Statistical approaches for making sense of high-throughput biological data (Christine B. Peterson -- Stanford University)

Abstract: In this talk, I will discuss statistical approaches I have developed to gain insight into the complex networks of regulation and interaction that govern biological systems. Understanding these networks and how they are disrupted by disease is an important step in identifying potential targets for the treatment of disease. Firstly, I will describe my work on the inference of biological networks such as metabolic or protein interaction networks from high-throughput data. In particular, I will address graphical modeling methods I have proposed in the Bayesian framework for inferring such networks based on limited sample sizes, and illustrate the application of these approaches to highlight mechanisms underlying cancer progression. Secondly, I will address the problem of establishing the genetic basis of multivariate traits such as gene expression or other molecular profiling data. Here I propose a multi-stage multiple testing procedure which controls important error rates regarding the discovery of regulatory variants and the association of these variants to traits.

When 4:00 pm to 5:00 pm on Thursday, February 4, 2016
Location MCS 148