Algebraic Statistics for Computational Biology [Hardcover]

This book, first published in 2005, offers an introduction to the application of algebraic statistics to computational biology.The quantitative analysis of biological sequence data is based on methods from statistics coupled with efficient algorithms from computer science. Algebra provides a framework for unifying many of the seemingly disparate techniques used by computational biologists. This book introduces this framework and describes tools for designing new algorithms for exact, accurate results. These are applied to biological problems such as aligning genomes, finding genes and constructing phylogenies.As the first book in the exciting and dynamic area, it will be welcomed as a text for self-study or for course use.The quantitative analysis of biological sequence data is based on methods from statistics coupled with efficient algorithms from computer science. Algebra provides a framework for unifying many of the seemingly disparate techniques used by computational biologists. This book introduces this framework and describes tools for designing new algorithms for exact, accurate results. These are applied to biological problems such as aligning genomes, finding genes and constructing phylogenies.As the first book in the exciting and dynamic area, it will be welcomed as a text for self-study or for course use.The quantitative analysis of biological sequence data is based on methods from statistics coupled with efficient algorithms from computer science. 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 for designing new algorithms for exact, accurate results. These algorithms can be applied to biological problems such as aligning genomes, finding genes and constructing phylogenies. As the first book in the exciting and dynamic area, it will be welcomed as a text for self-study or forl+