title: Bayesian integration of biological prior knowledge into the reconstruction of gene regulatory networks with Bayesian networks.
abstract: There have been various attempts to improve the reconstruction of gene regulatory networks from microarray data by the systematic integration of biological prior knowledge. Our approach is based on pioneering work by Imoto et al., where the prior knowledge is expressed in terms of energy functions, from which a prior distribution over network structures is obtained in the form of a Gibbs distribution. The hyperparameters of this distribution represent the weights associated with the prior knowledge relative to the data. To complement the work of Imoto et al., we have derived and tested an MCMC scheme for sampling networks and hyperparameters simultaneously from the posterior distribution. We have assessed the viability of this approach by reconstructing the RAF pathway from cytometry protein concentrations and prior knowledge from KEGG.
authors.affiliation: Biomathematics and Statistics Scotland, Edinburgh, United Kingdom. dirk@bioss.sari.ac.uk
authors.forename: Dirk
authors.initials: D
authors.lastname: Husmeier
authors.forename: Adriano V
authors.initials: AV
authors.lastname: Werhli
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D018511:Systems Integration
chemical_list: D020543:Proteome