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Activity Number:
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81
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Type:
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Invited
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Date/Time:
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Monday, July 30, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #307759 |
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Title:
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Bayesian Error Analysis Model for Reconstructing Transcriptional Regulatory Networks
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Author(s):
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Ning Sun*+ and Raymond J. Carroll and Hongyu Zhao
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Companies:
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Yale University and Texas A&M University and Yale University
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Address:
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300 George Street #531, New Haven, CT, 06511,
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Keywords:
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gene expression ; transcriptional regulation ; Markov chain Monte Carlo ; Bayesian ; misclassification
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Abstract:
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Transcription regulation is a fundamental biological process. In this article, we propose a Bayesian error analysis model to integrate protein-DNA binding data and gene expression data to reconstruct transcriptional regulatory networks. Transcription is modeled as a set of biochemical reactions to derive a linear system model with clear biological interpretation, and measurement errors in both protein-DNA binding data and gene expression data are explicitly considered in a Bayesian hierarchical model framework. Model parameters are inferred through Markov chain Monte Carlo. The usefulness of this approach is demonstrated through its application to infer transcriptional regulatory networks in the yeast cell cycle.
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