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Biological Sequence Analysis : Probabilistic Models of Proteins and Nucleic Acids by Sean R. Eddy, Richard Durbin, Graeme Mitchison and Anders S. Krogh (1998, Trade Paperback)
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- Buy It NowBiological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
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About this product
Product Identifiers
PublisherCambridge University Press
ISBN-100521629713
ISBN-139780521629713
eBay Product ID (ePID)245005
Product Key Features
Number of Pages370 Pages
LanguageEnglish
Publication NameBiological Sequence Analysis : Probabilistic Models of Proteins and Nucleic Acids
Publication Year1998
SubjectLife Sciences / Genetics & Genomics, Chemistry / Organic
TypeTextbook
AuthorSean R. Eddy, Richard Durbin, Graeme Mitchison, Anders S. Krogh
Subject AreaScience
FormatTrade Paperback
Dimensions
Item Height0.8 in
Item Weight22.8 Oz
Item Length9.6 in
Item Width6.7 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN97-046769
Reviews‘This book fills an important gap in the bioinformatics literature and should be required reading for anyone who is interested in doing serious work in biological sequence analysis. For biologists who have little formal training in statistics or probability, it is a long-awaited contribution that, short of consulting a professional statistician who is well versed in molecular biology, is the best source of statistical information that is relevant to sequence-alignment problems. This book seems destined to become a classic. I highly recommend it.’Andrew F. Neuwald, Trends in Biochemical Sciences, 'This book is a nice tutorial and introduction to the field and can certainly be recommended to all who wish to analyse biological sequences with computer methods. It can also serve as a basis for a university course for undergraduates.' Trends in Cell Biology, "This is one of the more rewarding books I have read within this field. My overall evaluation is that this book is very good and a must read for active participants in the field. In addition, it could be particularly useful for molecular biologists" Theoretical Population Biology, ‘This book is a nice tutorial and introduction to the field and can certainly be recommended to all who wish to analyse biological sequences with computer methods. It can also serve as a basis for a university course for undergraduates.’Trends in Cell Biology, "...successfully integrates numerous probabilistic models with computational algorithms to solve molecular biology problems of sequence alignment...an excellent textbook selection for a course on bioinformatics and a very useful consultation book for a mathematician, statistician, or biometrician working in sequence alignment." Bulletin of Mathematical Biology, ‘ … an enjoyable opportunity to see a blend of modeling and data analysis at work on an important class of problems in the rapidly growing field of computational biology.’D. Siegmund, Short Book Reviews, ' ... an enjoyable opportunity to see a blend of modeling and data analysis at work on an important class of problems in the rapidly growing field of computational biology.' D. Siegmund, Short Book Reviews, 'This book fills an important gap in the bioinformatics literature and should be required reading for anyone who is interested in doing serious work in biological sequence analysis. For biologists who have little formal training in statistics or probability, it is a long-awaited contribution that, short of consulting a professional statistician who is well versed in molecular biology, is the best source of statistical information that is relevant to sequence-alignment problems. This book seems destined to become a classic. I highly recommend it.' Andrew F. Neuwald, Trends in Biochemical Sciences, ' … an enjoyable opportunity to see a blend of modeling and data analysis at work on an important class of problems in the rapidly growing field of computational biology.' D. Siegmund, Short Book Reviews
Dewey Edition21
IllustratedYes
Dewey Decimal572.8/633
Table Of Content1. Introduction; 2. Pairwise sequence alignment; 3. Multiple alignments; 4. Hidden Markov models; 5. Hidden Markov models applied to biological sequences; 6. The Chomsky hierarchy of formal grammars; 7. RNA and stochastic context-free grammars; 8. Phylogenetic trees; 9. Phylogeny and alignment; Index.
SynopsisProbablistic 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., Probabilistic models are becoming increasingly important in analysing 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 analysing 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 aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.
LC Classification NumberQP620 .B576 1998
