Description: About this productProduct InformationProbablistic 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.Product IdentifiersPublisherCambridge University PressISBN-100521629713ISBN-139780521629713eBay Product ID (ePID)245005Product Key FeaturesAuthorRichard Durbin, Graeme Mitchison, SEAN R. Eddy, Anders S. KroghPublication NameBiological Sequence Analysis : Probabilistic Models of Proteins and Nucleic AcidsFormatTrade PaperbackLanguageEnglishPublication Year1998TypeTextbookNumber of Pages370 PagesDimensionsItem Length9.6inItem Height0.8inItem Width6.7inItem Weight22.8 OzAdditional Product FeaturesLc Classification NumberQp620 .B576 1998Reviews‘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 ReviewsTable 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.Copyright Date1998Target AudienceScholarly & ProfessionalTopicLife Sciences / Genetics & Genomics, Chemistry / OrganicLccn97-046769Dewey Decimal572.8/633Dewey Edition21IllustratedYesGenreScience
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Book Title: Biological Sequence Analysis: Probabilistic Models of Proteins a,
Narrative Type: Biochemistry
Genre: N/A
Intended Audience: N/A
Number of Pages: 370 Pages
Publication Name: Biological Sequence Analysis : Probabilistic Models of Proteins and Nucleic Acids
Language: English
Publisher: Cambridge University Press
Publication Year: 1998
Subject: Life Sciences / Genetics & Genomics, Chemistry / Organic
Item Height: 0.8 in
Type: Textbook
Item Weight: 22.8 Oz
Item Length: 9.6 in
Author: Sean R. Eddy, Richard Durbin, Graeme Mitchison, Anders S. Krogh
Subject Area: Science
Item Width: 6.7 in
Format: Trade Paperback