Inferring multiple graphical structures
Inferring multiple graphical structures
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DOI：
10.1007/s11222-010-9191-2
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发表时间：
2011-10-01
影响因子：
2.2
通讯作者：
Ambroise, Christophe
中科院分区：
文献类型：
--
作者：
Chiquet, Julien;Grandvalet, Yves;Ambroise, Christophe
高斯图形模型为表示变量之间的依赖关系提供了一个方便的框架。最近，这个工具已经收到了很高的兴趣，发现生物网络。文献集中在从一组测量推断单个网络的情况下。但是，由于湿实验室的数据通常是稀缺的，几个测定，其中实验条件影响相互作用，通常是合并来推断一个单一的网络。在本文中，我们提出了两种方法来估计多个相关图，通过渲染的贴近度假设到一个经验先验或组惩罚。我们提供了定量的结果证明所提出的方法的好处。本文中提出的方法嵌入在R软件包simone 1.0-0及更高版本中。
Gaussian Graphical Models provide a convenient framework for representing dependencies between variables. Recently, this tool has received a high interest for the discovery of biological networks. The literature focuses on the case where a single network is inferred from a set of measurements. But, as wetlab data is typically scarce, several assays, where the experimental conditions affect interactions, are usually merged to infer a single network. In this paper, we propose two approaches for estimating multiple related graphs, by rendering the closeness assumption into an empirical prior or group penalties. We provide quantitative results demonstrating the benefits of the proposed approaches. The methods presented in this paper are embeded in the R package simone from version 1.0-0 and later.