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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Network Inference Using Informative Priors
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Network Inference Using Informative Priors

机译：使用信息先验的网络推理

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摘要

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.
机译：近年来，对具有多个相互作用组件特征的系统的研究引起了极大兴趣。一类称为图形模型的统计模型，其中的图形用于表示变量之间的概率关系，为有关此类系统的形式推断提供了框架。在许多情况下，推理的对象是网络结构本身。众所周知，“网络推断”这一问题是具有挑战性的。但是，在科学环境中，通常存在有关网络连接性的现有信息。一个自然的想法是在推理过程中考虑这些信息。本文解决了将先验信息合并到网络推理中的问题。我们专注于称为贝叶斯网络的有向模型，并使用马尔可夫链蒙特卡罗方法从网络结构上的后验分布中提取样本。我们在能够捕获有关网络特征的信息的图形上引入了先验分布，这些信息包括边，边的类，度分布和稀疏度。我们在系统生物学的背景下说明了我们的方法，并将我们的方法应用于癌症信号的网络推断。

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