The five-gene-network data analysis with local causal discovery algorithm using causal bayesian networks

Annals of the New York Academy of Sciences

Volumn 1158, Issue , 2009, Pages 93-101

  Yoo, Changwon ^a   Brilz, Erik M ^a  

^a UNIVERSITY OF MONTANA   (United States)

Author keywords

Causal Bayesian networks; Causal discovery; Gene networks; High throughput data analysis; Microarray data analysis

Indexed keywords

ARTICLE; BAYES THEOREM; EXCITATORY JUNCTION POTENTIAL; GENETIC ANALYSIS; INHIBITION KINETICS; METHODOLOGY; MICROARRAY ANALYSIS; NERVE CELL NETWORK; SCORING SYSTEM;

EID: 63849332382     PISSN: 00778923     EISSN: 17496632     Source Type: Book Series    
DOI: 10.1111/j.1749-6632.2008.03749.x     Document Type: Article

References (19)

1. Spirtes, P., C. Glymour R. Scheines. 2000. Causation, prediction, and search. 2nd ed. MIT Press. Cambridge, MA.
2. Heckerman, D., D. Geiger D. Chickering. 1995. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 20 : 197 243.
3. Mani, S., G. Cooper P. Spirtes. 2006. A Theoretical Study of Y Structures for Causal Discovery. In UAI. Cambridge, MA.
4. Heckerman, D. 1995. A Bayesian approach to learning causal networks. In Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann. San Mateo, CA, pp. 285 295.
5. Karp, P.D. 2001. Pathway databases: A case study in computational symbolic theories. Science 293 : 2040 2044.
6. Gifford, D.K. 2001. Blazing pathways through genetic mountains. Science 293 : 2049 2051.
7. Mjolsness, E. D. DeCoste. 2001. Machine learning for Science: State of art and future prospects. Science 293 : 2051 2055.
8. Kitano, H. 2002. Systems biology: A brief overview, March 1, 2002. Science 295 : 1662 1664.
9. Friedman, N. 2004. Inferring cellular networks using probabilistic graphical models. Science 303 : 799 805.
10. Wille, A. et al. 2004. Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana. Genome Biol. 5 : R92.1 R92.13.
11. Beal, M. et al. 2005. A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics 21 : 349 356.
12. Yoo, C. G. Cooper. 2004. An evaluation of a system that recommends microarray experiments to perform to discover gene-regulation pathways. J. Artif. Intell. Med. 31 : 169 182.
13. Cooper, G.F. C. Yoo. 1999. Causal discovery from a mixture of experimental and observational data. In Proceedings of the Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann. San Mateo, CA, pp. 116 125.
14. Cooper, G.F. E. Herskovits. 1992. A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9 : 309 347.
15. Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems. In Representation and Reasoning. R.J. Brachman, Ed. Morgan Kaufmann. San Mateo, CA.
16. Yoo, C. G. Cooper. 2001. Causal discovery of latent-variable models from a mixture of experimental and observational data. In Center for Biomedical Informatics Research Report CBMI-173. Center for Biomedical Informatics. Pittsburgh, PA. Online publication.
17. Yoo, C., V. Thorsson G.F. Cooper. 2002. Discovery of a gene-regulation pathway from a mixture of experimental and observational DNA microarray data. In Pacific Symposium on Biocomputing. World Scientific. Maui, Hawaii, pp. 498 509.
18. E. Brilz. 2008. Local Causal Discovery Algorithm Using Causal Bayesian Networks. Master's Thesis, University of Montana.
19. Yoo, C. G. Cooper. 2002. Discovery of gene-regulation pathways using local causal search. In AMIA. San Antonio, Texas, pp. 914 918.