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Activity Number:
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85
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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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WNAR
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| Abstract - #307885 |
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Title:
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Bayesian Methods for Predicting Interacting Protein Pairs Using Domain Information
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Author(s):
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Inyoung Kim and Yin Liu and Hongyu Zhao*+
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Companies:
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Yale University and Yale University and Yale University
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Address:
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200 LEPH 60 College Street, New Haven, CT, 06520,
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Keywords:
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Bayesian method ; protein interaction ; domain interaction ; bioinformatics ; computational biology ; proteomics
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Abstract:
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Protein-protein interactions play important roles in most fundamental cellular processes. Therefore, it is important to develop effective statistical approaches to predicting protein interactions based on recently available large-scale yet noisy experimental data. In this paper we propose Bayesian methods to predict protein interactions based on interactions among domains, the functional units of proteins. We also propose a new model to associate protein interaction probabilities with domain interaction probabilities. When our Bayesian methods are compared with a likelihood-based approach, our methods have smaller mean square errors through both simulations and theoretical justification under a special scenario. The large-scale protein-protein interaction data obtained from high throughput yeast two-hybrid experiments are used to demonstrate the advantages of the Bayesian approaches.
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