Mathematics 655 - Biomed & Health Data Analysis

In this course, we will review, develop, and evaluate some computational biology methods. We will implement most of these methods in Python. Although programming skills, machine learning, or computational biology background are preferred, they are not required for this course. Importantly, this is a research-based course; it is an introduction on how to do research in computational biology. We all work as a team to learn novel methods in computational biology and hopefully find ways to improve them. We will read some recently published papers, which include four cutting-edge papers on computational oncology, implement the methods that have been introduced in these papers, and reproduce their results. Except the first few lectures, a team of students will present the papers and their implementation of methods. Students should be interested in Python programming, computational biology, and doing research as a team member. Students will be evaluated based on their participation, presentations, and works, including their codes and HWs.

Knowledge of calculus and linear algebra are required.
Knowledge of statistics (Stat 516, Stat 608, or equivalent) preferred, but not required.
Knowledge of regression (Stat 525, Stat 625, Stat 697R, or equivalent) preferred, but not required.

Advanced undergraduate students may request permission of instructor to enroll.