Abstract
We 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.
Publication types
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Research Support, N.I.H., Extramural
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Research Support, Non-U.S. Gov't
MeSH terms
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CCAAT-Enhancer-Binding Protein-beta / metabolism
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Chromatin Immunoprecipitation
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Cluster Analysis
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Computational Biology / methods*
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Databases, Nucleic Acid
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Enhancer Elements, Genetic / genetics
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Gene Deletion
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Gene Expression Regulation
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Gene Regulatory Networks
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Linear Models
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Models, Genetic*
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Protein Binding
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Regulon / genetics*
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Saccharomyces cerevisiae / genetics*
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Serum Response Factor / metabolism
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Software*
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Transcription Factors / metabolism
Substances
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CCAAT-Enhancer-Binding Protein-beta
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Serum Response Factor
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Transcription Factors