Statistical Bioinformatics with R

Designed for a one- or two-semester senior undergraduate or graduate bioinformatics course, Statistical Bioinformatics 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 and 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.

Provides coverage of complex statistical methods in context with applications in bioinformatics. This book integrates biological, statistical and computational concepts. It includes R & SAS code. It presents Bayesian methods and the modern multiple testing principles.

Inhalt

1. Introduction2. Genomics3. Probability and Statistical Theory4. Special Distributions, Properties and Applications5. Statistical Inference and Applications6. Nonparametric Statistics7. Bayesian Statistics8. Markov Chain, Monte Carlo9. Analysis of Variance10. Design of Experiments11. Multiple Testing of Hypotheses