.
100. G. N. Fuller, C. H. Rhee, K. R. Hess, et al., "Reactivation of insulin-like growth factor binding protein 2 expression in glioblastoma multiforme: a revelation by parallel gene expression profiling," Cancer Res., vol. 59, no. 17, pp. 4228-4232, 1999.
101. S. L. Sallinen, P. K. Sallinen, H. K. Haapasalo, et al., "Identification of differentially expressed genes in human gliomas by DNA microarray and tissue chip techniques," Cancer Res., vol. 60, no. 23, pp. 6617-6622, 2000.
102. M.W. Elmlinger,M. H. Deininger, B. S. Schuett, et al., "In vivo expression of insulin-like growth factor-binding protein-2 in human gliomas increases with the tumor grade," Endocrinology, vol. 142, no. 4, pp. 1652-1658, 2001.
103. V. Cazals, E. Nabeyrat, S. Corroyer, Y. de Keyzer, and A. Clement, "Role for NF-kappa B in mediating the effects of hyperoxia on IGF-binding protein 2 promoter activity in lung alveolar epithelial cells," Biochim. Biophys. Acta., vol. 1448, no. 3, pp. 349-362, 1999.
104. J. Folkman, "Angiogenesis in cancer, vascular, rheumatoid and other disease," Nat. Med., vol. 1, no. 1, pp. 27-31, 1995.
105. A. Bikfalvi and R. Bicknell, "Recent advances in angiogenesis, anti-angiogenesis and vascular targeting," Trends Pharmacol. Sci., vol. 23, no. 12, pp. 576-582, 2002.
106. J. S. Rubin, H. Osada, P. W. Finch, W. G. Taylor, S. Rudikoff, and S. A. Aaronson, "Purification and characterization of a newly identified growth factor specific for epithelial cells," Proc. Natl. Acad. Sci. USA, vol. 86, no. 3, pp. 802-806, 1989.
107. S. S. Banga, H. L. Ozer, and S. T. Park, S. K. Lee, "Assignment of PTK7 encoding a receptor protein tyrosine kinase-like molecule to human chromosome 6p21-1-p12-2 by fluorescence in situ hybridization," Cytogenet. Cell Genet., vol. 76, no. 1-2, pp. 43-44, 1997.
108. K. V. Stoletov, K. E. Ratcliffe, and B. I. Terman, "Fibroblast growth factor receptor substrate 2 participates in vascular endothelial growth factor-induced signaling," FASEB J., vol. 16, no. 10, pp. 1283-1285, 2002.
109. C. Q. Wei, Y. Gao, K. Lee, et al., "Macrocyclization in the design of Grb2 SH2 domain-binding ligand s exhibiting high potency in whole-cell systems," J.Med. Chem., vol. 46, no. 2, pp. 244-254, 2003.
110. C. C. Bancroft, Z. Chen, G. Dong, et al., "Coexpression of proangiogenic factors IL-8 and VEGF by human head and neck squamous cell carcinoma involves coactivation by MEK-MAPK and IKK-NF-kappaB signal pathways," Clin. Cancer Res., vol. 7, no. 2, pp. 435-442, 2001.
111. G. Pearson, J. M. English, M. A.White, and M. H. Cobb, "ERK5 and ERK2 cooperate to regulate NF-kappaB and cell transformation," J. Biol. Chem., vol. 276, no. 11, pp. 7927-7931, 2001.
112. J.-P. Yang, M. Hori, T. Sanda, and T. Okamoto, "Identification of a novel inhibitor of nuclear factor-kappa-B, RelA-associated inhibitor," J. Biol. Chem., vol. 274, no. 22, pp. 15662-15670, 1999.
113. D. P. Bertsekas, Dynamic Programming and Stochastic Control, Academic Press, Orland o, Fla, USA, 1976.
114. M. Bittner, P.Meltzer, and Y. Chen, "Molecular classification of cutaneous malignantmelanoma by gene expression profiling," Nature, vol. 406, no. 6795, pp. 536-540, 2000.
115. A. T.Weeraratna, Y. Jiang, G. Hostetter, et al., "Wnt5a signaling directly affects cell motility and invasion of metastatic melanoma," Cancer Cell, vol. 1, no. 3, pp. 279-288, 2002.
116. S. Agaian, J. Astola, and K. Egiazarian, Binary Polynomial Transforms and Nonlinear Digital Filters, Marcel Dekker, New York, NY, USA, 1995.
117. O. Coudert, "Doing two-level logic minimization 100 times faster," in Proc. 6th Annual ACMSIAM Symposium on Discrete Algorithms (SODA '98), pp. 112-121, Society for Industrial and Applied Mathematics, San Francisco, Calif, USA, January 1995.
118. S. Dedhar, B. Williams, and G. Hannigan, "Integrin-linked kinase (ILK): a regulator of integrin and growth-factor signalling," Trends Cell Biol., vol. 9, no. 8, pp. 319-323, 1999.
119. E. R. Dougherty and S. N. Attoor, "Design issues and comparison of methods for microarray based classification," in Computational and Statistical Approaches to Genomics, W. Zhang and I. Shmulevich, Eds., Kluwer Academic Publishers, Boston, Mass, USA, 2002.
120. B. Efron and R. J. Tibshirani, An Introduction to the Bootstrap, vol. 57 of Monographs on Statistics and Applied Probability, Chapman and Hall, New York, NY, USA, 1993.
121. C. J. Geyer and E. A. Thompson, "Annealing Markov Chain Monte Carlo with applications to ancestral inference," Journal of the American Statistical Asssociation, vol. 90, pp. 909-920, 1995.
122. G. D. Hachtel, E. Macii, A. Pardo, and F. Somenzi, "Markovian analysis of large finite state machines," IEEE Trans. Computer-Aided Design, vol. 15, no. 12, pp. 1479-1493, 1996.
123. P. L. Hammer, A. Kogan, and U. G. Rothblum, "Evaluation, strength, and relevance of variables of Boolean functions," SIAM Journal on Discrete Mathematics, vol. 13, no. 3, pp. 302-312, 2000.
124. W. K. Hastings, "Monte Carlo sampling methods using Markov Chains and their applications," Biometrika, vol. 57, no. 1, pp. 97-109, 1970.
125. S. Kim, E. R. Dougherty, I. Shmulevich, et al., "Identification of combination gene sets for glioma classification," Mol. Cancer Ther., vol. 1, no. 13, pp. 1229-1236, 2002.
126. L. M. Loew and J. C. Schaff, "The virtual cell: a software environment for computational cell biology," Trends Biotechnol., vol. 19, no. 10, pp. 401-406, 2001.
127. D. A. McAllester, "PAC-Bayesian stochastic model selection," Machine Learning, vol. 51, no. 1, pp. 5-21, 2003.
128. P. Mendes, W. Sha, and K. Ye, "Artificial gene networks for objective comparison of analysis algorithms," Bioinformatics, vol. 19, no. Suppl 2, pp. II122-II129, 2003.
129. C.Mircean, I. Tabus, J. Astola, et al., "Quantization and similaritymeasure selection for discrimination of lymphoma subtypes under k-nearest neighbor classification," in Proc. SPIE Photonics West, Biomedical Optics, San Jose, Calif, USA, January 2004.
130. M. Mitchell, J. P. Crutchfield, and P. T. Hraber, "Evolving cellular automata to perform computations: mechanisms and impediments," Physica D., vol. 75, no. 1-3, pp. 361-391, 1994.
131. S. R. Neves and R. Iyengar, "Modeling of signaling networks," Bioessays, vol. 24, no. 12, pp. 1110-1117, 2002.
132. S. N.Nikolopoulos and C. E. Turner, "Integrin-linked kinase (ILK) binding to paxillin LD1motif regulates ILK localization to focal adhesions," J. Biol. Chem., vol. 276, no. 26, pp. 23499-23505, 2001.
133. B. Plateau and K. Atif, "Stochastic automata network of modeling parallel systems," IEEE Trans. Software Eng., vol. 17, no. 10, pp. 1093-1108, 1991.
134. J. S. Rosenthal, "Minorization conditions and convergence rates for Markov chainMonte Carlo," Journal of the American Statistical Asssociation, vol. 90, no. 430, pp. 558-566, 1995.
135. I. Shmulevich, O. Yli-Harja, K. Egiazarian, and J. Astola, "Output distributions of recursive stack filters," IEEE Signal Processing Lett., vol. 6, no. 7, pp. 175-178, 1999.
136. P. T. Spellman, G. Sherlock, M. Q. Zhang, et al., "Comprehensive identification of cell cycleregulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization," Mol. Biol. Cell., vol. 9, no. 12, pp. 3273-3297, 1998.
137. I. Tabus and J. Astola, "On the use of MDL principle in gene expression prediction," EURASIP J. Appl. Signal Process., vol. 4, no. 4, pp. 297-303, 2001.
138. A.Wagner, "How to reconstruct a large genetic network from n gene perturbations in fewer than n2 easy steps," Bioinformatics, vol. 17, no. 12, pp. 1183-1197, 2001.
139. H. Wang, H. Wang, W. Shen, et al., "Insulin-like growth factor binding protein 2 enhances glioblastoma invasion by activating invasion-enhancing genes," Cancer Res., vol. 63, no. 15, pp. 4315-4321, 2003.
140. D. E. Zak, F. J. Doyle, G. E. Gonye, and J. S. Schwaber, "Simulation studies for the identification of genetic networks from cDNA array and regulatory activity data," in Proc. 2nd International Conference on Systems Biology (ICSB '01), pp. 231-238, Pasadena, Calif, USA, November 2001.
141. D. E. Zak, R. K. Pearson, R. Vadigepalli, G. E. Gonye, J. S. Schwaber, and F. J. Doyle III, "Continuous-time identification of gene expression models," OMICS, vol. 7, no. 4, pp. 373-386, 2003.