Biological Sequence Analysis

Probabilistic Models of Proteins and Nucleic Acids

Nonfiction, Science & Nature, Science, Biological Sciences, Genetics, Technology
Cover of the book Biological Sequence Analysis by Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison, Cambridge University Press
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Author: Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison ISBN: 9781107085015
Publisher: Cambridge University Press Publication: April 23, 1998
Imprint: Cambridge University Press Language: English
Author: Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison
ISBN: 9781107085015
Publisher: Cambridge University Press
Publication: April 23, 1998
Imprint: Cambridge University Press
Language: English

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.

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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.

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