Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids

Durbin, Richard; Eddy, Sean R.; Krogh, Anders; Mitchison, Graeme

ISBN 10: 0521629713 / ISBN 13: 9780521629713

Published by Cambridge University Press, 1998

About this Item

The item is very worn but is perfectly usable. Signs of wear can include aesthetic issues such as scratches, dents, worn and creased covers, folded page corners and minor liquid stains. All pages and the cover are intact, but the dust cover may be missing. Pages may include moderate to heavy amount of notes and highlighting, but the text is not obscured or unreadable. Page edges may have foxing age related spots and browning . May NOT include discs, access code or other supplemental materials.

Seller Inventory # BVV.0521629713.A

Bibliographic Details

• Title: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
• Publisher: Cambridge University Press
• Publication Date: 1998
• Language: English
• ISBN 10: 0521629713
• ISBN 13: 9780521629713
• Binding: Soft cover
• Condition: acceptable

About this title

Synopsis

Probablistic models are becoming increasingly important in analyzing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analyzing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it is accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time presents the state of the art in this new and important field.

Book Description

Probabilistic methods are assuming greater significance in the analysis of nucleotide sequence data. This book provides the first unified, up-to-date and self-contained account of such methods, and more generally of probabilistic methods of sequence analysis, presented in a Bayesian framework.