This course is designed to help students better understand the impact of computer science and mathematics on the field of medical bioinformatics, with a particular emphasis on the GWAS pipeline. This semester, we will additionally focus on applications to mental diseases. The course is divided into five chapters:
- SNPs and Haplotypes: Linkage Disequilibrium and the Haplotype Phasing Problem
- Protein Folding, Misfolding, and Disease
- Markov Chain Monte Carlo and Spectral Graph Theory, with Applications to Population Stratification
- Missing Heritability, Genetic Heterogeneity, and Rare and Common Genetic Variants
- Polygenic Risk Scores and GWAS
Each chapter is devoted to a fundamental field of research and mathematics in medical bioinformatics. We will explore not only the foundations that led to important discoveries and procedures like GWAS and AlphaFold but also how they can be applied to specific diseases. As in each of Professor Istrail’s undergraduate courses, we will come to understand together the most elegant of “beautiful” algorithms in the medical arena and how they were conceived. Each chapter will have a variety of information on its mathematical foundations, its applications in medicine, and of course, its history.
FAQ
Who takes the course?
This course is primarily targeted at graduate students, although we welcome all advanced undergraduates who have taken one of Professor Istrail's courses before (CS181, CS182). This course assumes in-depth programming knowledge in addition to some understanding of linear algebra and statistics, as well as an appreciation of genetic approaches to understanding disease. If you have any concerns about whether your background in these areas will be an issue, please come speak with either Professor Istrail or the TA staff, or read the sections below for more detail.
What biology background is needed?
There are no biology prerequisites, and no prior biology knowledge is assumed; the material that you need to know will be covered in class. There may be some assumed biological knowledge from CS181 and/or CS182, but it will not be a significant portion.
What computer science and mathematics background is needed?
In order to take this course, you must have taken CS181 in a past semester or have equivalent background from a different institution (for graduate students). From CS181, recall that students in the course generally have some prior exposure to basic concepts of discrete math (graphs, recurrence relations), discrete probability (random variables, independence), and algorithms (big-O notation, pseudocode). Given this course also dabbles in spectral graph theory, we will rely on a working understanding of basic linear algebra.
What programming background is needed?
This class has fewer programming assignments than CS181, but some may find them trickier than most of the CS181 assignments. Given we will be developing some new assignments throughout the semester, we request that all students be open to programming in Python. So, if you had the programming prerequisites for CS181 waived or took the course in R, make sure you spend some time brushing up on your programming skills before the semester starts.