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
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..View full abstract
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.