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A Framework for Elucidating Regulatory Networks Based on Prior Information and Expression Data
Author(s) -
GEVAERT OLIVIER,
VAN VOOREN STEVEN,
DE MOOR BART
Publication year - 2007
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1196/annals.1407.002
Subject(s) - computer science , prior probability , bayesian network , data integration , bayesian probability , task (project management) , gene regulatory network , data mining , prior information , data science , computational biology , machine learning , artificial intelligence , biology , gene , gene expression , biochemistry , management , economics
: 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.