Buy Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids by Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison available and for sale on Brightsurf

View Larger Image	Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids by Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison List Price:  $57.00 Price:  $51.20 You Save:  $5.80 (10%) Available:  Usually ships in 24 hours Sales Rank:  132005 Studio:  Cambridge University Press Binding:  Paperback Number Of Pages:  356 Publication Date:  July 01, 1999 Publisher:  Cambridge University Press
EDITORIAL REVIEWS Product Description 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. CUSTOMER REVIEWS (Average Customer Rating: 4.5 based on 17 reviews) Technically brilliant but totally inaccessible   While this is perhaps the best book on Hidden Markov Models in Bioinformatics available, you would do well to read Rabiner's review paper. For me this is the type of book that would put potential students off bioinformatics for life. It is too technical and uses inappropriate notation. It has too many "It is easily shown" phrases which means that actually the real proof would be rather involved. Dynamic programming is not explained very well. If you have a maths or computer background then go for it but if you prefer your Bio in Bioinformatics then stay well clear and go for Mount. April 21, 2008 An Excellent Introduction   This book gives an excellent introduction into sequence analysis for a person who is already somewhat familiar with the basics of Bayesian techniques. The authors illustrate concepts, as and when they are introduced, via carefully selected examples; comprehension is made much easier because of this. January 01, 2008 Great reference   A great reference and a good introduction to many important concepts in sequence analysis. However, if you don't have a reasonable grounding in math you may struggle with the terse notation. Borodovsky's companion book is an excellent partner for this book. Get both. September 06, 2007 One of the best available   Although this book is based primarily on work that was completed in 1998, and therefore somewhat out of date, it is the best book I have found for teaching bioinformatics. I selected this as the best of the available books on the subject for use in my bioinformatics and numerical methods course which is to be taught in the fall of 2007 at Univ. of Conn. This course is an upper division undergraduate and first year graduate course. That is roughly the level of this text and the comparative advantage of this book is the excellent presentation and thorough discussion of the algorithms. A student armed with Matlab or MathScriptor can take this book and start writing algorithms for sequence alignment and Hidden Markov Method (HMM) analysis after only the first three or four chapters. This book is in its 11th printing and is nearly error free (I found only a few in the figures). This book is strongly recommended for both students and researchers, particularly those interested in protein alignment, phylogenic analysis or an introduction to Hidden Markov Methods. August 17, 2007 Truly an Excellent Book   I will agree and submit: this is an invaluable introduction to the field of bioinformatics. With introductions to everything from sequence analysis to hidden markov models and even a primer on grammars, this is a useful introduction both to biological applications for computer scientists *as well as* computational methods for biologists. I am in a joint graduate-level biology/computer science class and we are using this book as a foundation to bring both groups up to speed and it seems to be working out nicely. However, one criticism is that sometimes Durbin et al jump into subjects without an adequate introduction or with one that is overcomplexified. In other words, they sometimes break Einstein's the rule of "make everything as simple as possible but not simpler". Durbin et al do not always make things as simple as possible. And it is annoying when they do not. Especially when I see them confusing the bejebus out of the biology people over computer science concepts that are really not that complicated through overly technical jargon. But this is rare and they provide many insightful diagrams to clear up their algorithms as well as lucid ways to introduce biological concepts. Sometimes the introduction of an algorithm/theory *and* a biological concept molds together beautifully such that the reader is simultaneously being infused with both. An example of this phenomenon is their dual introduction to CpG islands and markov models. February 18, 2006 SIMILAR PRODUCTS An Introduction to Bioinformatics Algorithms (Computational Molecular Biology) by Neil C. Jones, Pavel A. Pevzner Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology by Dan Gusfield Problems and Solutions in Biological Sequence Analysis by Mark Borodovsky, Svetlana Ekisheva Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health) by Warren J. Ewens, Gregory Grant Bioinformatics: Sequence and Genome Analysis by David W. Mount