A hypothesis test for equality of Bayesian network models

Eurasip Journal on Bioinformatics and Systems Biology

Volumn 2010, Issue , 2010, Pages

  Almudevar, Anthony ^a  

^a UNIVERSITY OF ROCHESTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 78349291185     PISSN: 16874145     EISSN: 16874153     Source Type: Journal    
DOI: 10.1155/2010/947564     Document Type: Article

References (45)

1. Dougherty E. R, Shmulevich I., Chen J., Wang Z. J., Genomic Signal Processing and Statistics 2005 2 New York, NY, USA Hindawi Publishing Corporation EURASIP Book Series on Signal Processing and Communications
2. Shmulevich I., Dougherty E. R., Genomic Signal Processing 2007 Princeton, NJ, USA Princeton University Press
3. Emmert-Streib F., Dehmer M., Emmert-Streib F., Dehmer M., Detecting pathological pathways of a complex disease by a comparitive analysis of networks Analysis of Microarray Data: A Network-Based Approach 2008 Weinheim, Germany Wiley-VCH 285 305
4. Mootha V. K., Lindgren C. M., Eriksson K.-F., Subramanian A., Sihag S., Lehar J., Puigserver P., Carlsson E., Ridderstrle M., Laurila E., Houstis N., Daly M. J., Patterson N., Mesirov J. P., Golub T. R., Tamayo P., Spiegelman B., Lander E. S., Hirschhorn J. N., Altshuler D., Groop L. C., PGC-1 -responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes Nature Genetics 2003 34 3 267 273
5. Subramanian A., Tamayo P., Mootha V. K., Mukherjee S., Ebert B. L., Gillette M. A., Paulovich A., Pomeroy S. L., Golub T. R., Lander E. S., Mesirov J. P., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles Proceedings of the National Academy of Sciences of the United States of America 2005 102 43 15545 15550
6. Subramanian A., Kuehn H., Gould J., Tamayo P., Mesirov J. P., GSEA-P: a desktop application for gene set enrichment analysis Bioinformatics 2007 23 23 3251 3253
7. Sebastiani P., Abad M., Ramoni M. F., Dougherty E. R., Shmulevich I., Chen J., Wang Z. J., Bayesian networks for genomic analysis Genomic Signal Processing and Statistics 2005 New York, NY, USA Hindawi Publishing Corporation EURASIP Book Series on Signal Processing and Communications
8. Friedman N., Linial M., Nachman I., Pe'er D., Using Bayesian networks to analyze expression data Journal of Computational Biology 2000 7 3-4 601 620
9. Needham C. J., Bradford J. R., Bulpitt A. J., Westhead D. R., A primer on learning in Bayesian networks for computational biology. PLoS Computational Biology 2007 3 8 e129
10. Chu T., Glymour C., Scheines R., Spirtes P., A statistical problem for inference to regulatory structure from associations of gene expression measurements with microarrays Bioinformatics 2003 19 9 1147 1152
11. Cowell R. G., Dawid P., Lauritzen S. L., Spiegelhalter D. J., Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks 1999 New York, NY, USA Spring Information Science and Statistics
12. Cowell R. G., Efficient maximum likelihood pedigree reconstruction Theoretical Population Biology 2009 76 4 285 291
13. Silander T., Myllymki P., Dechter R., Richardson T., A simple approach to finding the globally optimal bayesian network structure Proceedings of the 22nd Conference on Artificial intelligence (UAI '06) 2006 AUAI Press 445 452
14. Chickering D. M., Fisher D., Lenz H., Learning Bayesian net- works is NP-complete Learning from Data: Artificial Intelligence and Statistics V 1996 New York, NY, USA Springer 121 130
15. Chow C. K., Liu C. N., Approximating discrete probability distributions with dependence trees IEEE Transactions on Information Theory 1968 14 462 467
16. Abbeel P., Koller D., Ng A. Y., Learning factor graphs in polynomial time and sample complexity Journal of Machine Learning Research 2006 7 1743 1788
17. Murphy K., Software packages for graphical models bayesian networks Bulletin of the International Society for Bayesian Analysis 2007 14 13 15
18. Teyssier M., Koller D., Ordering-based search: a simple and effective algorithm for learning bayesian networks Proceedings of the 21st Conference on Uncertainty in AI (UAI '05) 2005 584 590
19. Papadimitriou C. H., Steiglitz K., Combinatorial Optimization: Algorithms and Complexity 1982 Englewood Cliffs, NJ, USA Prentice-Hall
20. Walsh A. H., Aspects of Statistical Inference 1996 New York, NY, USA John Wiley Sons
21. Efron B., Robbins, empirical Bayes and microarrays Annals of Statistics 2003 31 2 366 378
22. Besag J., Clifford P., Sequential monte carlo p -values Biometrika 1991 78 301 304
23. Lock R. H., A sequential approximation to a permutation test Communications in Statistics. Simulation and Computation 1991 20 1 341 363
24. Fay M. P., Follmann D. A., Designing Monte Carlo implementations of permutation or bootstrap hypothesis tests American Statistician 2002 56 1 63 70
25. Dudoit S., van der Laan M. J., Multiple Testing Procedures with Applications to Genomics 2008 New York, NY, USA Springer
26. Wald A., Sequential Analysis 1947 New York, NY, USA John Wiley Sons
27. Siegmund D., Sequential Analysis: Tests and Confidence Intervals 1985 New York, NY, USA Springer
28. Almudevar A., Exact confidence regions for species assignment based on DNA markers Canadian Journal of Statistics 2000 28 1 81 95
29. Zhou X., Kao M.-C.J., Wong W. H., Transitive functional annotation by shortest-path analysis of gene expression data Proceedings of the National Academy of Sciences of the United States of America 2002 99 20 12783 12788
30. Braun R., Cope L., Parmigiani G., Identifying differential correlation in gene/pathway combinations BMC Bioinformatics 2008 9, article no. 488
31. Barry W. T., Nobel A. B., Wright F. A., Significance analysis of functional categories in gene expression studies: a structured permutation approach Bioinformatics 2005 21 9 1943 1949
32. Jiang Z., Gentleman R., Extensions to gene set enrichment Bioinformatics 2007 23 3 306 313
33. Klebanov L., Glazko G., Salzman P., Yakovlev A., Xiao Y., A multivariate extension of the gene set enrichment analysis Journal of Bioinformatics and Computational Biology 2007 5 5 1139 1153
34. Goeman J. J., Bhlmann P., Analyzing gene expression data in terms of gene sets: methodological issues Bioinformatics 2007 23 8 980 987
35. Allison D. B., Cui X., Page G. P., Sabripour M., Microarray data analysis: from disarray to consolidation and consensus Nature Reviews Genetics 2006 7 1 55 65
36. Bild A., Febbo P. G., Application of a priori established gene sets to discover biologically important differential expression in microarray data Proceedings of the National Academy of Sciences of the United States of America 2005 102 43 15278 15279
37. Manoli T., Gretz N., Grne H.-J., Kenzelmann M., Eils R., Brors B., Group testing for pathway analysis improves comparability of different microarray datasets Bioinformatics 2006 22 20 2500 2506
38. Liu Q., Dinu I., Adewale A. J., Potter J. D., Yasui Y., Comparative evaluation of gene-set analysis methods BMC Bioinformatics 2007 8, article no. 431
39. Ackermann M., Strimmer K., A general modular framework for gene set enrichment analysis BMC Bioinformatics 2009 10, article no. 47
40. Efron B., Tibshirani R., On testing the significance of sets of genes Annals of Applied Statistics 2007 1 107 129
41. Goeman J. J., van de Geer S., de Kort F., van Houwellingen H. C., A global test for groups fo genes: testing association with a clinical outcome Bioinformatics 2004 20 1 93 99
42. Mansmann U., Meister R., Testing differential gene expression in functional groups: goeman's global test versus an ANCOVA approach Methods of Information in Medicine 2005 44 3 449 453
43. Tusher V. G., Tibshirani R., Chu G., Significance analysis of microarrays applied to the ionizing radiation response Proceedings of the National Academy of Sciences of the United States of America 2001 98 9 5116 5121
44. Dinu I., Potter J. D., Mueller T., Liu Q., Adewale A. J., Jhangri G. S., Einecke G., Famulski K. S., Halloran P., Yasui Y., Improving gene set analysis of microarray data by SAM-GS BMC Bioinformatics 2007 8, article 242
45. Almudevar A., A simulated annealing algorithm for maximum likelihood pedigree reconstruction Theoretical Population Biology 2003 63 2 63 75