A control study to evaluate a computer-based microarray experiment design recommendation system for gene-regulation pathways discovery

Journal of Biomedical Informatics

Volumn 39, Issue 2, 2006, Pages 126-146

  Yoo, Changwon ^a   Cooper, Gregory F ^b   Schmidt, Martin ^b  

^a UNIVERSITY OF MONTANA   (United States)

^b UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

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

Indexed keywords

ARRAYS; COMPUTER AIDED ANALYSIS; COMPUTER AIDED DESIGN; CONTROL SYSTEM ANALYSIS; PROBABILITY;

CAUSAL BAYESIAN NETWORKS; CAUSAL DISCOVERY; MICROARRAY STUDY DESIGN; SYSTEMS BIOLOGY;

GENETIC ENGINEERING;

DNA; PROTEIN KINASE;

ARTICLE; BAYES THEOREM; CAUSAL MODELING; COMPUTER AIDED DESIGN; DNA MICROARRAY; EXPERIMENTAL DESIGN; GENE CONTROL; GENE EXPRESSION; INTERVENTION STUDY; MATHEMATICAL COMPUTING; METHODOLOGY; MICROARRAY ANALYSIS; OUTCOME ASSESSMENT; PRIORITY JOURNAL; SIMULATION;

ANIMALS; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; HUMANS; MODELS, BIOLOGICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PROTEIN INTERACTION MAPPING; PROTEOME; RESEARCH DESIGN; SIGNAL TRANSDUCTION; SOFTWARE DESIGN;

EID: 33644959150     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2005.05.011     Document Type: Article

References (63)

1. G.F. Cooper , and E. Herskovits A Bayesian method for the induction of probabilistic networks from data Mach Learn 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. p. 285-95.
3. P. Spirtes , C. Glymour , and R. Scheines Causation, prediction, and search 2nd ed. 2000 MIT Press Cambridge, MA
4. S.D. Bay , J. Shrager , A. Pohorille , and P. Langley Revising regulatory networks: from expression data to linear causal models J Biomed Inform 35 2002 289 297
5. D.J. Spiegelhalter , L.S. Freedman , and M.K.B. Parmar Bayesian approach to randomized trials J R Stat Soc 157 Part 3 1994 357 416
6. D.A. Berry , and D.K. Stangl Bayesian methods in health-related research D.A. Berry D.K. Stangl Bayesian biostatistics 1996 Marcel Dekker New York 3 66
7. Friedman LM, Furberg CD, DeMets DL, Chapter 7. Sample size, In: Fundamentals of clinical trials. 3rd ed. Mosby-Year book: St. Louis; 1996. p. 94-129.
8. P.D. Karp , M. Krummenacker , S. Paley , and J. Wagg Integrated pathway/genome database and their role in drug discovery Trends Biotechnol 17 7 1999 275 281
9. M.K. Kerr , and G.A. Churchill Experimental design for gene expression microarrays Biostatistics 2 2001 183 201
10. R.M. Karp , R. Stoughton , and K.Y. Yeung Algorithms for choosing differential gene expression experiments Res Comput Biol 1999 208 217
11. Ideker T, Thorsson V. Karp RM. Discovery of regulatory interactions through perturbation: inference and experimental design. In: Pacific symposium biocompution. 2000. p. 305-16.
12. Tong S, Koller D. Active learning for structure in Bayesian networks. In: International joint conference on artificial intelligence. Seattle; WA: 2001.
13. P.O. Brown , and D. Botstein Exploring the new world of the genome with DNA microarrays Nat Genet 21 Suppl 1999 33 37
14. R.J. Lipshutz , S.P.A. Fodor , T.R. Gingeras , and D.J. Lockhart High density synthetic oligonucleotide arrays Nat Genet 21 Suppl 1999 20 24
15. Yoo C, Cooper G. Discovery of gene-regulation pathways using local causal search. In: AMIA. San Antonio, Texas; 2002. p. 914-8.
16. Pearl J. Probabilistic Reasoning in Intelligent Systems. In: Brachman RJ, editor. Representation and reasoning. San Mateo, CA: Morgan Kaufmann; 1988.
17. D. Heckerman , D. Geiger , and D. Chickering Learning Bayesian networks: the combination of knowledge and statistical data Mach Learn 20 1995 197 243
18. 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. p. 498-509.
19. Yoo C, Cooper G. 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: 2001.
20. Yoo C, Expected value of experimentation in causal discovery from gene expression studies. Ph.D. dissertation, 2002.
21. Henrion M. Propagating uncertainty in Bayesian networks by probabilistic logic sampling. In: Lemmer JF, Kanal LN, editor. Uncertainty in artificial intelligence 2. North-Holland, Amsterdam. 1988. p. 149-63.
22. Heckerman D, Horvitz E, Middleton B. An approximate non-myopic computation for value of information. In: Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence. 1991.
23. Chavez T, Henrion M. Efficient estimation of the value of information in Monte Carlo models. In: Uncertainty in artificial intelligence. 1994. p. 119-27.
24. C. Yoo , and G. Cooper An evaluation of a system that recommends microarray experiments to perform to discover gene-regulation pathways J Artif Intell Med 31 2004 169 182
25. J. von Neumann , and O. Morgenstern Theory of games and economic behavior 1944 Princeton University Press Princeton, NJ
26. R.L. Keeney , and H. Raiffa Decisions, with multiple objectives: preference and value tradeoffs 1976 John Wiley New York
27. J.A. Achcar Use of Bayesian analysis to design of clinical trials with one treatment Commun Stat Theory Methods 13 1984 1693 1707
28. J. Pearl Causality: models, reasoning, and inference 2000 Cambridge University Press Cambridge, UK
29. D. Heckerman , C. Meek , and G.F. Cooper A Bayesian approach to causal discovery C. Glymour G.F. Cooper Computation causation and discovery 1999 AAAI Press Menlo Park, CA 141 165
30. P.T. Spellman , G. Sherlock , M.Q. Zhang , V.R. Iyer , K. Anders , and M.B. Eisen Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization Mol Biol Cell 9 1998 3273 3297
31. T. Ideker , V. Thorsson , J.A. Ranish , R. Christmas , J. Buhler , and R. Bumgarner Integrated genomic and proteomic analysis of a systematically perturbed metabolic network Science 292 2001 929 934
32. Michaels GS, Carr DB, Askenazi M, Fuhrman S, Wen X, Somogyi R. Cluster analysis and data visualization of large-scale gene expression data. In: Pacific symposium on biocomputing. 1998. p. 42-53.
33. R. Herwig , A.J. Poustka , C. Muller , C. Bull , H. Lehrach , and J. O'Brien Large-scale clustering of cDNA-fingerprinting data Genome Res 9 1999 1093 1105
34. T.R. Golub , D.K. Slonim , P. Tamayo , C. Huard , M. Caasenbeek , and J.P. Mesirov Molecular classification of cancer: class discovery and class prediction by gene expression monitoring Science 286 1999 531 537
35. P.R. Bushel , H.K. Hamadeh , L. Bennett , J. Green , A. Ableson , and S. Misener Computational selection of distinct class- and subclass-specific gene expression signatures J Biomed Inform 35 2003 160 170
36. Tsang J. Gene expression, DNA arrays, and genetic network. In: Unpublished manuscript Bioinformatics Laboratory at University of Waterloo. 1999.
37. Dutilh, B. Gene networks from microarray data. In: Unpublished manuscript. Literature thesis at Utrecht University. 1999.
38. H. de Jong Modeling and simulation of genetic regulatory systems: a literature review J Comput Biol 9 1 2002 67 103
39. N. Friedman Inferring cellular networks using probabilistic graphical models Science 303 5659 2004 799 805
40. P. Smolen , D.A. Baxter , and J.H. Byrne Modeling transcriptional control in gene networks-methods, recent results and future directions Bull Math Biol 62 2000 247 292
41. T. Chu , C. Glymour , R. Scheines , and P. Spirtes A statistical problem for inference to regulatory structure from associations of gene expression measurement with microarrays 2003 Carnegie Mellon University
42. Shrager J, Langley P. Computational models of discovery and theory formation, In: Shrager J, Langley P, editor. San Mateo, CA: Morgan Kaufman; 1990.
43. P.D. Karp Hypothesis formation as design J. Shrager P. Langley Computational models of discovery and theory formation 1990 Morgan Kaufman San Mateo, CA 276 317
44. G.F. Cooper , and C. Yoo Causal discovery from a mixture of experimental and observational data Proceedings of the conference on uncertainty in artificial intelligence 1999 Morgan Kaufmann 116 125
45. 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. p. 17-28.
46. M. Tomita , K. Hashimoto , K. Takahashi , T. Shimizu , Y. Matsuzaki , and F. Miyoshi E-CELL: software environment for whole cell simulation Bioinformatics 15 1 1999 72 84
47. Scheines R. Ramsey J. Gene simulator. 2001: Available from: http://www.phil.cmu.edu/projects/tetrad/tetrad4.html .
48. Saavedra R, Glymour C. A regulatory network simulator. In: Simulator based on (Yuh et al. 1998) under development. 2001.
49. R. Edwards , and L. Glass Combinatorial explosion in model gene networks Chaos 10 2000 691 704
50. S. Kauffman Origins of order-self-organization and selection in evolution 1993 Oxford University Press Londan
51. R. McCartney , and M. Schmidt Regulation of Snf1 kinase. Activation requires phosphorylation of threonine 210 by an upstream kinase as well as a distinct step mediated by the Snf4 subunit J. Biol. Chem. 276 39 2001 36460 36466
52. M. Schmidt , R. McCartney , X. Zhang , T. Tillman , H. Solimeo , and S. Wolfl Std1 and Mth1 proteins interact with the glucose sensors to control glucose-regulated gene expression in Saccharomyces cerevisiae Mol Cell Biol 19 1999 4561 4571
53. M. Schmidt , and R. McCartney Beta-subunits of Snf1 kinase are required for kinase function and substrate definition EMBO J 19 18 2000 4936 4943
54. A. Gasch , P. Spellman , C. Kao , O. Carmel-Harel , M. Eisen , and G. Storz Genomic expression programs in the response of yeast cells to environmental changes Mol Biol Cell 11 12 2000 4241 4257
55. 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.
56. deGroot Probability and Statistics 1986 Addison-Wesley Reading, Massachusetts
57. P. Hegde , R. Qi , K. Abernathy , C. Gay , S. Dharap , and R. Gaspard A concise guide to cDNA microarray analysis Biotechniques 29 3 2000 548 562
58. Murphy K, Mian S. Modelling gene expression data using dynamic Bayesian networks. In: Technical report, U.B. Department of Computer Science, Editor. 1999.
59. N. Friedman , M. Linial , I. Nachman , and D. Pe'er Using Bayesian networks to analyze expression data J Comput Biol 7 2000 601 620
60. S.A. Racunas , N.H. Shah , I. Albert , and N.V. Fedoroff HyBrow: a prototype system for computer-aided hypothesis evaluation Bioinformatics 20 Suppl. 1 2004 257 264
61. I. Sim , B. Olasov , and S. Carini An ontology of randomized controlled trials for evidence-based practice: content specification and evaluation using the competency decomposition method J Biomed Inform 37 2 2004 108 119
62. KEGG, KEGG (Kyoto encyclopedia of genes and genomes), in Available from: http://www.genome.ad.jp/kegg/ .
63. WIT, WIT (What is there?) is a www-based system to support the creation of function assignments made to genes and the development of metabolic models, in Available from: http://wit.mcs.anl.gov/WIT2/ .