Biological Sequence Analysis
15%
off

Biological Sequence Analysis : Probabilistic Models of Proteins and Nucleic Acids

4.17 (74 ratings by Goodreads)
By (author)  , By (author)  , By (author)  , By (author) 

Free delivery worldwide

Available. Dispatched from the UK in 1 business day
When will my order arrive?

Description

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.
show more

Product details

  • Paperback | 370 pages
  • 170 x 244 x 20mm | 660g
  • Cambridge, United Kingdom
  • English
  • 50 Tables, unspecified; 100 Line drawings, unspecified
  • 0521629713
  • 9780521629713
  • 289,351

Table of contents

1. Introduction; 2. Pairwise sequence alignment; 3. Multiple alignments; 4. Hidden Markov models; 5. Hidden Markov models applied to biological sequences; 6. The Chomsky hierarchy of formal grammars; 7. RNA and stochastic context-free grammars; 8. Phylogenetic trees; 9. Phylogeny and alignment; Index.
show more

Review Text

'This book fills an important gap in the bioinformatics literature and should be required reading for anyone who is interested in doing serious work in biological sequence analysis. For biologists who have little formal training in statistics or probability, it is a long-awaited contribution that, short of consulting a professional statistician who is well versed in molecular biology, is the best source of statistical information that is relevant to sequence-alignment problems. This book seems destined to become a classic. I highly recommend it.' Andrew F. Neuwald, Trends in Biochemical Sciences
show more

Review quote

'This book fills an important gap in the bioinformatics literature and should be required reading for anyone who is interested in doing serious work in biological sequence analysis. For biologists who have little formal training in statistics or probability, it is a long-awaited contribution that, short of consulting a professional statistician who is well versed in molecular biology, is the best source of statistical information that is relevant to sequence-alignment problems. This book seems destined to become a classic. I highly recommend it.' Andrew F. Neuwald, Trends in Biochemical Sciences 'This book is a nice tutorial and introduction to the field and can certainly be recommended to all who wish to analyse biological sequences with computer methods. It can also serve as a basis for a university course for undergraduates.' Trends in Cell Biology ' ... an enjoyable opportunity to see a blend of modeling and data analysis at work on an important class of problems in the rapidly growing field of computational biology.' D. Siegmund, Short Book Reviews "The book is amply illustrated with biological applications and examples." Cell "...successfully integrates numerous probabilistic models with computational algorithms to solve molecular biology problems of sequence alignment...an excellent textbook selection for a course on bioinformatics and a very useful consultation book for a mathematician, statistician, or biometrician working in sequence alignment." Bulletin of Mathematical Biology "This is one of the more rewarding books I have read within this field. My overall evaluation is that this book is very good and a must read for active participants in the field. In addition, it could be particularly useful for molecular biologists" Theoretical Population Biology
show more

Rating details

74 ratings
4.17 out of 5 stars
5 43% (32)
4 38% (28)
3 14% (10)
2 4% (3)
1 1% (1)
Book ratings by Goodreads
Goodreads is the world's largest site for readers with over 50 million reviews. We're featuring millions of their reader ratings on our book pages to help you find your new favourite book. Close X