“Regulatory genomics approaches to prognosis prediction and interpretation of genetic variation”
Professor Gerald Quon
UC Davis Genome Center
2:10 p.m., Friday 2/26/16, in LSA 1022
Omics technologies can play an important role in many aspects of the management of human health, ranging from the prediction of patient prognosis and response to treatments, to interpretation of genetic variation associated with complex diseases and identification of drug targets. However, the widespread adoption and success of omics technologies in the clinic is still relatively limited due to technical and biological challenges in data collection and interpretation. My research focuses on developing machine learning and statistical approaches to address these challenges, and in this talk, I will discuss two examples. First, I will demonstrate how we have improved transcriptome-based prediction of cancer patient prognosis by developing a computational model to perform in silico micro-dissection of heterogeneous tumor samples. Second, I will present a novel statistical model that predicts the functional impact of non-coding genetic variation associated with complex diseases, and show how we have used this model to gain insight into type 2 diabetes and cholesterol genetics.