Combined Degree Curriculum - Genomics and Algorithms 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 in the Genomics and Algorithms track to take six courses:
- GCB 5340: Experimental Genome Science
- 1 CU of Fundamentals in Statistics
- 1 CU of Fundamentals in Computation
- 1 CU of Core Machine Learning
- 1 CU from the list of Approach electives
- 1 CU from the list of Biological Specialty electives
Example Schedule
| Fall | Spring | Summer | |
|---|---|---|---|
| Year 1 | GCB 5330 GCB 5340 GCB 5360 2nd Lab Rotation 3rd Lab Rotation |
CIS 5200 Approach Elective Biological Specialty Elective Pre-Dissertation Research Candidacy Exam |
Dissertation |
| Year 2+ | Dissertation | Dissertation | Dissertation |
Additional example schedules may be found at the bottom of the page here. 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
Fundamentals in Computation
All GCB students are required to take a fundamental in course in Computational Biology, Algorithms, or programming. The selection of this course requirement is tailored to the specifics of the student, as matriculating students tend to emerge from diverse backgrounds where they may have had very little to substantial levels of algorithmic experiences. Courses in this pool that satisfy this requirement are:
- GCB 5360: Fundamentals of Computational Biology
- BIOL 5535: Introduction to Computational Biology & Biological Modeling
- CIS 5450: Big Data Analytics
- BIOL 5860: Math Modeling in Biology
- CIS 6770: Advanced Topics in Algorithms and Complexity
- CIS 5520: Advanced Programing
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:
- CIS 5200: Machine Learning
- CIS 5190: Applied Machine Learning
- CIS 5210: Artificial Intelligence
- CIS 5220: Deep Learning for Data Science
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.
Qualifying Approach and Biological Specialty Courses
Approach Courses
- Any Core Statistics or Core Machine Learning alternative course
- BMIN 5020: Database and Data Integration in Biomedical Research
- BMIN 5200: Foundations of Artificial Intelligence in Health (Spring)
- BMIN 5210: Advanced Methods and Health Applications in Machine Learning
- BMIN 5220: Natural Language Processing for Health
- BSTA 7870: Statistical Methods and Data Analysis (Fall)
- CIS 5450: Big Data Analytics (Fall)
- GCB 5370: Advanced Computational Biology
- STAT 4310: Statistical Inference (Fall/Spring)
- STAT 5000: Applied Regression and Analysis of Variance (Fall)
- STAT 9270: Bayesian Statistics (Spring)
Biological Specialty Courses
- BBCB 5850: Wistar Cancer Biology: Signaling Pathways in Cancer (Fall)
- BIOL 4220: Human Genetics and Genomics (Spring, odd years)
- CAMB 4830: Epigenetics (Fall)
- CAMB 4850: The RNA World: A Functional and Computational Analysis (Spring, even years)
- GCB 5770: Advanced Epigenetics Technology (Spring; not offered every year)
- GCB 7520: Seminar in Genomics (Spring)
This list is not exhaustive, and additional qualifying courses may be approved by the Advising Committee and GCB Chair. Students can visit the University Catalog to view available courses.
For descriptions of GCB courses, visit the PhD Curriculum page.