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Review
'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
'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
Product 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.
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Astronomy began when the Babylonians mapped the heavens. Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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11 of 12 people found the following review helpful
Format:Paperback
This is one of the best and most concise books on current mathematical techniques as applied to sequences avaliable. The book (and each chapter in retrospect) is completely self contained (although might require a little reading around with regard to the probalistic aspects) and thorough. Well worth the money - especially some similar books are priced at almost double.
Format:Paperback
While this is perhaps the best book on Hidden Markov Models in Bioinformatics available, you would do well to read Rabiner's review paper. For me this is the type of book that would put potential students off bioinformatics for life. It is too technical and uses inappropriate notation. It has too many "It is easily shown" phrases which means that actually the real proof would be rather involved. Dynamic programming is not explained very well.
If you have a maths or computer background then go for it but if you prefer your Bio in Bioinformatics then stay well clear and go for something like Krane, Mount or Lesk.
If you have a maths or computer background then go for it but if you prefer your Bio in Bioinformatics then stay well clear and go for something like Krane, Mount or Lesk.
Format:Paperback
Well, this is considered a masterpiece if you are into biological machine learning.
The book does indeed cover the subject pretty well, in particular HMMs and profile HMMs.
Subjects are treated well and are often represented graphically too. Despite this, I think it would be better to have more examples (there are indeed very few of them).
It might be kinda hard for those who DON'T have a strong math/statistics background, since it does cover them in a couple of pages and assumes you know much about probability and stuff.
The book does indeed cover the subject pretty well, in particular HMMs and profile HMMs.
Subjects are treated well and are often represented graphically too. Despite this, I think it would be better to have more examples (there are indeed very few of them).
It might be kinda hard for those who DON'T have a strong math/statistics background, since it does cover them in a couple of pages and assumes you know much about probability and stuff.
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