"The book is amply illustrated with biological applications and examples."
--Cell
Cambridge University Press Web Site, April, 2000
Summary
Probabilistic models are becoming increasingly important in analyzing the huge amount of data being produced
by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used
for analyzing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary
structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms.
This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more
generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it is
accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other
fields, and at the same time presents the state of the art in this new and important field.
Winner of amazon.com's Bestselling Title of 1999 in its category-Organic Chemistry
Table of Contents
1. Introduction
2. Pairwise sequence alignment
3. Multiple alignments
4. Hidden Markov models
5. Hidden Markov models applied to biological sequences
6. The Chomsky hierarchy of formal grammars
7. RNA and stochastic context-free grammars
8. Phylogenetic trees
9. Phylogeny and alignment
