Wes Viles, Colby
Tuesday, November 14, starting at 3:30 pm, Davis 201
Refreshments at 3:00 pm, outside of Davis 216
Network data analysis fundamentally rests upon the collection of basic measurements relevant to interactions among elements within a system. A network representation of that system can be constructed whereby the quantities of interest are of a relational and combinatorial nature. Network analysis of biological systems, such as the human-associated microbiome, is inherently challenging since it frequently relies on high-dimensional, statistically-dependent measurements. This presentation will describe the analytical framework and computational methods used in my research on problems of network characterization. On the topic of subgraph counts in large networks, I will provide insight on the probabilistic approach my coauthors and I developed for quantifying the propagation of low-rate measurement error through the process of network construction and summary. On an applied matter, I will subsequently describe our computational approaches to the estimation and statistical evaluation of mesoscopic complex network structures, including dynamic communities of multi-slice networks and genuine high-order ecological interactions of the human microbiota from which vital synergistic biological processes emerge. Our quantification of the complexity, as measured by total correlation, is one aspect of our aim to develop computational methods for predicting high-order microbial interactions that are pertinent to clinical endpoints.