STA 290 Seminar Series
Thursday, November 3rd, 4:10pm, MSB 1147 (Colloquium Room)
Refreshments at 3:30pm in MSB 4110 (Statistics Lounge)
Speaker: Jennifer Sinnott (Ohio State University)
Title: "Kernel Machine Regression Methods in Risk Prediction"
Abstract: Attempts to predict risk using high dimensional genomic data can be made difficult by the large number of features and the potential complexity of the relationship between features and risk. Integrating prior biological knowledge into risk prediction with such data by grouping genomic features into pathways and networks reduces the dimensionality of the problem and could improve prediction accuracy. Additionally, such knowledge-based models can be more biologically grounded and interpretable. The kernel machine framework has been proposed as an effective approach for modeling the nonlinear and interactive effects of pathways on medical outcomes. In this talk, I will discuss these methods, as well as discuss some of the existing challenges in using this framework.