Mukherjee, Sach and Speed, T. P.. (2008) Network inference using informative priors. Proceedings of the National Academy of Sciences of the United States of America, Vol.105 (No.38). pp. 14313-14318. ISSN 0027-8424

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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.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QH Natural history > QH301 Biology
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Bayesian statistical decision theory, Cellular signal transduction, Systems biology, Biological models, Computational biology, Bioinformatics
Journal or Publication Title:	Proceedings of the National Academy of Sciences of the United States of America
Publisher:	National Academy of Sciences
ISSN:	0027-8424
Date:	23 September 2008
Volume:	Vol.105
Number:	No.38
Number of Pages:	6
Page Range:	pp. 14313-14318
Identification Number:	10.1073/pnas.0802272105
Status:	Not Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	United States-United Kingdom Educational Commission, AstraZeneca (Firm)
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URI:	http://wrap.warwick.ac.uk/id/eprint/29290

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