Network inference using informative priors.

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Network inference using informative priors. Abstract Text:

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.

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Network inference using informative priors. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: Proceedings of the National Academy of Sciences of

VOLUME: 105

Page Numbers: 14313-8

Journal Abbreviation: Proc. Natl. Acad. Sci. U.S.A.

ISSN: 1091-6490

DAY: 17

MONTH: 09

YEAR: 2008

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LANGUAGE: eng

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Grant and Affiliation Information for Network inference using informative priors.

AFFILIATION: Department of Statistics and Centre for Complexity Science, University of Warwick, Coventry CV4 7AL, United Kingdom.

Country: United States

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MEDLINETA: Proc Natl Acad Sci U S A

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