Knowledge management in a Systems Biology approach to translational medicine. Our focus is to combine rigorous mathematical and statistical methods with realistic applications. We present methods for the simultaneous analysis of gene expression and pharmacokinetic data that lay the groundwork for personalized medicine. Short Abstract: We describe applications of mixture models to pharmacokinetics, gene expression analysis and pharmacogenomics. Tatiana Tatarinova- Loyola Marymount UniversityĪlan Schumitzky (University of Southern California, Mathematics) Nonlinear Mixture Models with Applications to Clinical Pharmacokinetics and Gene Expression Analysis We demonstrate our model using the Genica data set. Secondly, we extend the method to identify two-way interaction effects. Firstly, the model allows inclusion of multiple types of covariate in addition to marker data. Short Abstract: In this poster, we extend a Bayesian method for detecting disease-related loci. Jonathan Keith (Queensland University of Technology, School of Mathematical Sciences) Peter Visscher (Queensland Institute of Medical Research, Genetic Epidemiology, Molecular Epidemiology and Queensland Statistical Genetics Laboratories) Kerrie Mengersen (Queensland University of Technology, School of Mathematical Sciences) A Bayesian Regression Model with Variable Selection for Genome-Wide Association Studies: Detection of Epistasis EffectsĬarla Chen- Queensland University of Technology
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