MMSc-BMI Curriculum

This introductory course surveys methods in biomedical informatics, including methods and approaches used in clinical informatics, bioinformatics, imaging and population health informatics. Basic concepts, trends, and best practices in biomedical informatics and biomedical research, including research ethics, the conduct of research in medical science, and broad issues relative to the application of research to human health are covered. Students will become familiar with core biomedical informatics methodologies.

BMI 701 Course and Schedule Details

This course will teach students advanced coding and statistics needed to extract, transform and prepare data to work with various biomedical informatics pipelines and analyze the results. Topics covered include:

• Foundational statistical theory for biomedical informatics
• Advanced R with focus on implementing statistical analyses and data visualizations
• Advanced Python with focus on running machine learning pipelines

BMI 714 Course and Schedule Details

This course will teach graduate students both important concepts and practical skills needed to perform high-quality biomedical informatics research. The course examines core principles related to the conduct of biomedical research and some of its practical applications in the field of biomedical informatics. Students will also learn to synthesize and communicate these concepts through written work and a poster presentation.

BMI 717 Course and Schedule Details

Outside of the set of required courses, students will have the opportunity to choose from a wide range of available courses to meet the program's elective requirements. In the first semester, students must complete a minimum of 6 credits to fulfill this requirement. 

Possible elective options in the fall semester include:

• BMI 706: Data Visualization for Biomedical Applications (2 credits)
• BMI 708: Precision Medicine (2 credits)
• BMI 709: Creating Biomedical Dashboards with R Shiny (2 credits)
• Any of the pre-approved courses listed on the Program Electives Webpage. 

Students will also have the opportunity to petition program approval to take courses outside of these options.  

Artificial intelligence is poised to enable breakthroughs in science and reshape medicine. This introductory course provides a survey of artificial intelligence for biomedical informatics, covering methods for key data modalities: clinical data, networks, language, and images. It introduces machine learning problems from a practical perspective, focusing on tasks that drive the adoption of machine learning in biology and medicine. It outlines foundational algorithms and emphasizes the subtleties of working with biomedical data and ways to evaluate and transition machine learning methods into biomedical and clinical implementation. An important consideration in this course is the broader impact of artificial intelligence, particularly topics of bias and fairness, interpretability, and ethical and legal considerations when dealing with artificial intelligence.

The second in a three-part quarter course series on academic research. The course’s learning objectives are:

• Execute a rigorous and systematic literature search
• Characterize the various components of a research article
• Critically appraise literature and extract conclusions relevant to the reader
• Understand the peer-review process and the importance of providing and receiving constructive feedback 

The last in the three-part series of quarter courses will set students up for successfully creating and defending a master thesis. Students will write and submit their Thesis Proposal as part of this course. The course’s learning objectives are:

• Write a research proposal and intermittent research progress reports
• Generate testable hypotheses and design an experimental setup accordingly
• Plan, organize, and time-manage your research
• Write up research in a style suitable for academic publication
• Present your own research through various communication channels other than papers (e.g., presentations, visuals, teaching, etc.)

Outside of the set of required courses, students will have the opportunity to choose from a wide range of available courses to meet the program's elective requirements. In the second semester, students must complete a minimum of 8 credits to fulfill this requirement. 

Possible elective options in the spring semester include:

• Deep Learning for Biomedical Data (2 credits)
• Single-cell analysis for Functional Genomics of Disease (2 credits)
• Creating Biomedical Dashboards Using R Shiny (2 credits)
• Health Information Technology: From Ideation to Innovation (4 credits)
• Any of the pre-approved courses listed on the Program Electives Webpage. 

Students will also have the opportunity to petition program approval to take courses outside of these options.