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Statistical Methods In
Bioinformatics

Contact instructor for class dates, times and meeting locations
Course Director: Yin Liu, Ph.D.

Neurobiology and Anatomy > Courses > Statistical Methods in Bioinformatics

Course Description

Advances in recent high-throughput experimental technologies have generated enormous amounts of data and provided valuable resources in studying gene sequences, expression and biological interaction at a whole genome-wide scale. Robust statistical models and efficient computational methods are needed to fully take advantage of the rapid data accumulation. This course will introduce students to the concepts and statistical methods for analyzing large-scale biological data generated from emerging genomic and proteomic techniques. 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 transcriptional regulation network, protein interaction network and protein functional modules.

Course Web Page: http://nba.uth.tmc.edu/homepage/liu/teaching/fall2009.html

Grading: homework (40%), midterm exam (30%), and final project (30%).

Time: Course schedule varies. Please see syllabus for details.

Location: We meet in room ETC 2.146 (Austin) or MSB G.520c (Houston). See syllabus for details.

Prerequisite

This course is designed for graduate students and advanced undergraduate students who are interested in the emerging area of biomedical informatics and computational biology. Basic knowledge in statistics inference, algorithms and programming experience in R/MATLAB/C/C++ are expected. Knowledge in biology is a plus but not a must.

Recommended Text

Warren J. Ewens, Gregory Grant: Statistical Methods in Bioinformatics: An Introduction, Springer, 2005

Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Cambridge University Press, 1999.


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