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Liverani, Silvia; Anderson, Paul E.; Edwards, Kieron D.; Millar, A. J.; Smith, J. Q. (2009)
Publisher: Int Soc Bayesian Analysis
Languages: English
Types: Article
Subjects: QA

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_PATTERNRECOGNITION
Because of the huge number of partitions of even a moderately sized dataset, even when Bayes factors have a closed form, in model-based clustering a comprehensive search for the highest scoring (MAP) partition is usually impossible. However, when each cluster in a partition has a signature and it is known that some signatures are of scientific interest whilst others are not, it is possible, within a Bayesian framework, to develop search algorithms which are guided by these cluster signatures. Such algorithms can be expected to find better partitions more quickly. In this paper we develop a framework within which these ideas can be formalized. We then briefly illustrate the efficacy of the proposed guided search on a microarray time coursed at a set where the clustering objective is to identify clusters of genes with different types of circadian expression profiles.
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    • Anderson, P. E., Smith, J. Q., Edwards, K. D., and Millar, A. J. (2006). \Guided Conjugate Bayesian Clustering for Uncovering Rhytmically expressed Genes." CRISM Working Paper, (07). 556
    • Ban¯eld, J. D. and Raftery, A. E. (1993). \Model-Based Gaussian and Non-Gaussian Clustering." Biometrics, 49(3): 803{821. 540
    • Ben-Dor, A., Shamir, R., and Yakhini, Z. (1999). \Clustering Gene Expression Patterns." Journal of Computational Biology , 6(3{4): 281{297. 540
    • Bernardo, J. M. and Smith, A. F. M. (1994). Bayesian Theory. Chichester: Wiley. 540
    • Booth, J. G., Casella, G., and Hobert, J. P. (2008). \Clustering using objective functions and stochastic search." Journal of the Royal Statistical Society, Series B, 70(1): 119{ 139. 556
    • Chipman, H. A., George, E. I., and McCulloch, R. E. (2002). \Bayesian treed models." Machine Learning, 48(1{3): 299{320. 556
    • Crowley, E. M. (1997). \Product Partition Models for Normal Means." Journal of the American Statistical Association, 92(437): 192{198. 556
    • Denison, D. G. T., Holmes, C. C., Mallick, B. K., and Smith, A. F. M. (2002). Bayesian Methods for Nonlinear Classi¯cation and Regression. Wiley Series in Probability and Statistics. John Wiley and Sons. 540, 541
    • Edwards, K. D., Anderson, P. E., Hall, A., Salathia, N. S., Locke, J. C. W., Lynn, J. R., Straume, M., Smith, J. Q., and Millar, A. J. (2006). \FLOWERING LOCUS C Mediates Natural Variation in the High-Temperature Response of the Arabidopsis Circadian Clock." The Plant Cell, 18: 639{650. 541, 551, 552
    • Eisen, M., Spellman, P., Brown, P., and Botstein, D. (1998). \Cluster analysis and display of genome-wide expression patterns." Proceedings of the National Academy of Sciences, 95(25): 14863{14868. 553
    • Fraley, C. and Raftery, A. E. (1998). \How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis." The Computer Journal, 41: 578{588. 539, 540
    • French, S. and Rios Insua, D. (2000). Statistical Decision Theory . London: Arnold. 543
    • Heard, N. A., Holmes, C. C., and Stephens, D. A. (2006). \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. 539, 540, 541, 553
    • Keeney, R. and Rai®a, H. (1976). Decision with multiple objectives: Preferences and value tradeo®s. New York: John Wiley & Sons. 543
    • Keeney, R. and von Winterfeldt, D. (2007). \Practical Value Models." In Edwards, W., Miles, R. F., and von Winterfeldt, D. (eds.), Advances in Decision Analysis: From Foundations to Applications, 232{252. Cambridge University Press. 543
    • Luan, Y. and Li, H. (2003). \Clustering of time-course gene expression data using a mixed-e®ects model with B-splines." Bioinformatics, 19(4): 474{482. 540
    • McCullagh, P. and Yang, J. (2006). \Stochastic classi¯cation models." In Proceedings International Congress of Mathematicians, volume III, 669{686. 556
    • Michael, T., Mockler, T., Breton, G., McEntee, C., Byer, A., Trout, J., Hazen, S., Shen, R., Priest, H., Sullivan, C., Givan, S., Yanovsky, M., Hong, F., Kay, S., and Chory, J. (2008). \Network Discovery Pipeline Elucidates Conserved Time-of-Day{Speci¯c cis-Regulatory Modules." PLoS Genetics, 4(2): e14. 552
    • O'Hagan, A. and Forster, J. (2004). Bayesian Inference: Kendall's Advanced Theory of Statistics. Arnold, second edition. 541
    • Ramoni, M. F., Sebastiani, P., and Kohane, I. S. (2002). \Cluster Analysis of Gene Expression Dynamics." Proceedings of the National Academy of Sciences of the United States of America, 99(14): 9121{9126. 540
    • Ray, S. and Mallick, B. (2006). \Functional clustering by Bayesian wavelet methods." J. Royal Statist. Soc.: Series B, 68(2): 305{332. 541
    • Smith, J. Q., Anderson, P. E., and Liverani, S. (2008). \Separation Measures and the Geometry of Bayes Factor Selection for Classi¯cation." Journal of the Royal Statistical Society, Series B, 70(5): 957{980. 540, 541, 542, 552, 554
    • Zhou, C., Wake¯eld, J. C., and Breeden, L. L. (2006). \Bayesian Analysis of Cell-Cycle Gene Expression Data." In Do, K.-A., MuÄller, P., and Vannucci, M. (eds.), Bayesian Inference for Gene Expression and Proteomics, 177{200. Cambridge University Press. 540, 553
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