JSM Preliminary Online Program
This is the preliminary program for the 2009 Joint Statistical Meetings in Washington, DC.

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Activity Number: 191
Type: Invited
Date/Time: Monday, August 3, 2009 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #303123
Title: Kernel Machine Approach to Testing the Significance of Multiple Markers for Risk Prediction
Author(s): Tianxi Cai*+
Companies: Harvard University
Address: Dept of Biostatistics, Boston, MA, 02115,
Keywords: kernel machine learning ; risk prediction ; high dimensional data
Abstract:

DNA microarray technologies permit systematic approaches to biological discovery that have begun to have a profound impact on biological research, pharmacology, and medicine. The ability to obtain quantitative information about the complete transcription profile of cells promises to be an exceptionally powerful means to explore basic biology, diagnose disease, facilitate drug development, and tailor therapeutics to specific pathologies. The standard approach to the computational analysis of microarray data identifies important genes by assessing the marginal effects of individual genes on the phenotype of interest. However, when multiple genes relate to the phenotype simultaneously via a complex structure, such type of single gene analysis may not be effective and may lead to results that are not reproducible.

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