BIOCB 4381 - Biomedical Data Mining and Modeling

Fall. 3 credits. Letter grades only.

Prerequisite: at least one introductory course in computer programming (any language) and one in statistical methods, or permission of the instructor. Co-meets with BIOCB 6381.

H. Yu.

A biomedical data science course using Python and available bioinformatics tools and techniques for the analysis of molecular biological data, including biosequences, microarrays, and networks. This course emphasizes practical skills rather than theory. Topics include advanced Python programming, R and Bioconductor, sequence alignment, MySQL database (DBI), web programming and services (CGI), genomics and proteomics data mining and analysis, machine learning, and methods for inferring and analyzing regulatory, protein-protein interaction, and metabolite networks.

Outcome 1: Demonstrate familiarity with the basics of applied statistical methodology.

Outcome 2: Demonstrate familiarity with statistical software and a programming language.

Outcome 3: Demonstrate ability to perform complex data mining of biological datasets using a programming language.

Outcome 4: Demonstrate ability to effectively communicate the results of a statistical analysis to biologists.

Outcome 5: Demonstrate familiarity with statistical and computational tools for high throughput genomic data.

Outcome 6: Demonstrate ability to build stand-alone softwares, web tools, and databases for analyzing biological data.