Algebraic Statistics for Computational Biology
- Editors:
- L. Pachter, University of California, Berkeley
- B. Sturmfels, University of California, Berkeley
- Date Published: August 2005
- availability: Available
- format: Hardback
- isbn: 9780521857000
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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 beginning graduate courses.
Read more- First book in an exciting area at intersection of computation, statistics, and genomics
- Has quick guides to background topics, then applies these in case studies at forefront of research
- Includes links to online software and ancillary material from www.cambridge.org/9780521857000
Reviews & endorsements
"This substantial, enthusiastically presented, and confidently written book is largely based on and around a graduate course taught by the two editors, who are in the mathematics department at the University of California, Berkeley, during the fall of 2004. The four introductory chapters were written by the editors, whilst the seventeen chapters in Part II were written up as a result of research projects undertaken by course participants."
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×Product details
- Date Published: August 2005
- format: Hardback
- isbn: 9780521857000
- length: 434 pages
- dimensions: 261 x 186 x 28 mm
- weight: 1.12kg
- contains: 100 b/w illus. 3 colour illus. 5 tables
- availability: Available
Table of Contents
Preface
Part I. Introduction to the Four Themes:
1. Statistics L. Pachter and B. Sturmfels
2. Computation L. Pachter and B. Sturmfels
3. Algebra L. Pachter and B. Sturmfels
4. Biology L. Pachter and B. Sturmfels
Part II. Studies on the Four Themes:
5. Parametric inference R. Mihaescu
6. Polytope propagation on graphs M. Joswig
7. Parametric sequence alignment C. Dewey and K. Woods
8. Bounds for optimal sequence alignment S. Elizalde
9. Inference functions S. Elizalde
10. Geometry of Markov chains E. Kuo
11. Equations defining hidden Markov models N. Bray and J. Morton
12. The EM algorithm for hidden Markov models I. B. Hallgrímsdóttir, A. Milowski and J. Yu
13. Homology mapping with Markov random fields A. Caspi
14. Mutagenetic tree models N. Beerenwinkel and M. Drton
15. Catalog of small trees M. Casanellas, L. Garcia and S. Sullivant
16. The strand symmetric model M. Casanellas and S. Sullivant
17. Extending statistical models from trees to splits graphs D. Bryant
18. Small trees and generalized neighbor-joining M. Contois and D. Levy
19. Tree construction using Singular Value Decomposition N. Eriksson
20. Applications of interval methods to phylogenetics R. Sainudiin and R. Yoshida
21. Analysis of point mutations in vertebrate genomes J. Al-Aidroos and S. Snir
22. Ultra-conserved elements in vertebrate genomes M. Drton, N. Eriksson and G. Leung
Index.-
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Algebraic Statistics for Computational Biology
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