Arnold School of Public Health

Division of Biostatistics

Lecture Presented By

Yi Li, PhD

School of Public Health

University of Michigan

“Conditional Networks: A New Framework for Integrative Analyses”

Friday, November 17, 2017

1:00 pm – 2:00 pm

Discovery I Building

Room 140

Abstract: Understanding how genes interplay with each other and how their regulations are associated with other high-dimensional genomic markers may uncover the underlying mechanism of disease progression processes.  Graphical models have commonly been used in simultaneously learning the response network and the associations between the response, but these models assume a homogeneous population and ignore the heterogeneity between individual-level networks.  In this talk, we propose a conditional graphical model with functional precision parameters. We propose a Fisher scoring matching approach for variable selection and network recovery.  We show that the proposed method can consistently select important predictors and recover the response network structure. The proposed method is computationally inexpensive and can be directly applied to analyzing “omic" scaled networks and DNA data, such as the cancer genome atlas (TCGA) data, to study cancer-triggering biological pathways.