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pubmed-article:17204163pubmed:abstractTextWe present a Bayesian hierarchical model and Gibbs Sampling implementation that integrates gene expression, ChIP binding, and transcription factor motif data in a principled and robust fashion. COGRIM was applied to both unicellular and mammalian organisms under different scenarios of available data. In these applications, we demonstrate the ability to predict gene-transcription factor interactions with reduced numbers of false-positive findings and to make predictions beyond what is obtained when single types of data are considered.lld:pubmed
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pubmed-article:17204163pubmed:articleTitleClustering of genes into regulons using integrated modeling-COGRIM.lld:pubmed
pubmed-article:17204163pubmed:affiliationDepartment of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA. ggchen@pcbi.upenn.edulld:pubmed
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