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Statistical Bioinformatics provides a balanced treatment of statistical theory in the context of bioinformatics applications.
Designed for a one or two semester senior undergraduate or graduate bioinformatics course, the text takes a broad view of the subject – not just gene expression and sequence analysis, but a careful balance of statistical theory in the context of bioinformatics applications.
The inclusion of R & SAS code as well as the development of advanced methodology such as Bayesian and Markov models provides students with the important foundation needed to conduct bioinformatics.
- Integrates biological, statistical and computational concepts
- Inclusion of R & SAS code
- Provides coverage of complex statistical methods in context with applications in bioinformatics
- Exercises and examples aid teaching and learning presented at the right level
- Bayesian methods and the modern multiple testing principles in one convenient book
Senior undergraduate and graduate level students taking a one or two semester course in bioinformatics
3. Probability and Statistical Theory
4. Special Distributions, Properties and Applications
5. Statistical Inference and Applications
6. Nonparametric Statistics
7. Bayesian Statistics
8. Markov Chain, Monte Carlo
9. Analysis of Variance
10. Design of Experiments
11. Multiple Testing of Hypotheses
- No. of pages:
- © Academic Press 2010
- 21st December 2009
- Academic Press
- eBook ISBN:
- Paperback ISBN:
- Hardcover ISBN:
Director, Statistical Computing and Consulting Center University of Mississippi, Oxford, USA
"Students and biologists who want to specialize in the fast-paced field of bioinformatics should read this book. Mathur brings together a comprehensive and very practical view of the field. He combines sufficient mathematical proofs with hints and suggestions, and provides many real examples taken directly from the genetics, proteomics, and molecular biology fields…Many other bioinformatics topics--for example, clustering algorithms, specialized R packages, or the challenges of analyzing mass-spectrometry data--are only alluded to and not covered fully in the book. However, in its entirety, this is a very useful, clearly written introduction to statistical bioinformatics with R. It contains many real examples, and would be a help to those starting out in the field."--Computing Reviews.com
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