CS 6343 - Machine Learning for Genomics, Transcriptomics and Proteomics

Institution:

Utah Tech University

Subject:

Computer Science

Description:

This course will assess the relationships among sequence, structure, and function in complex biological systems and networks. This course covers the application of computational algorithms in genomic, transcriptomic, and proteomic data analysis. **COURSE LEARNING OUTCOMES (CLOs)** At the successful conclusion of this course students will: 1. Employ specialized software and libraries to load, process, and analyze genomic, transcriptomic and proteomic data. 2. Understand and implement genome annotation algorithms to predict genes, regulatory elements, or other functional regions in genomic sequences. 3. Create and apply machine learning models for precision medicine focusing on identification of potential disease-associated biomarkers. 4. Implement computational algorithms for protein structure, function, and interaction prediction. Prerequisites: CS 6330 (B- or higher); AND CS 6331 (B- or higher). SP

Credits:

3.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

www.utahtech.edu/

Phone Number:

(435) 652-7500

Regional Accreditation:

Northwest Commission on Colleges and Universities

Calendar System:

Semester

General Education

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