Relating GeneChip® Data To Measures of Pharmacological and Toxicological Phenotypes

Can one use microarray measured gene expression (MMGE) data to predict pharmacological and toxicological phenotypes in animals and man? Can similar data be used to account for a significant portion of the individualized variability in patient drug response? In collaboration with the Computational Biology Group at Roche Palo Alto, we are evaluating and contrasting the computational approaches that have been proposed to address these questions. Two large sets of MMGE data are being used to compare and contrast the information that results from use of the various available statistical pattern extraction, clustering and evaluation tools and techniques. Our current focus is on a variable selection and testing strategy that is particularly driven by directly relevant archival phenotypic data, information and knowledge.

Key personnel: Yuanyuan Xiao and Donglei Hu.

Collaborators: Mark Segal, Steve Shiboski, Michael N. Liebman, and Roche Palo Alto.


The premise of the agent paradigm, its related theory and methodologies together with advances in multilevel modeling of complex systems of interactions opened new frontiers for advancing the physical, natural, social, military, and information sciences and engineering...