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Activity Number:
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428
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Type:
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Invited
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Date/Time:
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Wednesday, August 6, 2008 : 2:00 PM to 3:50 PM
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Sponsor:
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WNAR
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| Abstract - #300263 |
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Title:
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Reconstructing Gene Regulatory Networks from Gene Expression Data and Biological Prior Knowledge
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Author(s):
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Dirk Husmeier*+
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Companies:
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Biomathematics & Statistics Scotland
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Address:
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JCMB, Room 3606, Edinburgh, International, EH9 3JZ, United Kingdom
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Keywords:
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Gene regulatory networks ; Bayesian networks ; KEGG ; microarrays ; MCMC ; flow cytometry
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Abstract:
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The objective of the talk is the discussion of a method for improving the reconstruction of gene regulatory networks from microarray data by the systematic integration of biological prior knowledge. Our approach is based on the Bayesian paradigm whereby a prior distribution over network structures is derived from partial knowledge of signaling pathways, as obtained from databases such as KEGG. The hyperparameters of this distribution represent the weights associated with the prior knowledge relative to the data. We have derived and tested an MCMC scheme for sampling networks and hyperparameters simultaneously from the posterior distribution, thereby automatically learning how to trade off information from the prior and the data. We have assessed the viability of the proposed method on cytometry data obtained from the RAF signaling pathway.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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