Dose and time relationship through probabilistic graphical models of gene expression time course toxicogenomics data

Abstract

Background A probabilistic graphical model is a representation of searched properties of random variables represented by nodes. The edges in the graph represent conditional independence properties used to obtain a number of valid factorizations of the joint probability distribution. There are different types of graphical models, Bayesian networks for instance, are directed graphical models but Markov random fields are undirected models. Dynamic Bayesian networks (DBN) are Bayesian networks that model time series where edges have a direction and point in the direction of time. The directed edge in a BN can encode either a random or a deterministic relationship between the two variables. The structure of a graphical model represents the way in which a set of random variables probabilistically represents natural and systematic processes such as noisy measurements of the observed gene expression levels at each time point, and how these variables interact in a form of a gene regulatory network.