Combined Degree Curriculum - Biomedical Informatics Track
Before graduate coursework begins, Combined Degree students complete an independent study in spring of their first year in medical school. This is followed by their first lab rotation which takes place in the summer that same year. In their third year, students do full time coursework and two more lab rotations. The GCB program requires Combined Degree students following the Biomedical Informatics (BMI) track to take six courses:
- 1 CU of Fundamentals in Statistics
- 1 CU of Fundamentals in Computation
- 1 CU of Core Machine Learning
- 1 CU of Core BMI Domain Knowledge
- BMIN 5220: Natural Language Processing for Health or CIS 5300: Natural Language Processing
- BMIN 6010: Learning Health Systems for Biomedical Informatics
Example Schedule
| Fall | Spring | Summer | |
|---|---|---|---|
| Year 1 | BMIN 5220 GCB 5360 STAT 5100 2nd Lab Rotation 3rd Lab Rotation |
BMIN 5040 BMIN 6010 CIS 5200 Pre-dissertation Research Candidacy Exam |
Dissertation |
| Year 2+ | Dissertation | Dissertation | Dissertation |
Additional example schedules may be found here, at the bottom of the page. For a complete overview of curricula for our Combined Degree programs, please visit the MD-PhD website or VMD-PhD website.
Fundamentals in Statistics
All GCB students are required to take a fundamental course in Statistics. Typically, this will be a probability theory class we developed for our students (GCB5330), but some students enter our program with the background to take more advanced Statistical training. Courses in this pool that satisfy this requirement are:
- GCB 5330: Statistics for Genomics and Biomedical Informatics
- STAT 5100: Probability Theory
- BSTA 6200: Probability I
- Chair-approved course in advanced statistics (STAT 5000+) or advanced biostatistics (BSTA 6000+)
Fundamentals in Computations
- Any Core Machine Learning course.
- GCB 5360: Fundamentals of Computational Biology
- BIOL 5535: Introduction to Computational Biology & Biological Modeling
- BIOL 5860: Mathematical Modeling in Biology
- BMIN 5020: Database and Data Integration
- BMIN 5250: Intro to Python Programming
- CIS 5020: Analysis of Algorithms
- CIS 5450: Big Data Analytics
- CIS 5520: Advanced Programing
- CIS 5590: Programming and Problem Solving
- CIS 5650: GPU Programming Architecture
- CIS 6770: Advanced Topics in Algorithms and Complexity
Additional alternatives may also satisfy this requirement, subject to Chair approval.
Core Machine Learning
Machine learning and approaches that utilize Artificial Intelligence are increasingly being utilized to facilitate biological and clinical inferences on data sets of increasingly large sizes. Thus, we believe GCB students need to carry forward a fundamental understanding of this approach and its use in these contexts. Students may enter into the program substantial expertise from previous coursework; in this case, more advanced coursework in specific topic areas or advanced techniques can be taken instead. Courses in this pool that satisfy this requirement are:
- BMIN 5210: Advanced Methods and Health Applications in Machine Learning
- CIS 5190: Applied Machine Learning
- CIS 5200: Machine Learning
- CIS 5210: Artificial Intelligence
- CIS 5220: Deep Learning for Data Science
- CIS 6200: Advanced Topics in Deep Learning
- ESE 5460: Principles of Deep Learning
Additional alternatives may also satisfy this requirement, subject to Chair approval.
Core BMI Domain Knowledge
GCB students on the BMI track should develop domain knowledge around key topics and approaches in biomedical informatics. The selection of courses can be tailored to the specifics of the student, as matriculating students tend to emerge from diverse backgrounds where they may have had very little (and need introductory materials) to substantial levels (where they can engaged in specific topics of interest) of biomedical health informatics training. Courses in this pool that satisfy this requirement are:
- BMIN 5040: Special Topics in Biomedical Informatics
- BMIN 5010: Introduction to Biomedical Health Informatics
- BMIN 5060: Standards and Clinical Terminology
- BMIN 5070: Human Factors
- CIS 7000: Health, Health Systems, and Technology
Additional alternatives may also satisfy this requirement, subject to Chair approval.
Lab Rotations (GCB 6990)
Because it is essential that candidates have a firm training in biology and experimental techniques, a crucial component of the GCB curriculum is research rotations in the laboratories of GCB-affiliated faculty. Students in this program are required to do three lab rotations as part of their training. The definition of a lab rotation is flexible and includes the possibility of rotations in a computer science lab (for example, the application of data mining techniques to biological information sources) or a course of directed reading and research in mathematics/statistics, but students should expect to spend at least 25 hours per week in their rotation lab. At least one rotation must be a wet-lab project, and one must be computational.
Combined Degree students do an 11 week rotation before their 2nd year in medical school. Rotations two and three will take place during the third year fall term, lasting 9 weeks each.
The dissertation laboratory is usually chosen from one (or more) of these rotation labs, although this is not required. To ensure breadth of the training experience, all laboratory assignments must be approved in advance by the GCB Chair.
Pre-dissertation Research (GCB 8990)
Once the student has identified a thesis lab, generally after their third rotation, they begin graded lab work in their chosen dissertation laboratory in the Spring. Students will advance to full time dissertation research work after passing their Candidacy Exam.