Statistical methods in bioinformatics : an introduction / Warren J. Ewens, Gregory R. Grant.

Includes bibliographical references (p. [561]-580) and index.

1. Probability theory (i) : one random variable -- 2. Probability theory (ii) : many random variables -- 3. Statistics (i) : an introduction to statistical inference -- 4. Stochastic processes (i) : Poisson processes and Markov chains -- 5. The analysis of one DNA sequence -- 6. The analysis of multiple DNA or protein sequences -- 7. Stochastic processes (ii) : random walks -- 8. Statistics (ii) : classical estimation theory -- 9. Statistics (iii) : classical hypothesis testing theory -- 10. BLAST -- 11. Stochastic processes (iii) : Markov chains -- 12. Hidden Markov models -- 13. Gene expression, microarrays, and multiple testing -- 14. Evolutionary models -- 15. Phylogenetic tree estimation -- App. A. Basic notions in biology -- App. B. Mathematical formulae and results -- App. C. Computational aspects of the binomial and generalized geometric distribution functions -- App. D. BLAST : sums of normalized scores.

"Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community." "The book is written so as to appeal to biologists and computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved with bioinformatics."--BOOK JACKET.