Local network-based measures to assess the inferability of different regulatory networks
Local network-based measures to assess the inferability of different regulatory networks
复制标题
DOI：
10.1049/iet-syb.2010.0028
复制
发表时间：
2010-07-01
影响因子：
2.3
通讯作者：
Altay, G.
中科院分区：
文献类型：
--
作者：
Emmert-Streib, F.;Altay, G.
本研究的目的是比较各种合成和真实生物调节网络的可推断性。为了评估差异，我们采用基于本地网络的措施。这意味着，我们不是应用全局测量，而是在各个边和子网络的层面上本地研究和评估推理算法。我们通过进行大规模模拟来展示基于本地网络的措施针对不同监管网络的行为。作为推理算法，我们示例性地使用 ARACNE。我们的探索性分析结果不仅使我们能够对推理算法相对于不同监管网络的特征的优缺点有新的见解，而且还可以获得可用于设计新颖的针对特定问题的统计估计器的信息。
The purpose of this study is to compare the inferability of various synthetic as well as real biological regulatory networks. In order to assess differences we apply local network-based measures. That means, instead of applying global measures, we investigate and assess an inference algorithm locally, on the level of individual edges and subnetworks. We demonstrate the behaviour of our local network-based measures with respect to different regulatory networks by conducting large-scale simulations. As inference algorithm we use exemplarily ARACNE. The results from our exploratory analysis allow us not only to gain new insights into the strength and weakness of an inference algorithm with respect to characteristics of different regulatory networks, but also to obtain information that could be used to design novel problem-specific statistical estimators.