This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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681
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Type:
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Contributed
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Date/Time:
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Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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Abstract - #309066 |
Title:
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Exact Distribution of Prediction Error Rates for Protein and Domain Interactions
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Author(s):
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Donald E.K. Martin*+ and John Aston
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Companies:
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North Carolina State University and University of Warwick
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Address:
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4272 SAS Hall, Raleigh, NC, 27695,
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Keywords:
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protein and domain interactions ;
sum-product algorithm ;
recursive computation ;
computational statistics
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Abstract:
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The study of interactions of proteins and of the domains that comprise them helps with understanding protein functions. High-throughput methods of measuring protein interactions have high error rates; however structural similarities between proteins may be exploited to make model-based predictions of their interactions, and of interacting domains. A Bayesian network may be used to model protein and domain interactions in a unified fashion, and the sum-product algorithm allows the computation of marginal probabilities of interactions. Proteins with probabilities above a threshold are predicted as interacting. Using this setup, we give an algorithm to compute model-based prediction error rate distributions, conditional on noisy measurements. The methodology applies to hidden states of undirected and directed graphical models represented using conditional random fields and factor graphs.
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