Computational Bioscience (CPBS)
CPBS 7601 - Computing Skills in the Biomedical Sciences (2 Credits)
To introduce the skills needed to perform reproducible and high-quality computational research. Topics include version control with Git, integrated development environments, software development fundamentals, Data management and Tidyverse, high performance computing and parallel computing, workflows and orchestration, and code documentation and readability.
Grading Basis: Letter Grade
A-GRAD Restricted to graduate students only.
Typically Offered: Fall.
CPBS 7602 - Introduction to Big Data in the Biomedical Science (2 Credits)
To introduce standard methods in the analysis of high-dimensional biomedical data including supervised and unsupervised learning, dimension reduction, classification, clustering, and big data visualization. This course will prepare students to participate in rotations and take more advanced classes in these areas as electives.
Grading Basis: Letter Grade
Typically Offered: Fall.
CPBS 7605 - Ethics in Bioinformatics (1 Credit)
Discussions of professional conduct, social implications of research and questions raised by biomedical research, with an emphasis on topics relevant to computational biologists. Active student participation is required. Offered every other year.
Grading Basis: Letter Grade
Typically Offered: Fall, Spring.
CPBS 7606 - Statistics for the Basic Sciences (3 Credits)
This course provides an overview of fundamental concepts in statistics such as hypothesis testing and estimation and it provides an overview of statistical methods (for example, regression and analysis of variance) that apply to many areas of science. Crosslisted Course: BIOS 6606.
Grading Basis: Letter Grade
A-GRAD Restricted to graduate students only.
Typically Offered: Fall, Spring.
CPBS 7650 - Research in Computational Bioscience (1-5 Credits)
Research work in Computational Bioscience.
Grading Basis: Letter Grade with IP
Repeatable. Max Credits: 5.
A-GRAD Restricted to graduate students only.
Typically Offered: Fall, Spring, Summer.
CPBS 7655 - Statistical Methods in Genetic Association Studies (3 Credits)
This course is designed to give an introduction to statistical methods in genetic association studies.Topics include an introduction to population genetics topics relevant to genetic association studies, design strategies, and analysis methods for case-control and family data. Crosslisted Course: BIOS 6655.
Grading Basis: Letter Grade
Typically Offered: Fall.
CPBS 7659 - Statistical Methods in Genomics (3 Credits)
This course will give an introduction to statistical methods for analyzing molecular sequences and genomic data. Topics include hidden Markov models for sequence alignment, molecular evolution and gene expression data analysis. Crosslisted Course: BIOS 6659 (sponsoring department) / BIOS 7659
Grading Basis: Letter Grade
Typically Offered: Spring.
CPBS 7660 - Analysis of Genomics Data Using R and Bioconductor (2 Credits)
This course provides students with hands on experience in solving real life biological problems using the statistical software R and Bioconductor. Students will work and communicate with participating researchers and clinicians on their case studies of genomics data. Pre/Corequisite BIOS 6602 or 6612, or consent of instructor.
Grading Basis: Letter Grade
Typically Offered: Fall, Spring, Summer.
CPBS 7712 - Research Methods in Biomedical Informatics (4 Credits)
This course focuses on application of algorithms to analysis of different types of big data and provides training in how to plan, develop, execute and report on research in computational biology. Topics include: 1) Molecular Data; 2) Biomedical data; 3) Drug/disease data.
Grading Basis: Letter Grade
A-GRAD Restricted to graduate students only.
Typically Offered: Spring.
CPBS 7785 - Advance Topics in in Computational Bioscience (1-5 Credits)
An in-depth, discussion-based course for graduate students focusing on key topics in computational biosciences across four tracks: Bioinformatics, Clinical Informatics, Imaging Informatics, and Computational Neuroscience. The course explores interdisciplinary research initiatives and the computational and statistical methods used to address complex biomedical data. Students will gain a strong foundation in modern computational approaches, including machine learning, natural language processing, computer vision, and artificial intelligence (AI), with an emphasis on real-world applications in biomedical research and healthcare. Ethical, safe, and responsible deployment of these technologies is also highlighted. This course is listed for the benefit of the advanced student who desires to pursue one or more topics in considerable dept. Supervision by a full time faculty member is necessary.
Grading Basis: Letter Grade
A-GRAD Restricted to graduate students only.
Typically Offered: Fall, Spring, Summer.
CPBS 7791 - Readings in Computational Bioscience (1 Credit)
A seminar course in which students read and present recent publications from the primary computational bioscience literature.
Grading Basis: Letter Grade
Repeatable. Max Credits: 1.
A-GRAD Restricted to graduate students only.
Typically Offered: Fall, Spring, Summer.
CPBS 7792 - Special Topics in Computational Bioscience (1-3 Credits)
Topic varies by semester. Designed to give students a chance to evaluate critically some practical or theoretical problem under faculty supervision and to present results of their thinking to fellow students and instructors for critical evaluation. Prerequesites: Permission of Instructor.
Grading Basis: Letter Grade
Repeatable. Max Credits: 3.
A-GRAD Restricted to graduate students only.
Typically Offered: Fall, Spring, Summer.
CPBS 8990 - Doctoral Thesis (1-10 Credits)
Doctoral Thesis work in Computational Bioscience.
Grading Basis: Letter Grade with IP
Repeatable. Max Credits: 10.
A-GRAD Restricted to graduate students only.
Additional Information: Report as Full Time.
Typically Offered: Fall, Spring, Summer.