GS010133  Statistical Methods in Bioinformatics

Liu, Yin. Three semester hours. Fall, annually. Prerequisite: Introduction to Mathematical Statistics (GS010113) or consent of instructor.

The objective of this course is to introduce students to the concepts and statistical methods for
analyzing large-scale biological data generated from emerging genomic and proteomic
techniques. The course will focus on the integration oftwo disciplines - biology and statistics by
first describing statistical methods most often used in the field of bioinformatics and then
discussing their applications on the computational analysis of gene sequence, expression and
biological interactions at a large scale. The statistical methods covered include dynamic
programming, maximum likelihood estimation, Bayesian inference, Hidden Markov Models,
Markov chain Monte Carlo, classification and clustering methods. The students will master
advanced applications of statistical computing in a wide range of biological and biomedical
problems, including multiple sequence alignment, biomarker and disease gene identification,
inference of protein interaction network, functional modules and signal transduction networks.