基于潜在调控因子筛选的高阶动态贝叶斯建模方法

High-Order Dynamic Bayesian Network Modelling Method Based on Potential Regulatory Factors Screening

为了解决目前用于构建基因调控网络的方法中所存在的网络构建准确率低、网络构建时间过长等问题,以及减小网络构建的复杂度,提高网络构建效率,提出了一种基于潜在调控因子筛选的高阶动态贝叶斯网络建模方法(high-order dynamic Bayesian network modeling method based on potential regulatory factor screening,PRS-HO-DBN).该方法将关联模型与高阶动态贝叶斯网络模型相结合,首先利用潜在调控因子筛选的方法在不同的时间延迟下删除与目标基因关联程度较低的基因,保留与目标基因关联程度较高的基因并作为目标基因的潜在调控因子集,以减小搜索空间;然后利用高阶动态贝叶斯模型进行结构学习,以提高网络构建的精确率.与其他的网络构建模型方法相比,该方法可以极大地缩短网络构建的时间,提升效率和精确度.

In order to solve the problems of low network construction accuracy and long network construction time in the current methods used to construct gene regulatory networks,so as to reduce the complexity of network construction and improve the efficiency of network construction,a method called high-order dynamic Bayesian network modelling method based on potential regulatory factors screening(PRS-HO-DBN)was proposed.The method combines the correlation model with the high-order dynamic Bayesian network model.Firstly,the potential regulatory factor screening method is used to delete the genes with low association with the target gene under different time delays,and retain the genes with high association with the target gene as the potential regulatory factor set of the target gene to reduce the search space.Then the high-order dynamic Bayesian model is used for structure learning to improve the accuracy of network construction.Compared with other methods,the method can greatly reduce network construction time and improve efficiency and accuracy.