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
1. Introduction 2. Genomics 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