Computer Science-Applied: Bioinformatics
To submit a proposed plan of study, use the Focus Area Survey.
At first glance, you might not think biology is even remotely related to computer science. But in recent years, computer science has helped other scientists make enormous advances by giving them access to huge amounts of data.
Today, computer scientists develop ways to model and analyze biological data that can help scientists speed up their research or discover new insights into the secrets of life. And the Human Genome Project, for example, would not have been possible without having computers to analyze huge amounts of genetic data. Combining biology with computer science is called bioinformatics or computational biology.
Potential Job Opportunities
- Bioinformatics scientist: develop algorithms, integration & analysis of biological data across multiple databases. Companies: Ceres Inc. Somalogic Inc. Ocean Ridge Biosciences, ACGT
- Bioinformatician: design, develop, maintain data processing of biological data. Companies: UC Davis, University of Chicago, DNAnexus, Qiagen
Degree Requirements
Find the specific course requirements for the Applied: Bioinformatics option of Computer Science in the course catalog.
Example Plan - Core (23 credits) + Electives (9 credits)
Core
- BB 485 Applied Bioinformatics
- BI 221Z Principles of Biology: Cells
- BI 222Z Principles of Biology: Organisms
- CS 434 (Machine Learning & Data Mining)
- MTH 341 Linear Algebra I
- ST 314 Intro to Stats for Engineers (3)**
CS Electives
- CS 331 Intro to Artifical Intelligence
- CS 332 (CS 399) Intro to Applied Data Science w/ Programming
- CS 406 Projects (maximum 6 cr.)
- CS 420 Graph Theory w/ Apps to CS
- CS 432 (CS 499) Intro to Applied Machine Learning
-
CS 435 Applied Deep Learning
- CS 446 Networks in Computational Biology
- CS 453 Scientific Visualization
- CS 458 Intro to Info Visualization
- CS 475 Intro to Parallel Prog
- CS 499 Algorithms in Computational Biology
- CS 499 - If topic is appropriate (varies)
Biological Data Science Electives
- BDS 310 Foundations of BDS
- BDS 311 Computational Approaches for Biological Data
- BDS 472 Advanced Computing for Biological Data Analysis
- BDS 474 Intro to Genome Biology
- MTH 342 Linear Algebra II
Molecular Biology, Genetics, and Ecology Electives
- BB 314 Cell and Molecular Biology
- BB 331 Intro to Molecular Biology
- BI 311 Genetics
- BI 445 Evolution
- BI 454 Evolutionary Genomics
- BI 370 Ecology
- MB 302 General Microbiology
- MB 448 Microbe-Environment Interactions
- MB 420 Microbial Genome Evolution and Biodiversity
**ST 314 must be used in Focus area. Must complete 9 credits of CS restricted electives
Faculty
David Hendrix
Professor
Research Groups
Data Science and Engineering | Artificial Intelligence and Robotics | Health Engineering
Rebecca Hutchinson
Associate Professor Kearney Faculty Scholar
rebecca.hutchinson@oregonstate.edu
Research Groups
Data Science and Engineering
V John Mathews
Professor
Research Groups
Data Science and Engineering | Communications and Signal Processing | Health Engineering
Stephen Ramsey
Professor
Research Groups
Data Science and Engineering | Artificial Intelligence and Robotics | Health Engineering