An evaluation of a system that recommends microarray experiments to perform to discover gene-regulation pathways

Artificial Intelligence in Medicine

Volumn 31, Issue 2, 2004, Pages 169-182

  Yoo, Changwon ^a   Cooper, Gregory F ^b  

^a UNIVERSITY OF MONTANA   (United States)

^b UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

Author keywords

Causal Bayesian networks; Causal discovery; Microarray study design; Systems biology

Indexed keywords

DATA REDUCTION; GENES; MATHEMATICAL MODELS; PROBABILITY; STATISTICAL METHODS;

GENE KNOCKOUT; MICROARRAYS;

MEDICINE;

ANALYTIC METHOD; ARTICLE; BAYES THEOREM; DNA MICROARRAY; EVALUATION; GENE CONTROL; GENE EXPRESSION; GENE EXPRESSION DATA USING EXPECTED VALUE OF EXPERIMENTATION; KNOCKOUT GENE; METHODOLOGY; PRIORITY JOURNAL; REGULATORY MECHANISM; SIMULATION;

ANIMALS; ARTIFICIAL INTELLIGENCE; DECISION TREES; FORECASTING; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; HUMANS; MODELS, THEORETICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS;

EID: 3042632570     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2004.01.018     Document Type: Article

References (50)

1. Cooper G.F., Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Machine Learning. 9:1992;309-347
2. Heckerman D. A Bayesian approach to learning causal networks. In: Proceedings of the Conference on uncertainty in artificial intelligence. Morgan Kaufmann; 1995.
3. Spirtes P, Glymour C, Scheines R. Causation, prediction, and search. 2nd ed. Cambridge, MA: MIT Press; 2000.
4. Spiegelhalter D.J., Freedman L.S., Parmar M.K.B. Bayesian approach to randomized trials. J. R. Stat. Soc. 157(Part 3):1994;357-416
5. Berry DA, Stangl DK. Bayesian methods in health-related research. In: Berry DA, Stangl DK, editors. Bayesian Biostatistics. New York: Marcel Dekker; 1996. p. 3-66.
6. Friedman LM, Furberg CD, DeMets DL. Sample size. In: Fundamentals of clinical trials. 3rd ed. St. Louis: Mosby-Year book; 1996. Chapter 7. p. 94-129.
7. Karp P.D., et al. Integrated pathway/genome database and their role in drug discovery. Trends Biotechnol. 17(7):1999;275-281
8. Kerr M.K., Churchill G.A. Experimental design for gene expression microarrays. Biostatistics. 2:2001;183-201
9. Karp RM, Stoughton R, Yeung KY. Algorithms for choosing differential gene expression experiments. Res Comput Biol 1999.
10. Ideker T, Thorsson V, Karp RM. Discovery of regulatory interactions through perturbation: inference and experimental design. In: Pacific Symposium Biocompution. 2000.
11. Tong S, Koller D. Active learning for structure in Bayesian networks. In: International Joint Conference on Artificial Intelligence. Seattle, WA; 2001.
12. Brown P.O., Botstein D. Exploring the new world of the genome with DNA microarrays. Nat. Genet. 21(Supplement):1999;33-37
13. Lipshutz R.J., et al. High density synthetic oligonucleotide arrays. Nat. Genet. 21(Supplement):1999;20-24
14. Yoo C, Cooper G. Discovery of gene-regulation pathways using local causal search. In: AMIA. San Antonio, Texas, 2002.
15. Pearl J. Probabilistic reasoning in intelligent systems. In: Brachman RJ, editor. Representation and reasoning. San Mateo, CA: Morgan Kaufmann; 1988.
16. Heckerman D., Geiger D., Chickering D. Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning. 20:1995;197-243
17. Yoo C, Thorsson V, Cooper GF. Discovery of a gene-regulation pathway from a mixture of experimental and observational DNA microarray data. In: Pacific Symposium on biocomputing. Maui, Hawaii: World Scientific; 2002.
18. Yoo C, Cooper G. Causal discovery of latent-variable models from a mixture of experimental and observational data. In: CBMI Research Report CBMI-173. Pittsburgh, PA: Center for Biomedical Informatics; 2001.
19. Yoo C. Expected value of experimentation in causal discovery from gene expression studies. Ph.D. dissertation, 2002.
20. Henrion M. Propagating uncertainty in Bayesian networks by probabilistic logic sampling. In: Lemmer JF, Kanal LN, editors. Uncertainty in artificial intelligence 2. North-Holland: Amsterdam; 1988. p. 149-63.
21. Heckerman D, Horvitz E, Middleton B. An approximate nonmyopic computation for value of information. In: Proceedings of the Seventh Conference on uncertainty in artificial intelligence. 1991.
22. Chavez T, Henrion M. Efficient estimation of the value of information in Monte Carlo models. In: Uncertainty in artificial intelligence. 1994.
23. von Neumann J, Morgenstern O. Theory of games and economic behavior. Princeton NJ: Princeton University Press; 1944.
24. Keeney RL, Raiffa H. Decisions with multiple objectives: preference and value tradeoffs. New York: John Wiley; 1976.
25. Achcar J.A. Use of Bayesian analysis to design of clinical trials with one treatment. Commun. Stat. Theory Methods. 13:1984;1693-1707
26. Pearl J. Causality: models, reasoning, and inference. Cambridge, UK: Cambridge University Press; 2000.
27. Heckerman D, Meek C, Cooper GF. A Bayesian approach to causal discovery. In: Glymour C, Cooper GF, editors. Computation, causation, and discovery. Menlo Park, CA: AAAI Press; 1999. p. 141-65.
28. Spellman P.T., et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell. 9:1998;3273-3297
29. Ideker T., et al. Integrated genomic and proteomic analysis of a systematically perturbed metabolic network. Science. 292:2001;929-934
30. Michaels GS, et al. Cluster analysis and data visualization of large-scale gene expression data. Pacific Symposium on biocomputing. 1998.
31. Herwig R., et al. Large-scale clustering of cDNA-fingerprinting data. Genome Res. 9:1999;1093-1105
32. Golub T.R., et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 286:1999;531-537
33. Tsang J. Gene expression, DNA arrays, and genetic network. In: Unpublished manuscript, Bioinformatics Laboratory at University of Waterloo; 1999.
34. Dutilh B. Gene networks from microarray data. In: Unpublished manuscript. Literature thesis at Utrecht University; 1999.
35. de Jong H. Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9(1):2002;67-103
36. Smolen P., Baxter D.A., Byrne J.H. Modeling transciptional control in gene networks - methods. Bull. Math. Biol. 62:2000;247-292
37. Shrager J, Langley P. In: Shrager J, Langley P, editors. Computational models of discovery and theory formation. San Mateo, CA: Morgan Kaufman; 1990.
38. Karp PD. Hypothesis formation as design. In: Shrager J, Langley P, editors. Computational models of discovery and theory formation. San Mateo, CA: Morgan Kaufman; 1990. p. 276-317.
39. Cooper GF, Yoo C. Causal discovery from a mixture of experimental and observational data. In: Proceedings of the Conference on uncertainty in artificial intelligence. Morgan Kaufmann; 1999.
40. Akutsu T, Miyano S, Kuhara S. Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. In: Pacific Symposium on biocomputing. Hawaii; 1999.
41. Tomita M., et al. E-CELL: software environment for whole cell simulation. Bioinformatics. 15(1):1999;72-84
42. Scheines R, Ramsey J. Gene simulator. In: Available at: http://www.phil.cmu.edu/tetrad/.2001.
43. Saavedra R, Glymour C. A regulatory network simulator. In: Simulator based on (Yuh et al., 1998) under development. 2001.
44. Edwards R., Glass L. Combinatorial explosion in model gene networks. Chaos. 10:2000;691-704
45. Kauffman S. Origins of order - self-organization and selection in evolution. Oxford University Press; 1993.
46. Efron B. The jacknife, the boostrap and other resampling plans. Soc. Ind. Appl. Math. 1092:1982;2-5
47. Hegde P., et al. A concise guide to cDNA microarray analysis. Biotechniques. 29(3):2000;548-562
48. Murphy K, Mian S. Modelling gene expression data using dynamic Bayesian networks. In: Technical report, U.B. Department of Computer Science; 1999.
49. Friedman N, et al. Using Bayesian networks to analyze expression data. J Computat Biol 2000.
50. Spirtes P, Glymour C, Scheines R. Constructing Bayesian network models of gene expression networks from microarray data. In: To appear in the Proceedings of the Atlantic Symposium on computational biology. Genome Information Systems and Technology; 2001.