STA 571

Statistical Methods for Genetic Data

This is an introduction to quantitative methods assoicated with the analysis of human genetic data, with an emphasis on applied projects aimed at prediction of disease status of a new sample on the basis of observed samples and identification of biomarkers leading to human disease. Topics will include overview of microarray, proteomics, and metablomics data, overview of supervised learning, linear methods for classification, kernel methods, boosting and additive trees, neural networks, support vector machines and flexible discriminants, and unsupervised learning. Students must be familiar with matrix notation and the statistical programming language R will be used in this course.

Units: 4.0