Algebraic Statistics for Computational Biology. This item is unavailable.Algebraic Statistics for 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 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.

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.

Bernd Sturmfels is Professor of Mathematics and Computer Science at the University of California, Berkeley. His honors include a National Young Investigator Fellowship, a Sloan Fellowship, and a David and Lucile Packard Fellowship. Sturmfels served as von Neumann Professor at TU Munich in Summer 2002, as the Hewlett-Packard Research Professor at MSRI Berkeley in 2003/04, and he was a Clay Senior Scholar in 2004.