A Framework for Elucidating Regulatory Networks Based on Prior Information and Expression Data

• Published in: Annals of the New York Academy of Sciences Vol. 1115; no. 1; pp. 240 - 248
• Main Authors: GEVAERT, OLIVIER, VAN VOOREN, STEVEN, DE MOOR, BART
• Format: Journal Article
• Language: English
• Published: Malden, USA Blackwell Publishing Inc 01-12-2007
• Subjects: Algorithms; Artificial Intelligence; Bayes Theorem; Bayesian networks; Biomedical Engineering - methods; Computational Biology - methods; Computer Simulation; Databases, Protein; Gene Expression - physiology; Gene Expression Profiling - methods; Gene Expression Regulation - physiology; Information Storage and Retrieval - methods; Models, Biological; prior information; Proteome - metabolism; regulatory networks; Signal Transduction - physiology
• ISSN: 0077-8923; 1749-6632
• DOI: 10.1196/annals.1407.002

:  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 s 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.