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Algebraic Statistics for Computational Biology

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ISBN-10: 0521857007

ISBN-13: 9780521857000

Edition: 2005

Authors: L. Pachter, B. Sturmfels

List price: $98.00
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Description:

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 for advanced undergraduate and…    
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Book details

List price: $98.00
Copyright year: 2005
Publisher: Cambridge University Press
Publication date: 8/22/2005
Binding: Hardcover
Pages: 434
Size: 7.00" wide x 10.00" long x 1.00" tall
Weight: 2.332
Language: English

Lior Pachter is Associate Professor of Mathematics at the University of California, Berkeley. He received his PhD in mathematics from the Massachusetts Institute of Technology in 1999. He then moved to the mathematics department at UC Berkeley where he was a postdoctoral researcher for two years, before being hired as an assistant professor. He has been awarded an NSF Career award, and has received the Sloan Fellowship for his work on molecular biology and evolution. Equally at home amongst both mathematicians and biologists, he has published over 40 research articles in areas ranging from combinatorics to gene finding, and has participated in several large genome projects.

Preface
Introduction to the Four Themes
Statistics
Computation
Algebra
Biology
Studies on the Four Themes
Parametric inference
Polytope propagation on graphs
Parametric sequence alignment
Bounds for optimal sequence alignment
Inference functions
Geometry of Markov chains
Equations defining hidden Markov models
The EM algorithm for hidden Markov models
Homology mapping with Markov random fields
Mutagenetic tree models
Catalog of small trees
The strand symmetric model
Extending statistical models from trees to splits graphs
Small trees and generalized neighbor-joining
Tree construction using Singular Value Decomposition
Applications of interval methods to phylogenetics
Analysis of point mutations in vertebrate genomes
Ultra-conserved elements in vertebrate genomes
Index