Abstract
Microarray data from time-series experiments, where gene expression profiles are measured over the course of the experiment, require specialised algorithms. In this paper we introduce new architectures of Bayesian classifiers that highlight how both relative and absolute temporal relationships can be captured in order to understand how biological mechanisms differ. We show that these classifiers improve the classification of microarray data and at the same time ensure that the models can easily be analysed by biologists by incorporating time transparently. In this paper we focus on data that has been generated to explore different types of muscular dystrophy.
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Cohn, R.D., Campbell, K.P.: Molecular basis of muscular dystrophies. Muscle Nerve 23, 1456–1471 (2000)
Dalkilic, I., Kunkel, L.M.: Muscular dystrophies: genes to pathogenesis. Curr.Opin.Genet.Dev. 13, 213–238 (2003)
Friedman, N.: Learning the structure of dynamic probabilistic networks. In: Proceedings of the 14th Annual Conference on Uncertainty in AI, pp. 139–147 (1998)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–163 (1997)
Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using Bayesian networks to analyze expression data. Journal of Computational Biology 7, 601–620 (2000)
Lam, W., Bacchus, F.: Learning Bayesian belief networks: an approach based on the MDL principle. Computational Intelligence 10(4), 269–293 (1994)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)
Ramoni, M., Sebstiani, P., Cohen, P.: Bayesian clustering by dynamics. Machine Learning 47(1), 91–121 (2002)
Tucker, A., Vinciotti, V., Garway-Heath, D., Liu, X.: A spatio-temporal bayesian network classifier for understanding visual field deterioration. In: Artificial Intelligence in Medicine (2005) (to Appear)
Turk, R., Sterrenburg, E., de Meijer, E.J., van Ommen, G.J.B., den Dunnen, J.T., Hoen, ’t Hoen, P.A.: Muscle regeneration in dystrophin-deficient mdx mice studied by gene expression profiling (submitted)
Turk, R., Sterrenburg, E., Wees, C.G.C.v.d., Meijer, E.J.d., Groh, S., Campbell, K., Noguchi, S., Ommen, G.J.B.v., Dunnen, J.T.d., Hoen, P.A.C.’.: Common pathological mechanisms in mouse models for muscular dystrophies (submitted)
Vinciotti, V., Tucker, A., Liu, X., Panteris, E., Kellam, P.: Identifying genes with high confidence from small samples. In: Workshop on Data Mining in Functional Genomics and Proteomics, European Conference in Artificial Intelligence (2004)
Wit, E.C., McClure, J.D.: Statistics for Microarrays: Design, Analysis and Inference. John Wiley & Sons, Chichester (2004)
Zou, M., Conzen, S.D.: A new dynamic bayesian network approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 21, 71–79 (2005)
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© 2005 Springer-Verlag Berlin Heidelberg
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Tucker, A., Vinciotti, V., ’t Hoen, P.A.C., Liu, X. (2005). Bayesian Network Classifiers for Time-Series Microarray Data. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds) Advances in Intelligent Data Analysis VI. IDA 2005. Lecture Notes in Computer Science, vol 3646. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552253_43
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DOI: https://doi.org/10.1007/11552253_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28795-7
Online ISBN: 978-3-540-31926-9
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