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  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.

Ancillary list:
* Online ISM-
* Companion Website w/ R code and Ebook-
* Powerpoint slides-

Key Features

  • 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

Table of Contents

  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


No. of pages:
© 2009
Academic Press
eBook ISBN:
Print ISBN:
Print ISBN:

About the author

Sunil Mathur

Affiliations and Expertise

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