关键词: 基因表达时序数据, 统计特性, 隐马尔可夫模型, 层次聚类

Abstract: The use of DNA microarray technology produces a large number of gene expression time series data.Clustering of these data is a significant approach to extract molecular bioinformation hidden in them.In this paper，a Hidden Markov Model-based Hierarchical Clustering（HMM-HC） method is presented to analyze gene expression time series data.Gene expression time series data are preprocessed according to their statistics，including normalizing them and discretizing them.HMMs are used to model the preprocessed data so as to take advantage of the time dependency between different time points in the gene profile.The built HMM models are clustered with hierarchical strategy to achieve clustering of the data.The experimental results show that this method can not only produce high-quality clusters，but also find out the appropriate number of clusters.

Key words: gene expression time series data, statistics, Hidden Markov Model（HMM）, hierarchical clustering