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BIPG 5200/7200 Statistical Methods in Bioinformatics
This course introduces students to statistical methods commonly used in bioinformatics. Students will learn to use statistical programs and related bioinformatics resources locally and on the Internet. Lectures and lab discussion will emphasize on the statistical models and methods underlying the computational tools. The course will focus on the application of the newer statistical methods and the reasoning behind these applications. More emphasis will be placed on the analysis of functional genomic experiments and students will learn statistical techniques to handle microarray data.
Sadik A. Khuder. Ph.D.
Office: Room 12 RHC
Phone: (419) 383-4089; Fax: (419) 383-6244
Prerequisite: Statistical Methods I or permission of instructor.
At the end of the course, students will:
1. have an excellent understanding and appreciation of fundamental concepts of statistics
2. understand the formulation of stochastic models for genomic data,
3. be able to apply statistical techniques to analyze microarray data and interpret the results generated,
4. be able to use statistical tests commonly employed in bioinformatics
5. be familiar with modern statistical methods and software to solve complex problems in bioinformatics
Grading: 30% assignments, 20% projects, 50% final exam.
Richard C. Deonier, Simon Tavare, and Michael S. Waterman, Computational Genome Analysis: An Introduction, Springer, 2005.
Warren J. Ewens and Gregory R. Grant, Statistical Methods in Bioinformatics: An Introduction, Second Edition. Springer, 2005.
Gentleman et al. Bioinformatics and computational biology solutions using R and Bioconductor. Springer, New York, 2005