Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection
Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection
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DOI：
10.1093/bioinformatics/btg1082
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发表时间：
2003-09-01
期刊：
影响因子：
5.8
通讯作者：
Miyano, Satoru
中科院分区：
文献类型：
--
作者：
Tamada, Yoshinori;Kim, SunYong;Miyano, Satoru
我们提出了一个统计方法估计基因网络和检测启动子元件同时进行。当仅从基因表达数据估计网络时，一个常见的问题是与网络模型中的变量数量相比，微阵列的数量有限，这使得准确估计成为一项困难的任务。我们的方法通过将微阵列基因表达数据和DNA序列信息整合到贝叶斯网络模型中来克服这个问题。我们的方法的基本思想是，如果一个亲本基因是一个转录因子，它的孩子可能会分享一个共同的DNA序列的启动子区域的基序。我们的方法检测的基础上估计的网络结构的一致性模体，然后重新估计网络使用的模体检测的结果。我们继续这个迭代，直到网络变得稳定。为了证明我们的方法的有效性，我们进行了蒙特卡罗模拟，并将我们的方法应用于酿酒酵母数据作为真实的应用。联系方式：tamada@ims.u-tokyo.ac.jp
We present a statistical method for estimating gene networks and detecting promoter elements simultaneously. When estimating a network from gene expression data alone, a common problem is that the number of microarrays is limited compared to the number of variables in the network model, making accurate estimation a difficult task. Our method overcomes this problem by integrating the microarray gene expression data and the DNA sequence information into a Bayesian network model. The basic idea of our method is that, if a parent gene is a transcription factor, its children may share a consensus motif in their promoter regions of the DNA sequences. Our method detects consensus motifs based on the structure of the estimated network, then re-estimates the network using the result of the motif detection. We continue this iteration until the network becomes stable. To show the effectiveness of our method, we conducted Monte Carlo simulations and applied our method to Saccharomyces cerevisiae data as a real application.Contact: tamada@ims.u-tokyo.ac.jp