Biological sequence analysis : probabalistic models of proteins and nucleic acids / Richard Durbin [and three others].
By: Durbin, Richard [author.]
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Material type: 
BookPublisher: Cambridge : Cambridge University Press, 1998Description: 1 online resource (xi, 356 pages) : digital, PDF file(s).Content type: text Media type: computer Carrier type: online resourceISBN: 9780511790492 (ebook).Subject(s): Nucleotide sequence -- Statistical methods | Amino acid sequence -- Statistical methods | Numerical analysis | ProbabilitiesAdditional physical formats: Print version: : No titleDDC classification: 572.8/633 Online resources: Click here to access online Summary: 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.
Title from publisher's bibliographic system (viewed on 05 Oct 2015).
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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