Statistics 697BD - ST-Biomed&HealthDataAnalysis

In this course, we will apply several novel machine learning algorithms, including normalization methods, classification and regression analysis on cancer patient data sets to arrive at personalized cancer treatments. We will develop several algorithms for analyzing cancer data sets, including gene expression data sets. 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 cutting-edge methods in computational biology and hopefully find ways to improve them. We will read some recently published papers and implement methods that have been introduced in these papers. 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. There is no exam or final project. 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.