HMM Training using Correlation Coefficients of Time-Series Gene Expression Data

摘要：

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.

关键词： time series data gene expression data HMM gene regular network correlation coefficient

作者: Jiangeng Li Qinglei Guo Yiheng He

作者单位: Institute of Artificial Intelligence and Robots, College of Electronic Information and Control Engin Institute of Artificial Intelligence and Robots, College of Electronic Information and Control Engin

会议类型: 国际会议

会议名称: The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

会议地点: 太原

会议语种:英文

页码: 3736-3740

在线出版日期: 2012-05-23（万方平台首次上网日期，不代表论文的发表时间）