Computational Phylogenetics: An Introduction to Designing Methods for Phylogeny Estimation

Cambridge University Press
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A comprehensive account of both basic and advanced material in phylogeny estimation, focusing on computational and statistical issues. No background in biology or computer science is assumed, and there is minimal use of mathematical formulas, meaning that students from many disciplines, including biology, computer science, statistics, and applied mathematics, will find the text accessible. The mathematical and statistical foundations of phylogeny estimation are presented rigorously, following which more advanced material is covered. This includes substantial chapters on multi-locus phylogeny estimation, supertree methods, multiple sequence alignment techniques, and designing methods for large-scale phylogeny estimation. The author provides key analytical techniques to prove theoretical properties about methods, as well as addressing performance in practice for methods for estimating trees. Research problems requiring novel computational methods are also presented, so that graduate students and researchers from varying disciplines will be able to enter the broad and exciting field of computational phylogenetics.
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About the author

Tandy Warnow is a Founder Professor of Engineering at the University of Illinois, Urbana-Champaign. Her awards include the National Science Foundation Young Investigator Award (1994), the David and Lucile Packard Foundation Award in Science and Engineering (1996), a Radcliffe Institute for Advanced Study Fellowship (2003), and a John Simon Guggenheim Memorial Foundation Fellowship (2011). She was elected a Fellow of the Association for Computing Machinery (ACM) in 2015, and of the International Society for Computational Biology (ISCB) in 2017.

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Additional Information

Publisher
Cambridge University Press
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Published on
Nov 2, 2017
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Pages
400
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ISBN
9781316886922
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Best For
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Language
English
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Genres
Computers / Bioinformatics
Computers / Natural Language Processing
Computers / Programming / Algorithms
Mathematics / Discrete Mathematics
Mathematics / Probability & Statistics / General
Science / Life Sciences / Biology
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Content Protection
This content is DRM protected.
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