Statistical Issues in Disease Diagnosis Using Public Gene Expression Data

• Activity Number: 55
• Type: Invited
• Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
• Sponsor: WNAR
• Author(s): Haiyan Huang*+
• Companies: University of California at Berkeley
• Keywords: Hierarchical multi-label classification ; integrative data analysis ; computational disease diagnosis ; Bayesian classifier ; cross-platform data standardization

Abstract

The rapid accumulation of gene expression data has offered unprecedented opportunities to study human diseases. The National Center for Biotechnology Information Gene Expression Omnibus is currently the largest database that systematically documents the genome-wide molecular basis of diseases. However, thus far, this resource has been far from fully utilized. This paper describes the first study to transform public gene expression repositories into an automated disease diagnosis database. Particularly, we have developed a systematic framework, including a two-stage Bayesian learning approach, to achieve the diagnosis of one or multiple diseases for a query expression profile along a hierarchical disease taxonomy. Our approach, including standardizing cross-platform gene expression data and heterogeneous disease annotations, allows analyzing both sources of information in a unified probabilistic system. A high level of overall diagnostic accuracy was shown by cross validation. We also showed how our disease diagnosis system can be used to characterize complex phenotypes and to construct a disease-drug connectivity map.