Results - BIOL 6385 - UTD TRENDS

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Computational Biology

BIOL 6385

School of Natural Sciences and Mathematics

Machine learning and probabilistic graphical models have become essential tools for analyzing and understanding complex systems biology data in biomedical research. This course introduces fundamental principles and methods behind the most important high throughput data analysis tools. Applications will cover molecular evolutionary models, DNA/protein motif discovery, gene prediction, high-throughput sequencing and microarray data analysis, computational modeling gene expression regulation, and biological pathway and network analysis. 3 credit hours.

Prerequisite: Some background in elementary statistics/probability or introductory bioinformatics, or instructor consent required.

Offering Frequency: Each year

Grades: 32

Median GPA: B+

Mean GPA: 3.068

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