Seeded Bayesian Networks: constructing genetic networks from microarray data.

ABSTRACT:

Background

DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes - often represented as networks - in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to in

SUBMITTER: Djebbari A 

PROVIDER: S-EPMC2474592 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature