Inference of Gene Regulatory Network by Bayesian Network Using Metropolis-Hastings Algorithm-猫眼课题宝

DOI：

10.1007/978-3-540-73871-8_26

发表时间：

2007-08

期刊：

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Khwunta Kirimasthong;Aompilai Manorat;Jeerayut Chaijaruwanich;Sukon Prasitwattanaseree;C. Thammarongtham

贝叶斯网络被广泛用于从基因的转录表达数据推断基因调控网络。基因调控模型通常选择得分最高的贝叶斯网络。然而，如果没有来自生物学基础事实的提示，并且给定少量转录表达观察，则所得贝叶斯网络可能不对应于真实的贝叶斯网络。为了处理这两个约束，本文提出了一种随机的方法来适应现有的假设基因调控网络，来自生物学证据，与一些可用量的基因的转录表达水平。将假设的基因调控网络作为贝叶斯网络的初始模型，并利用Metropolis-Hastings算法对转录表达数据进行拟合。本研究以酿酒酵母（Saccharomycescerevisiae）的辅助调节因子HAP 2、HAP 3、HAP 4对CYC 1基因的转录调控为例。根据模拟结果，得到了10个与给定假设模型相似的可能基因调控网络。这表明Metropolis-Hastings算法可以作为基因调控网络的模拟模型。

Bayesian networks are widely used to infer genes regulatory network from their transcriptional expression data. Bayesian network of the best score is usually chosen as genes regulatory model. However, without the hint from biological ground truth, and given a small number of transcriptional expression observations, the resulting Bayesian networks might not correspond to the real one. To deal with these two constrains, this paper proposes a stochastic approach to fit an existing hypothetical gene regulatory network, derived from biological evidence, with few available amount of transcriptional expression levels of the genes. The hypothetical gene regulatory network is set as an initial model of Bayesian network and fitted with transcriptional expression data by using Metropolis-Hastings algorithm. In this work, the transcriptional regulation of gene CYC1 by co-regulators HAP2 HAP3 HAP4 of yeast (Saccharomyces Cerevisiae) is considered as example. Due to the simulation results, ten probable gene regulatory networks which are similar to the given hypothetical model are obtained. This shows that Metropolis-Hastings algorithm can be used as a simulation model for gene regulatory network.

Inference of Gene Regulatory Network by Bayesian Network Using Metropolis-Hastings Algorithm

文献摘要

被引文献

相似文献