The primary focus of my research is to better understand genetic determinants of drug response and adverse reactions to common therapies. Specifically I have identified and characterized novel genetic variations underlying adverse reactions to hypertension drugs and variable response to asthma medications. My research applies traditional statistical approaches and machine learning methods to uncover relationships in complex genomics data, including identifying and characterizing human subjects for genomics studies using patient-derived data and finding genetic variants that are associated with clinical outcomes to medications. In addition, I use both in silico and molecular biology approaches to characterize the function of genetic variants correlated with drug response. My current research goals are to integrate multiple biological datasets from human clinical trials to identify potentially functional variations as well as networks of potentially related genes underlying human disease or drug response. My work is in the area pharmacogenomics, which is a translational discipline of genomics that promises to revolutionize health care by replacing the current trial-and-error paradigm of drug therapy to maximize therapeutic benefit while reducing costs.
School of Computing
Department of Biomedical and Molecular Sciences