HMM training using correlation coefficients of time-series gene expression data

In the processes of gene expressing, gene expression data at each time point is different. Each gene expression levels in a time point affects gene expression levels in next time point. It is a hot point to construct gene regular network using time series gene expression data. There are many methods being used for this work, such as Boolean networks, Differential equations, Bayesian networks and so on. In this paper, we build transfer relationship of gene as gene observation matrix by correlation coefficients and P_value of time-series gene expression data in adjacent time points. And then we get gene states transfer probability by training HMM using gene observation matrix, and build gene regular network corresponding it. By comparing with real network, our experiment provides good result, and the method has less computation complexity than other regular methods like dynamic Bayesian networks.