Computational Genomics and Proteomics

Catalog description

This course introduces computational inference and visualization approaches for high-throughput data from genomics and proteomics. Topics include an introduction to high-throughput experimental data, experiment planning, data normalization, data representation, clustering, classification, approaches for detecting differential expression, hierarchical Bayesian models, Bayesian variable selection, data integration, statistical network models, and statistical metrics for model validation. Prerequisite(s): STAT 7640 >= C and DATS 8170 >= C Lecture Hours: 3 Repeatability: May not be repeated for credit. Grade Mode: Normal, Audit Program Restrictions: DPHIL_BIOS, MS_BIOS Schedule Type (Primary): Lecture Click here for the Schedule of Classes.