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 m...

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Bibliographische Detailangaben

1. Verfasser:	Durbin, Richard (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Cambridge Cambridge University Press 1998
Schlagworte:	Nucleotide sequence / Statistical methods Amino acid sequence / Statistical methods Numerical analysis Probabilities Sequenzanalyse > Chemie Proteine Wahrscheinlichkeit Aminosäurensequenz Molekulare Bioinformatik Nucleinsäuren Modell Statistisches Modell Nucleotidsequenz
Online-Zugang:	DE-12 DE-92 URL des Erstveröffentlichers
Zusammenfassung:	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
Beschreibung:	Title from publisher's bibliographic system (viewed on 05 Oct 2015)
Beschreibung:	1 online resource (xi, 356 pages)
ISBN:	9780511790492
DOI:	10.1017/CBO9780511790492