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DNA Microarray Data Clustering by Hidden Markov Models and Bayesian Information Criterion

  • Phasit Charoenkwan
  • Aompilai Manorat
  • Jeerayut Chaijaruwanich
  • Sukon Prasitwattanaseree
  • Sakarindr Bhumiratana
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)

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.

Keywords

Hide Markov Model Bayesian Information Criterion Mixture Gaussian Model Hide Markov Model Model Observation Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Phasit Charoenkwan
    • 1
  • Aompilai Manorat
    • 1
  • Jeerayut Chaijaruwanich
    • 1
  • Sukon Prasitwattanaseree
    • 2
  • Sakarindr Bhumiratana
    • 3
  1. 1.Department of Computer Science, Faculty of ScienceChiang Mai UniversityChiang MaiThailand
  2. 2.Department of Statistics, Faculty of ScienceChiang Mai UniversityChiang MaiThailand
  3. 3.National Center for Genetic Engineering and Biotechnology (BIOTEC)Klong LuangThailand

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