Network inference using informative priors
Network inference using informative priors
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DOI：
10.1073/pnas.0802272105
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发表时间：
2008-09-23
影响因子：
11.1
通讯作者：
Speed, Terence P.
中科院分区：
文献类型：
--
作者：
Mukherjee, Sach;Speed, Terence P.
近年来，多组分相互作用系统的研究引起了人们的极大兴趣。一类被称为图形模型的统计模型，其中图被用来表示变量之间的概率关系，为关于这类系统的形式推理提供了框架。在许多情况下，推理的对象是网络结构本身。众所周知，这个“网络推理”问题是一个具有挑战性的问题。然而，在科学环境中，通常存在关于网络连接的现有信息。因此，一个自然的想法是在推理过程中考虑到这些信息。本文讨论了将先验信息融入网络推理中的问题。我们将重点放在称为贝叶斯网络的有向模型上，并使用马尔可夫链蒙特卡罗从网络结构上的后验分布中提取样本。我们在图上引入先验分布，这些先验分布能够捕捉有关网络特征的信息，包括边、边的类别、度分布和稀疏性。我们在系统生物学的背景下说明了我们的方法，将我们的方法应用于癌症信号的网络推理。
Recent years have seen much interest in the study of systems characterized by multiple interacting components. A class of statistical models called graphical models, in which graphs are used to represent probabilistic relationships between variables, provides a framework for formal inference regarding such systems. In many settings, the object of inference is the network structure itself. This problem of "network inference" is well known to be a challenging one. However, in scientific settings there is very often existing information regarding network connectivity. A natural idea then is to take account of such information during inference. This article addresses the question of incorporating prior information into network inference. We focus on directed models called Bayesian networks, and use Markov chain Monte Carlo to draw samples from posterior distributions over network structures. We introduce prior distributions on graphs capable of capturing information regarding network features including edges, classes of edges, degree distributions, and sparsity. We illustrate our approach in the context of systems biology, applying our methods to network inference in cancer signaling.