Focusing on cardiovascular diseases, the goal of my research is to use multi-modal big data analysis to create reliable network models of human biology and disease. Network models have enormous potential to improve our ability to predict disease risk, identify new therapeutic targets, and to monitor molecular effects of treatments. To achieve this goal, I have designed and generated a range of clinical datasets of cardiovascular disease that combine detailed clinical characteristics with imaging, genomics, proteomics, and other types of data.
My research has long focused on cardiovascular disease. My early work explored the role of triglyceride-rich lipoproteins in coronary artery disease (CAD), and my postdoctoral studies in mouse models established the hepatic gene microsomal triglyceride transfer protein as a key target to lower plasma cholesterol levels and reduce atherosclerosis. Since then, my primary focus has been systems analyses to generate network models from large genomic datasets—both from CAD patients in the clinic and from cellular and mouse models of atherosclerosis progression and regression in the laboratory.