Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series

Node-and-edge graphs are a foundational structure for recording, visualizing and analyzing high-throughput genomics and proteomics data. Like most data, systems biology observations generated by high- throughput technologies are subject to measurement error and therefore must be treated accordingly. Currently, most analyses present only naive summary statistics of these observations. We apply classic statistical modeling approaches for a variety of problems, thereby improving inference on commonly reported graph statistics, local features of interest in global graphs, and plausible error probability bounds.

Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.