JSM 2005 - Toronto

Abstract #302390

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 304
Type: Invited
Date/Time: Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #302390
Title: Statistical Problems in Protein Interaction Networks
Author(s): Fengzhu Sun*+ and Ting Chen
Companies: University of Southern California and University of Southern California
Address: 1042 West 36th Place, DRB288, Los Angeles, CA, 90089-1113, United States
Keywords: protein interaction ; markov random fields
Abstract:

Statistical computation has played an important role in genomic research. With the accumulation of a large amount of genomic data such as molecular sequences, gene expressions, protein interaction networks, and proteomics data, the role of statistical computation becomes more important. Large-scale protein interaction data generally have high false positive and false negative rates. In order to use them to understand biological problems, it is important to understand the usefulness and limitations of large-scale protein interaction datasets. In this paper, we developed statistical methods to estimate the accuracy of several large-scale protein interaction data based on gene expression data as well as protein localization data. Assigning functions to novel proteins is one of the most important problems in the post-genomic era. Here, we illustrate a novel approach that employs the theory of Markov random fields to infer a protein's functions using an integrated approach combining various sources of biological data. The model is flexible in that other protein pairwise relationship information and features of individual proteins can be easily incorporated.

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Revised March 2005