JSM 2005 Online Program

Abstract #304863

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	472
Type:	Invited
Date/Time:	Thursday, August 11, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Section on Physical and Engineering Sciences
Abstract - #304863
Title:	Ontology-enhanced Statistical Analysis
Author(s):	Jiajun Liu*+ and Jacqueline M. Hughes-Oliver and Alan Menius
Companies:	North Carolina State University and North Carolina State University and GlaxoSmithKline
Address:	Department of Statistics, Raleigh, NC, 27695-0001,
Keywords:
Abstract:	New systems biology technology platforms and techniques give scientists the ability to measure thousands of biomolecules, including genes, proteins, lipids, and metabolites. Analyses of these combined data typically are complex, resulting in hundreds of statistically significant findings. The potential for type-I error and lack of interpretability can greatly diminish the impact of these experiments. Our goal is to analyze gene expression data using classical statistical methods, guided by domain knowledge as captured in the Gene Ontology (GO). Methods combining existing domain knowledge with classical methods can yield more interpretable results, or even improved analysis. Our research reveals this conclusion with various simulations as well as analysis on real datasets.

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