Inferring Boolean network structure via correlation
Inferring Boolean network structure via correlation
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DOI：
10.1093/bioinformatics/btr166
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发表时间：
2011-06-01
期刊：
影响因子：
5.8
通讯作者：
Kestler, Hans A.
中科院分区：
文献类型：
--
作者：
Maucher, Markus;Kracher, Barbara;Kestler, Hans A.
结果：为了检测网络中的调控依赖性，我们研究了不同基因的表达如何与连续的网络状态相关。为此，我们使用Pearson相关性作为基本的相关性度量。给定一个只包含单调布尔函数的布尔网络，我们证明了连续状态的相关性可以识别网络中的依赖关系。该方法不仅发现随机创建的人工网络的依赖性非常高的百分比，但也重建了大部分的公开的大肠杆菌调控网络从模拟数据和酵母细胞周期网络从真实的微阵列数据。
Results: To detect regulatory dependencies in a network, we examined how the expression of different genes correlates to successive network states. For this purpose, we used Pearson correlation as an elementary correlation measure. Given a Boolean network containing only monotone Boolean functions, we prove that the correlation of successive states can identify the dependencies in the network. This method not only finds dependencies in randomly created artificial networks to very high percentage, but also reconstructed large fractions of both a published Escherichia coli regulatory network from simulated data and a yeast cell cycle network from real microarray data.