Co-sponsored by: Ernest Njouondo

Modern biomedical applications based on genomic and proteomic expression profiles often produce large amounts of data characterized by a very large number of measurements made on a much smaller number of sample points. This introduces difficult challenges in the application of classification methods to obtain accurate predictive models. In this talk, we discuss our recent work on this topic, highlighting Bayesian approaches to classification of proteomic and metagenomic data, as well as novel approaches for classification and error estimation based on restricted and nonstationary data.

Speaker(s): Ulisses M. Braga-Neto,

Agenda:

6:30 Registration

6:45 Dinner

7:10 Presentation

8:10 Q&A

8:15 Closing Remarks

Location:
5430 Westheimer Rd
Houston, Texas
TX 77056