Algebraic Statistics for Computational Biology

edited by Lior Pachter and Bernd Sturmfels

[Table of Contents] [Excerpt] [Frontmatter] [Publisher]

About the book:

The quantitative analysis of biological sequence data is based on methods from statistics coupled with efficient algorithms from computer science. The mathematical field of algebra provides a framework for unifying many of the seemingly disparate techniques used by computational biologists. This book offers an introduction to this mathematical framework and describes tools from computational algebra that can be used to design new algorithms for exact, accurate results. These can be applied to biological problems such as aligning genomes, finding genes and constructing phylogenies.

The first part of this book consists of four chapters on the themes of Statistics, Computation, Algebra and Biology. These chapters offer a speedy self-contained introduction to the emerging field of algebraic statistics and its applications to genomics. Specific topics that are discussed include graphical models, Gröbner bases, maximum likelihood estimation, convex polytopes, phylogenetic combinatorics, tropical geometry and DNA sequence analysis.

In the second part the four themes are combined and developed to tackle real problems in computational genomics. Written by participants in a graduate seminar at Berkeley, it consists of 18 chapters which offer in-depth case studies at the very forefront of research in this area. Topics include parametric inference (with applications to sequence alignment), Markov chains and hidden Markov models (with emphasis on the EM algorithm), and new methods for phylogeny and comparative genomics. Also included are descriptions of software with examples.

As the first book in this exciting and dynamic area, it will be welcomed as a text for self-study or for advanced undergraduate and beginning graduate courses.




The Laboratory for Mathematical and Computational Biology, 2005.

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