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Abstract Details
Activity Number:
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253
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #306938 |
Title:
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Bayesian System Network Modeling of Multiple Sparse Replicates
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Author(s):
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James Lawrence Norris*+ and David John and Kristopher Patton
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Companies:
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Wake Forest University and Wake Forest University and Wake Forest University
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Address:
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Department of Mathematics, Winston-Salem, NC, 27109,
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Keywords:
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Bioinformatics ;
system network modeling ;
Bayesian statistics
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Abstract:
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Collection of protein interaction or gene activation time course data is fundamental in systems network studies. In many of these investigations, multiple replications with a sparse number of time points in each replication are obtained. Frequently, the replications are analyzed separately and only simple or ad hoc syntheses are performed. In this contribution, we utilize all the information from all of the replications to make rigorous composite inferences about the network. Specifically, we determine Bayesian posterior probabilities for network features through the following: determination of closed form Bayes factors for networks (here, modeled as directed acyclic graphs, DAGs), multi-faceted Metropolis-Hastings searching over the DAG space, and Bayesian model averaging. Our simulations suggest that our method identifies many existing associations.
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