Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids |  | Authors: Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison
Publisher: Cambridge University Press
List Price: $61.00
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as of 11/25/2009 00:52 CST details
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Seller: green_great_books
Rating:  19 reviews
Sales Rank: 154704
Media: Paperback
Pages: 356
Number Of Items: 1
Shipping Weight (lbs): 1.5
Dimensions (in): 9.6 x 6.9 x 0.8
ISBN: 0521629713
Dewey Decimal Number: 572.8633
EAN: 9780521629713
ASIN: 0521629713
Publication Date: July 1, 1999
Availability: Usually ships in 1-2 business days
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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.
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.
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| Customer Reviews:
Showing reviews 1-5 of 19
 nice book April 16, 2009
Jianbin Wang
very useful book for sequence analysis. i use it as part of my bioinformatics reference in stanford. professor's recommendation!
Must Have for any Bioinformatics Student March 3, 2009
Wyatt Clark (Indiana, USA)
This book is a must have for any bioinformatics student working with sequence or genomic data. Useful for anyone attempting to gain an understanding of stochastic models, hidden markov models, and semi-hidden markov models
 Technically brilliant but totally inaccessible April 21, 2008
Andrew Dalby (oxford)
3 out of 5 found this review helpful
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.
An Excellent Introduction January 1, 2008
kprema (Miami, FL, USA)
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.
 Great reference September 5, 2007
Mark Schreiber
1 out of 1 found this review helpful
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.
Showing reviews 1-5 of 19
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