Joint estimation of multiple related biological networks

Annals of Applied Statistics

Volumn 8, Issue 3, 2014, Pages 1892-1919

  Oates, Chris J ^a   Korkola, Jim ^b   Gray, Joe W ^b   Mukherjee, Sach ^c  

^a UNIVERSITY OF WARWICK   (United Kingdom)

^b OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

^c UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Bayesian network; Belief propagation; Hierarchical model; Information sharing

Indexed keywords

EID: 84907838128     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/14-AOAS761     Document Type: Article

References (38)

1. THE 1000 GENOMES PROJECT CONSORTIUM (2010). A map of human genome variation from population-scale sequencing. Nature 467 1061-1073.
2. AKBANI, R. et al. (2014). A pan-cancer proteomic perspective on the Cancer Genome Atlas. Nat. Commun. 5 3887.
3. BARRETINA, J., CAPONIGRO, G., STRANSKY, N., VENKATESAN, K., MARGOLIN, A. A., KIM, S., WILSON, C. J., LEHÁR, J., KRYUKOV, G. V., SONKIN, D., REDDY, A., LIU, M., MURRAY, L., BERGER, M. F., MONAHAN, J. E., MORAIS, P., MELTZER, J., KOREJWA, A., Jané-Valbuena, J., MAPA, F. A., THIBAULT, J., Bric-Furlong, E., RAMAN, P., SHIPWAY, A., ENGELS, I. H., CHENG, J., YU, G. K., YU, J., PETER ASPESI, J., DE SILVA, M., JAGTAP, K., JONES, M. D., WANG, L., HATTON, C., PALESCANDOLO, E., GUPTA, S., MAHAN, S., SOUGNEZ, C., ONOFRIO, R. C., LIEFELD, T., MACCONAILL, L., WINCKLER, W., REICH, M., LI, N., MESIROV, J. P., GABRIEL, S. B., GETZ, G., ARDLIE, K., CHAN, V., MYER, V. E., WEBER, B. L., PORTER, J., WARMUTH, M., FINAN, P., HARRIS, J. L., MEYERSON, M., GOLUB, T. R., MORRISSEY, M. P., SELLERS, W. R., SCHLEGEL, R. and GARRAWAY, L. A. (2012). The Cancer Cell Line Encyclopedia enables predictive modelling of anti- cancer drug sensitivity. Nature 483 603-607.
4. BAYARRI, M. J., BERGER, J. O., FORTE, A. and García-Donato, G. (2012). Criteria for Bayesian model choice with application to variable selection. Ann. Statist. 40 1550-1577. MR3015035
5. BOUTILIER, C., FRIEDMAN, N., GOLDSZMIDT, M. and KOLLER, D. (1996). Context-specific independence in Bayesian networks. In Uncertainty in Artificial Intelligence (Portland, OR, 1996) 115-123. Morgan Kaufmann, San Francisco, CA. MR1617129
6. BUTTE, A. J., TAMAYO, P., SLONIM, D., GOLUB, T. R. and KOHANE, I. S. (2000). Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc. Natl. Acad. Sci. USA 97 12182-12186.
7. THE CANCER GENOME ATLAS NETWORK (2012). Comprehensive molecular portraits of human breast tumours. Nature 490 61-70.
8. CAO, J., SCHNEEBERGER, K., OSSOWSKI, S., GÜNTHER, T., BENDER, S., FITZ, J., KOENIG, D., LANZ, C., STEGLE, O., LIPPERT, C., WANG, X., OTT, F., MÜLLER, J., Alonso-Blanco, C., BORGWARDT, K., SCHMID, K. J. and WEIGEL, D. (2011). Whole-genome sequencing of multiple Arabidopsis thaliana populations. Nat. Genet. 43 956-963.
9. CURTIS, C. et al. (2012). The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486 346-352.
10. DANAHER, P., WANG, P. and WITTEN, D. M. (2014). The joint graphical lasso for inverse covariance estimation across multiple classes. J. R. Stat. Soc. Ser. B Stat. Methodol. 76 373-397. MR3164871
11. DONDELINGER, F., LÈBRE, S. and HUSMEIER, D. (2013). Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure. Mach. Learn. 90 191-230. MR3015742
12. FORBES, S. A. et al. (2011). COSMIC: Mining complete cancer genomes in the catalogue of somatic mutations in cancer. Nucleic Acids Res. 39 D945-D950.
13. GEIGER, D. and HECKERMAN, D. (1996). Knowledge representation and inference in similarity networks and Bayesian multinets. Artificial Intelligence 82 45-74. MR1391056
14. GEORGE, E. I. and FOSTER, D. P. (2000). Calibration and empirical Bayes variable selection. Biometrika 87 731-747. MR1813972
15. HENNESSY, B. T. et al. (2010). A technical assessment of the utility of reverse phase protein arrays for the study of the functional proteome in nonmicrodissected human breast cancer. Clin. Proteom. 6 129-151.
16. HILL, S. M. et al. (2012). Bayesian inference of signaling network topology in a cancer cell line. Bioinformatics 28 2804-2810.
17. IBRAHIM, J. G. and CHEN, M.-H. (2000). Power prior distributions for regression models. Statist. Sci. 15 46-60. MR1842236
18. IDEKER, T. and KROGAN, N. J. (2012). Differential network biology. Mol. Syst. Biol. 8 565.
19. IMOTO, S. et al. (2003). Combining microarrays and biological knowledge for estimating gene net- works via Bayesian networks. In Proceedings of the IEEE Computer Society Bioinformatics Conference 104-113.
20. KSCHISCHANG, F. R., FREY, B. J. and LOELIGER, H.-A. (2001). Factor graphs and the sumproduct algorithm. IEEE Trans. Inform. Theory 47 498-519. MR1820474
21. LIANG, F., PAULO, R., MOLINA, G., CLYDE, M. A. and BERGER, J. O. (2008). Mixtures of g priors for Bayesian variable selection. J. Amer. Statist. Assoc. 103 410-423. MR2420243
22. MAHER, B. (2012). ENCODE: The human encyclopaedia. Nature 489 46-48.
23. MUKHERJEE, S. and SPEED, T. P. (2008). Network inference using informative priors. Proc. Natl. Acad. Sci. USA 105 14313-14318.
24. MURPHY, K. P. (2002). Dynamic Bayesian networks: Representation, inference and learning. Ph.D. thesis, California Univ., Berkeley. MR2704368
25. NEVE, R. M. et al. (2006). A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell 10 515-527.
26. Niculescu-Mizil, A. and CARUANA, R. (2007). Inductive transfer for Bayesian network structure learning. J. Mach. Learn. Res. Workshop and Conference Proceedings 27 339-346.
27. OATES, C. J. and MUKHERJEE, S. (2014). Joint structure learning of multiple non-exchangeable networks. J. Mach. Learn. Res. Workshop and Conference Proceedings 33 687-695. In Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS 2014).
28. OATES, C. J., COSTA, L. and NICHOLS, T. E. (2014). Towards a multi-subject analysis of neural connectivity. Neural Comput. To appear.
29. OATES, C. J., KORKOLA, J., GRAY, J. W. and MUKHERJEE, S. (2014a). Supplement to “Joint estimation of multiple related biological networks.” DOI: 10.1214/14-AOAS761SUPPA.
30. OATES, C. J., KORKOLA, J., GRAY, J. W. and MUKHERJEE, S. (2014b). Supplement to “Joint estimation of multiple related biological networks.” DOI: 10.1214/14-AOAS761SUPPB.
31. OYEN, D. and LANE, T. (2013). Bayesian discovery of multiple Bayesian networks via transfer learning. In IEEE Thirteenth International Conference on Data Mining (ICDM) 577-586. IEEE, Dallas.
32. PEARL, J. (1982). Reverend bayes on inference engines: A distributed tree approach. In Proceedings of the Second National Conference on Artificial Intelligence 133-136. Pittsburg.
33. PEARL, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge Univ. Press, Cambridge. MR1744773
34. PENFOLD, C. A., Buchanan-Wollaston, V., Denby, K. J. and Wild, D. L. (2012). Non- parametric Bayesian inference for perturbed and orthologous gene regulatory networks. Bioinformatics 28 i233-i241.
35. SPENCER, S., HILL, S. M. and MUKHERJEE, S. (2012). Dynamic Bayesian networks for interventional data. CRiSM Working Paper 12:24, Warwick Univ., UK.
36. WERHLI, A. V. and HUSMEIER, D. (2008). Gene regulatory network reconstruction by Bayesian integration of prior knowledge and/or different experimental conditions. J. Bioinform. Comput. Biol. 6 543-572.
37. XU, T. R. et al. (2010). Inferring signaling pathway topologies from multiple perturbation measurements of specific biochemical species. Sci. Sig. 3 ra20.
38. ZELLNER, A. (1986). On assessing prior distributions and Bayesian regression analysis with g- prior distributions. In Bayesian Inference and Decision Techniques. Stud. Bayesian Econometrics Statist. 6 233-243. North-Holland, Amsterdam. MR0881437