Identifying interventional and pathogenic mechanisms by generative inverse modeling of gene expression profiles

Journal of Computational Biology

Volumn 11, Issue 6, 2004, Pages 1135-1148

  Dejori, Mathäus ^a,b   Stetter, Martin ^a  

^a SIEMENS AG   (Germany)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Bayesian networks; Genetic networks; Microarrays; Modeling interventions

Indexed keywords

ARTICLE; CELL FUNCTION; GENE EXPRESSION; GENETIC REGULATION; GENOME ANALYSIS; LEARNING; MATHEMATICAL ANALYSIS; MOLECULAR BIOLOGY; MOLECULAR MODEL; PATHOGENESIS; PRIORITY JOURNAL;

COMPUTATIONAL BIOLOGY; DATA INTERPRETATION, STATISTICAL; GENE EXPRESSION PROFILING; MODELS, GENETIC;

EID: 4544243999     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/cmb.2004.11.1135     Document Type: Article

References (30)

1. Aspland, S.E., Bendall, H.H., and Murre, C. 2001. The role of E2A-PBX1 in leukemogenesis. Oncogene 20, 5708-5717.
2. Brown, P.O., and Botstein, D. 1999. Exploring the new world of the genome with DNA microarrays. Nature Genet. 21, 33-37.
3. Chickering, D.M., Geiger, D., and Heckerman, D. 1994. Learning Bayesian Networks in NP-Hard. Technical report MSR-TR-94-17, Microsoft Research.
4. Cooper, G., and Herskovits, E. 1991. A Bayesian method for constructing Bayesian belief networks from databases. Uncertainty in Artificial Intelligence, 86-94.
5. de Jong, H. 2002. Modeling and simulation of genetic regulatory systems: A literature review. J. Comp. Biol. 9(1), 67-103.
6. Dechter, R. 1996. Bucket elimination: A unifying framework for probabilistic inference. Uncertainty in Artificial Intelligence, 211-219.
7. Dejori, M., and Stetter, M. 2003. Bayesian inference of genetic networks from gene-expression data: Convergence and reliability. Proc. 2003 Int. Conf. on Artificial Intelligence (IC-AI'03), 323-327.
8. Dulbecco, R. 1986. A turning point in cancer research: Sequencing the human genome. Science 231, 1055-1056.
9. Efron, B., and Tibshirani, R.J. 1993. An Introduction to the Bootstrap, Chapman and Hall, New York.
10. Eisen, M.B., Spellman, P.T., Brown, P.O., and Botstein, D. 1998. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95(25), 14863-14868.
11. Fainstein, E., Marcelle, C., Rosner, A., Canaani, E., Gale, R.P., Dreazen, O., Smith, S.D., and Croce, C.M. 1987. A new fused transcript in Philadelphia chromosome positive acute lymphoblastic leukemia. Nature 330, 386-388.
12. Fodor, S.P., Rava, R.P., Huang, X.C., Pease, A.C., Holmes, C.P., and Adams, C.L. 1993. Multiplexed biochemical assays with biological chips. Nature 364, 555-556.
13. Friedman, N., Linial, M., Nachman, I., and Pe'er, D. 2000. Using Bayesian networks to analyze expression data. J. Comp. Biol. 7, 601-620.
14. Golub, T.R., Slonim, D.K., Tamayo, P., Gaasenbeek, M., Mesirov, J.P., et al. 1999. Molecular classification of cancer: Class discovery and class prediction by gene-expression monitoring. Science 286(5439), 531-537.
15. Hanahan, D., and Weinberg, R.A. 2000. The hallmarks of cancer. Cell 100, 57-70.
16. Hartemink, A.J., Gifford, D.K., Jaakkola, T.S., and Young, R.A. 2001. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Proc. 6th Pacific Symposium on Biocomputing, 422-433.
17. Heckerman, D., Geiger, D., and Chickering, D. 1995. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 20, 197-243.
18. Imoto, S., Goto, T., and Miyano, S. 2002. Estimation of genetic networks and functional structures between genes by using Bayesian network and nonparametric regression. Pacific Symposium on Biocomputing, 175-186.
19. Imoto, S., Higuchi, T., Goto, T., Tashiro, K., Kuhara, S., and Miyano, S. 2003. Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. Proc. 2nd Computational Systems Bioinformatics, 104-113.
20. Kirkpatrick, S., Gelatt, C.D., and Vecchi, M.P. 1983. Optimization by simulated annealing. Science 220(4598), 671-680.
21. Pearl, J. 1998. Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann, San Francisco, CA.
22. Pe'er, D., Regev, A., Elidan, G., and Friedman, N. 2001. Inferring subnetworks from perturbed expression profiles. Bioinformatics 338(1), 1-9.
23. Schena, M., Shalon, D., Davis, R., and Brown, P.O. 1995. Quantitative monitoring of gene-expression patterns with a cDNA microarray. Science 270, 467-470.
24. Segal, E., Shapira, M., Regev, A., Pe'er, D., Botstein, D., Koller, D., and Friedman, N. 2003. Module networks: Identifying regulatory modules and their condition-specific regulators from gene-expression data. Nature Genet. 34(2), 166-176.
25. Shachter, R. 1998. Bayes-ball: The rational pastime (for determining irrelevance and requisite information in belief networks and influence diagrams. Proc. 14th Conf. on Uncertainty and Artificial Intelligence (UAI), vol. 14, 480-487.
26. Steck, H. 2001. Constraint-based structural learning in Bayesian networks using finite data sets. Doctoral thesis, Technical University of Munich, Munich, Germany.
27. Stetter, M., Deco, G., and Dejori, M. 2003. Large-scale computational modeling of genetic regulatory networks. Artificial Intell. Rev. 20, 75-93.
28. van Duk, M.A., Voorhoeve, P.M., and Murre, C. 1993. PBX1 is converted into a transcriptional activator upon acquiring the N-terminal region of E2A in pre-b-cell acute lympohblastic leukemia. Proc. Natl. Acad. Sci. USA 90, 6061-6065.
29. Yeoh, E.-J., Ross, M.E., Shurtleff, S.A., Williams, W.K., et al. 2002. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 1, 133-143.
30. Zoubir, A.M. 1993. Bootstrap: Theory and applications, in F.T. Luk, ed., Advanced Signal Processing Algorithms, Architectures and Algorithms, vol. 2027 of Proceedings of SPIE.