Using Input-Dependent Weights for Model Combination and Model Selection with Multiple Sources of Data

• Activity Number: 45
• Type: Invited
• Date/Time: Sunday, August 6, 2006 : 4:00 PM to 5:50 PM
• Sponsor: International Chinese Statistical Association
• Author(s): Wei Pan*+ and Guanghua Xiao and Xiaohong Huang
• Companies: University of Minnesota and University of Minnesota and University of Minnesota
• Keywords: classification ; microarray data ; model mixing ; partial least squares ; prediction

Abstract

With constantly accumulating data, the importance of integrative analyses of multiple data sources has been recognized increasingly. A natural approach is to combine multiple models, each built on one source of data. A challenge, however, is to account for different local information contents of different sources of data. The choice of the weight on each candidate model (and thus each source of data) may depend on the input for which a prediction is to be made, suggesting the constant weights used in most existing approaches may not be optimal. Here, we propose an input-dependent weighting (IDW) scheme. We apply IDW to discriminating human heart failure etiology using two sources of gene expression data and to gene function prediction by a combined analysis of gene-expression and protein-protein interaction data.