Gene expression time course clustering with countably infinite hidden Markov models

Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

Volumn , Issue , 2006, Pages 23-30

  Beal, Matthew J ^a   Krishnamurthy, Praveen ^a  

^a the State University of New York   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING APPLICATIONS; DAMAGING EFFECTS; DATA SETS; FINITE MODEL; HIDDEN STATE; HIERARCHICAL DIRICHLET PROCESS; INFINITE STATE SPACE; MODEL ARCHITECTURE; MODEL SELECTION METHODS; OVERFITTING; STATE-TO-STATE TRANSITION; TIME COURSE; TIME POINTS;

ARTIFICIAL INTELLIGENCE; GENE EXPRESSION;

HIDDEN MARKOV MODELS;

EID: 80053211910     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. D. Aldous. Exchangeability and related topics. In Ecole d'été de probabilités de Saint-Flour XIII-1983, pages 1-198. Springer, Berlin, 1985.
2. Z. Bar-Joseph, G. Gerber, D. Gifford, T. Jaakkola, and I. Simon. Continuous representations of time series gene expression data. Journal of Computational Biology, 10 (3-4):241-256, 2003.
3. M. J. Beal. Variational Algorithms for Approximate Bayesian Inference. PhD thesis, Gatsby Computational Neuroscience Unit, University College London, UK, 2003.
4. M. J. Beal, Z. Ghahramani, and C. E. Rasmussen. The infinite hidden Markov model. In Advances in Neural In- formation Processing Systems 14, Cambridge, MA, 2002. MIT Press.
5. M. J. Beal, F. L. Falciani, Z. Ghahramani, C. Rangel, and D. Wild. A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics, 21:349-356, February 2005. (Pubitemid 40246606)
6. R. J. Cho, M. J. Campbell, E. A. Winzeler, L. Steinmetz, A. Conway, L. Wodicka, T. G. Wolfsberg, A. E. Gabrielian, D. Landsman, D. J. Lockhart, and R. W. Davis. A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell, 2(1):65-73, 1998. (Pubitemid 128378967)
7. A. Dubey, S. Hwang, C. Rangel, C. E. Rasmussen, Z. Ghahramani, and D. L. Wild. Clustering protein sequence and structure space with infinite Gaussian mixture models. In R. B. Altman, A. K. Dunker, L. Hunter, and T.E. Klein, editors, Pacific Symposium in Biocomputing 2004, pages 399-410. World Scientific Publishing, Singapore, 2004.
8. M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein. Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences of the USA, 95(25):14863-14868, December 1998. (Pubitemid 29003722)
9. T. S. Ferguson. A Bayesian analysis of some nonparametric problems. Annals of Statistics, 1(2):209-230, 1973.
10. N. A. Heard, C. C. Holmes, and D. A. Stephens. A quantitative study of gene regulation involved in the immune response of Anopheline mosquitoes: An application of Bayesian hierarchical clustering of curves. Journal of the American Statistical Association, 101(473):18-29, 2006. (Pubitemid 43500022)
11. V. R. Iyer, M. B. Eisen, D. T. Ross, G. Schuler, T. Moore, J. C. F. Lee, J. M. Trent, L. M. Staudt, J. Hudson Jr., M. S. Boguski, D. Lashkari, D. Shalon, D. Botstein, and P. O. Brown. The Transcriptional Program in the Response of Human Fibroblasts to Serum. Science, 283 (5398):83-87, 1999. (Pubitemid 29044852)
12. M. Medvedovic and S. Sivaganesan. Bayesian infinite mixture model based clustering of gene expression profiles. Bioinformatics, 18(9):1194-1206, 2002.
13. M. Medvedovic, K. Y. Yeung, and R.E. Bumgarner. Bayesian mixture model based clustering of replicated microarray data. Bioinformatics, 20(8):1222-1232, 2004. (Pubitemid 38807578)
14. R. M. Neal. Markov chain sampling methods for Dirichlet process mixture models. Technical Report 9815, Department of Statistics, University of Toronto, 1998.
15. L. R. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77, 1989.
16. M. F. Ramoni, P. Sebastiani, and I.S. Kohane. Cluster analysis of gene expression dynamics. Proc. National Academy of Sciences of the USA, 99(14):9121-6, 2002. (Pubitemid 34764577)
17. C. E. Rasmussen. The infinite Gaussian mixture model. In Advances in Neural Information Processing Systems 12, Cambridge, MA, 2000. MIT Press.
18. A. Schliep, I. G. Costa, C. Steinhoff, and A. Schonhuth. Analyzing gene expression time-courses. IEEE Trans. Comp. Biology and Bioinformatics, 2(3), 2005.
19. G. Schwarz. Estimating the dimension of a model. The Annals of Statistics, 6:461-464, 1978.
20. J. Sethuraman. A constructive definition of Dirichlet priors. Statistica Sinica, 4:639-650, 1994.
21. A. Stolcke and S. Omohundro. Hidden Markov model induction by Bayesian model merging. In S. J. Hanson, J. D. Cowan, and C. L. Giles, editors, Advances in Neural Information Processing Systems 5, pages 11-18, San Francisco, CA, 1993. Morgan Kaufmann.
22. Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei. Sharing clusters among related groups: Hierarchical Dirichlet Processes. In L. K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, Cambridge, MA, 2005. MIT Press.
23. Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei. Hierarchical Dirichlet processes. Journal of the American Statistical Society, 2006. To appear.
24. J. Wakefield, C. Zhou, and S. Self. Modelling gene expression over time: curve clustering with informative prior distributions. In J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, and M. West, editors, Bayesian Statistics 7, Proc. 7th Valencia International Meeting, pages 721-732. OUP, 2003.
25. D. L. Wild, C. E. Rasmussen, Z. Ghahramani, J. Cregg, B. J. de la Cruz, C-C. Kan, and K. A. Scanlon. A Bayesian approach to modelling uncertainty in gene expression clusters, 2002. Extended conference abstract in 3rd Int. Conf. on Systems Biology, Stockholm, Sweden.