BGT 200/STA 270
Statistical Methods for Computational Biology
Fall 2003
Course Description:
Introduction to methods of statistical inference and stochastic
modeling underlying common tools in functional genomics and
computational molecular biology. Statistical principles behind a
range of problems in bioinformatics and genomics, including biological
sequence analysis and structure prediction, database searching, gene
expression analysis, statistical genetics, phylogenetic inference and
genetic epidemiology. Applied data analysis techniques including
regression analysis, clustering, principal components analysis (PCA),
and data visualization. Advanced statistical models and algorithms
including hidden Markov models (HMMs), the EM algorithm, Gibbs
sampling and Markov chain Monte Carlo (MCMC).
Course assignments will involve hands-on data analysis of a range of
biological datasets, and each lecture will include a practical
component demonstrating use of standard statistical software. As a
final project, each student will perform a more detailed analysis of a
dataset of their choice, with instructor guidance.
| Course meetings: | Friday 11:50-2:10 |
| Location: | 025 Old Chem Bldg. |
last updated 20 August 2003