Author: Richard Durbin
Edition: First Edition
Binding: Paperback
ISBN: 0521629713
Edition: First Edition
Binding: Paperback
ISBN: 0521629713
Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
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.
Problems and Solutions in Biological Sequence Analysis: Mark Borodovsky, Svetlana Ekisheva
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
Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids: Anders Krogh, Graeme Mitchison, Richard Durbin, Sean RY
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Problems and Solutions in Biological Sequence Analysis, ISBN-13: 9780521612302, ISBN-10: 0521612306
author mark borodovsky author svetlana ekisheva format paperback language english publication year 11 09 2006 subject mathematics sciences subject 2 life sciences general title problems and solutions in biological sequence analysis author mark borodovsky svetlana ekisheva publisher cambridge univ pr publication date sep 21 2006 pages 346 binding paperback edition 1 st dimensions 6 75 wx 9 25 hx 0 75 d isbn 0521612306 subject science life sciences genetics genomics description companion to biol
Download Biological Sequence Analysis
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
Written by an interdisciplinary team of authors, it is accessible to molecular biologists, computer scientists, and mathematicians
