Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
by Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison
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Book Summary Information
Author: Anders Krogh, Graeme Mitchison, Richard Durbin, Sean R. EddyEdition: Paperback
Published: 1999-07-01
ISBN: 0521629713
Number of pages: 356
Publisher: Cambridge University Press
Book Reviews of Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
Book Review: Technically brilliant but totally inaccessible
Summary: 2 Stars
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.
Summary of Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
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.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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