Evan Johnson - Boston University

Title: Adaptive factor analysis models for assessing drug sensitivity and pathway activation in individual patient samples. Abstract: The development of personalized treatment regimes is an active area of current research in genomics. The focus of our research is to investigate core biological components that contribute to disease prognosis and development, and to develop latent variable models to accurately determine optimal therapeutic regimens for individual patients. To accomplish this aim, we have developed an adaptive Bayesian factor analysis model that integrates in vitro experimental data into our models while still allowing for the refinement and adaptation of drug or pathway profiles within each patient cohort and individual, efficiently accounting for cell-type specific pathway differences or any “rewiring” do to cancer deregulation. Our modeling approach serves an essential role in our attempts to develop a comprehensive and integrated set of relevant, biologically interpretable computational tools for genomic studies in personalized medicine. We are currently working on a variety of applications using data from cancer and pulmonary disease with the potential to be extremely important in treating patients with these diseases.