Title: "Statistical Modeling of Graph Theoretic Data in Systems Biology"
Speaker: Dr. Denise Scholtens, Department of Preventive Medicine, Northwestern University
Place: Mechanical Engineering (ME) 161; March 18, 2008, Tuesday, 4:30pm


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.

Associated Reading:

Scholtens D, Chiang T, Huber W, Gentleman R. 2007. Estimating node degree in bait-prey graphs. Bioinformatics. 2008 Jan 15;24(2):218-24. Epub 2007 Nov 19.

Chiang T, Scholtens D, Sarkar D, Gentleman R, Huber W. 2007. Coverage and error models of protein-protein interaction data by directed graph analysis. Genome Biol. 2007;8(9):R186.

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