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Biological sequence analysis :probab...

Durbin, Richard,

Biological sequence analysis :probabalistic models of proteins and nucleic acids /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	572.8/633
書名/作者:	Biological sequence analysis : : probabalistic models of proteins and nucleic acids // Richard Durbin [and three others].
作者:	Durbin, Richard,
面頁冊數:	1 online resource (xi, 356 pages) : : digital, PDF file(s).
附註:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
標題:	Nucleotide sequence - Statistical methods.
標題:	Amino acid sequence - Statistical methods.
標題:	Numerical analysis.
標題:	Probabilities.
ISBN:	9780511790492 (ebook)
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1017/CBO9780511790492

Biological sequence analysis :probabalistic models of proteins and nucleic acids /
Durbin, Richard,

Biological sequence analysis :probabalistic models of proteins and nucleic acids /Richard Durbin [and three others]. - 1 online resource (xi, 356 pages) :digital, PDF file(s).

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.

ISBN: 9780511790492 (ebook)Subjects--Topical Terms:

644133
Nucleotide sequence
--Statistical methods.

LC Class. No.: QP620 / .D87 1998

Dewey Class. No.: 572.8/633

Biological sequence analysis :probabalistic models of proteins and nucleic acids /
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520 $a 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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650 0 $a Amino acid sequence $x Statistical methods. $3 644134
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