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Not teaching in Spring 2026 | |||||
BIOL 6385 Pradipta Ray | |||||
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| Name | Grades | Rating | |||
|---|---|---|---|---|---|
Not teaching in Spring 2026 | |||||
BIOL 6385 Pradipta Ray | |||||
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: Spring
Grades: 43
Median GPA: B+
Mean GPA: 3.084
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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: Spring
Grades: 43
Median GPA: B+
Mean GPA: 3.084
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