title: A framework for elucidating regulatory networks based on prior information and expression data.
abstract: Elucidating regulatory networks is an intensively studied topic in bioinformatics. Integration of different sources of information could facilitate this task. We propose to incorporate these information sources in the structure prior of a Bayesian network. We are currently investigating two complementary sources of information: PubMed abstracts combined with publicly available taxonomies or ontologies, and known protein-DNA interactions. These priors, either separately or combined, have the potential of reducing the complexity of reverse-engineering regulatory networks while creating more robust and reliable models. Moreover this approach can easily be extended with other data sources. In such a way Bayesian networks provide a powerful framework for data integration and regulatory network modeling.
authors.affiliation: Katholieke Universiteit Leuven, Department of Electrical Engineering (ESAT), Kasteelpark Arenberg 10, 3001 Leuven, Belgium. olivier.gevaert@esat.kuleuven.be
authors.forename: Olivier
authors.initials: O
authors.lastname: Gevaert
authors.forename: Steven
authors.initials: S
authors.lastname: Van Vooren
authors.forename: Bart
authors.initials: B
authors.lastname: De Moor
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001698:Biomedical Engineering / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030562:Databases, Protein; D015870:Gene Expression / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D016247:Information Storage and Retrieval / Q000379:methods; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome