STAT 9270 - Computational Genomics and Proteomics

Institution:

Augusta University

Subject:

Description:

Computational inference and visualization approaches for high-thoughput data from genomics and proteomics. Topics include and introduction to high-thoughput experimental data, experiment planning, data normalization, data representation, clustering, classification, approaches for detecting differential experession, hierarchical Bayesian models, Gayesian viariable selection, other computational approaches to varialbe selection, statistical network models, and statistical metrics for model validation. 3.000 Credit Hours 3.000 Lecture hours Levels: Graduate Semester Schedule Types: Lecture Graduate Studies College Biostatistics Department Course Attributes: Elective Course

Credits:

3.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

www.augusta.edu

Phone Number:

(706) 721-0211

Regional Accreditation:

Southern Association of Colleges and Schools

Calendar System:

Semester

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