DNA microarray data clustering by hidden markov models and bayesian information criterion

Authors:
Phasit Charoenkwan;Aompilai Manorat;Jeerayut Chaijaruwanich;Sukon Prasitwattanaseree;Sakarindr Bhumiratana
Affiliations:
Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand;Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand;Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand;Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand;National Center for Genetic Engineering and Biotechnology (BIOTEC), Pathumthani, Thailand
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

Citing 2
Cited 0

A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Robust inference of groups in gene expression time-courses using mixtures of HMMs

Bioinformatics

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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.