Biological sequence analysis : probabilistic models of proteins and nucleic acids

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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

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Availability

Detail Information

Series Title

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Call Number

572.8633 Dur b

Publisher

New York : Cambridge University Press., 2006

Collation

xi, 357 p. : ill. : ind. ; 25 cm.

Language

English

ISBN/ISSN

9780521629713

Classification

NONE

Content Type

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Media Type

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Carrier Type

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Edition

10th

Subject(s)

Amino acid sequence
Nucleotide sequence--Statistical methods
Numerical analysis
Probabilities

Specific Detail Info

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Statement of Responsibility

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