Abstract
Microarrays allow monitoring of thousands of genes over time periods. Recently, gene clustering approaches specially adapted to deal with the time dependences of these data have been proposed. According to these methods, we investigate here how to use prior knowledge about the approximate profile of some classes to improve the classification result. We propose a Bayesian approach to this problem. A mixture model is used to describe and classify the data. The parameters of this model are constrained by a prior distribution defined with a new type of model that can express both our prior knowledge about the profile of classes of interest and the temporal nature of the data. Then, an EM algorithm estimates the parameters of the mixture model by maximizing its posterior probability.
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References
Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D., Futcher, B.: Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 9, 3273–3297 (1998)
Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95, 14863–14868 (1998)
Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Info. Theory IT-2, 129–137 (1982)
Herwig, R., Poustka, A.J., Muller, C., Bull, C., Lehrach, H., O’Brien, J.: Large-scale clustering of cDNA-fingerprinting data. Genome Res 9, 1093–1105 (1999)
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1997)
Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E.S., Golub, T.R.: Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 96, 2907–2912 (1999)
Ramoni, M.F., Sebastiani, P., Kohane, I.S.: Cluster analysis of gene expression dynamics. Proc Natl Acad Sci USA 99, 9121–9126 (2002)
Bar-Joseph, Z., Gerber, G.K., Gifford, D.K., Jaakkola, T.S., Simon, I.: Continuous representations of time-series gene expression data. J Comput Biol 10, 341–356 (2003)
Schliep, A., Schonhuth, A., Steinhoff, C.: Using hidden markov models to analyze gene expression time course data. Bioinformatics 19 Suppl 1, 255–263 (2003)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77, 257–285 (1989)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc. B 39, 1–38 (1977)
McLachlan, G., Krishnan, T.: Finite mixture models. John Wiley, Chichester (2000)
Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiley, Chichester (2001)
Casacuberta, F.: Some relations among stochastic finite state networks used in automatic speech recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 691–695 (1990)
Iyer, V.R., Eisen, M.B., Ross, D.T., Schuler, G., Moore, T., Lee, J.C., Trent, J.M., Staudt, L.M., Hudson, J.J., Boguski, M.S., Lashkari, D., Shalon, D., Botstein, D., Brown, P.O.: The transcriptional program in the response of human fibroblasts to serum. Science 283, 83–87 (1999)
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Bréhélin, L. (2005). Clustering Gene Expression Series with Prior Knowledge. In: Casadio, R., Myers, G. (eds) Algorithms in Bioinformatics. WABI 2005. Lecture Notes in Computer Science(), vol 3692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11557067_3
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DOI: https://doi.org/10.1007/11557067_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29008-7
Online ISBN: 978-3-540-31812-5
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