A scale-free structure prior for graphical models with applications in functional genomics.

ABSTRACT: The problem of reconstructing large-scale, gene regulatory networks from gene expression data has garnered considerable attention in bioinformatics over the past decade with the graphical modeling paradigm having emerged as a popular framework for inference. Analysis in a full Bayesian setting is contingent upon the assignment of a so-called structure prior-a probability distribution on networks, encoding a priori biological knowledge either in the form of supplemental data or high-level topological features. A key topological consideration is that a wide range of cellular networks are approximately scale-free, meaning that the fraction, , of nodes in a network with degree is roughly described by a power-law with exponent between and . The standard practice, however, is to utilize a random

SUBMITTER: Sheridan P 

PROVIDER: S-EPMC2974640 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature