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

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.