Abstract
In this study, the microarray data under diauxic shift condition of Saccharomyces Cerevisiae was considered. The objective of this study is to propose another strategy of cluster analysis for gene expression levels under time-series conditions. The continuous hidden markov model was newly proposed to select genes which significantly expressed. Then, new approach of hidden markov model clustering was proposed to include Bayesian information criterion technique which helped to determine the size of model. The result of this technique provided a good quality of clustering from gene expression patterns.
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© 2006 Springer-Verlag Berlin Heidelberg
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Charoenkwan, P., Manorat, A., Chaijaruwanich, J., Prasitwattanaseree, S., Bhumiratana, S. (2006). DNA Microarray Data Clustering by Hidden Markov Models and Bayesian Information Criterion. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_90
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DOI: https://doi.org/10.1007/11811305_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
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