Journal article

Network inference using informative priors

Sach Mukherjee, Terence P Speed

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA | NATL ACAD SCIENCES | Published : 2008

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 incorporatin..

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University of Melbourne Researchers

Grants

Funding Acknowledgements

We thank Rich Neve, Paul Spellman, Laura Heiser, and other members of Joe Gray's laboratory at Lawrence Berkeley National Laboratory for a productive, ongoing collaboration and for providing the proleomic dataset used in this article. S.M. was supported by a Fulbright-AstraZeneca postdoctoral fellowship.