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

• Main Authors: Phasit Charoenkwan, Aompilai Manorat, Jeerayut Chaijaruwanich, Sukon Prasitwattanaseree, Sakarindr Bhumiratana
• Format: Book Series
• Published: 2018
• Subjects: Computer Science; Mathematics
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=33749419223&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/61610
• Institution: Chiang Mai University

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. © Springer-Verlag Berlin Heidelberg 2006.