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Analyzing time-course gene expression data using profile-state hidden Markov model

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4 Author(s)
Qiang Huang ; Nat. Center for Math. & Interdiscipl. Sci., CAS, Beijing, China ; Ling-Yun Wu ; Ji-Bin Qu ; Xiang-Sun Zhang

More and more gene expression data are available due to the rapid development of high-throughput experimental techniques such as microarray and next generation sequencing (NGS). The gene expression data analysis is still one of the fundamental tasks in bioinformatics. In this paper, we propose a new profile-state hidden Markov model (HMM) for analyzing time-course gene expression data, which gives a new point of view to explain the variation of gene expression and regulation in different time. This model addresses the bicluster problem in time-course data efficiently and can identify the irregular shape and overlapping biclusters. The comprehensive computational experiments on simulated and real data show that the new method is effective and useful.

Published in:

Systems Biology (ISB), 2011 IEEE International Conference on

Date of Conference:

2-4 Sept. 2011