Network inference using informative priors
Author(s)
Mukherjee, S; Speed, TP;
Details
Publication Year 2008-09-23,Volume 105,Issue #38,Page 14313-14318
Journal Title
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Publication Type
Journal Article
Abstract
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.
Publisher
NATL ACAD SCIENCES
Keywords
BAYESIAN NETWORKS; GRAPHICAL MODELS; GROWTH; KNOWLEDGE
Terms of Use/Rights Notice
Refer to copyright notice on published article.


Creation Date: 2008-09-23 12:00:00
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