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Hidden Markov Models for Bioinformatics (Computational Biology) Paperback – May 1, 2002

4.0 out of 5 stars 4 customer reviews

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Product Details

  • Series: Computational Biology (Book 2)
  • Paperback: 391 pages
  • Publisher: Springer; Softcover reprint of the original 1st ed. 2001 edition (May 1, 2002)
  • Language: English
  • ISBN-10: 1402001363
  • ISBN-13: 978-1402001369
  • Product Dimensions: 6 x 0.9 x 9 inches
  • Shipping Weight: 1.4 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (4 customer reviews)
  • Amazon Best Sellers Rank: #2,302,288 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

By Seungwoo Hwang on March 10, 2004
Format: Paperback
The intended audience of this book are mathematicians. To understand this book, you should have prior coursework experience in at least several upper division undergraduate courses in mathematical statistics and probability theory. The structure of this book is also that of a typical math book; full of proposition, corollary, lemma, etc, and very limited use of illustrations (e.g., there is no single figure up to chapter 6).
I wanted a book with a mathematical sophistication simliar to Durbin's book, but this book is way more than that. On the other hand, I showed this book to a mathematics graduate student and she said this book is perfect for her. So I guess this book is written by a mathematician only for mathematicians.
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Format: Paperback
The book gives outstanding coverage of all that goes into building HMMs - one of the most important tools in genome analysis and structure prediction. It covers the field in extreme depth. More depth, in fact, than needed for building useful HMM systems. It not only presents the forward and backward algorithms leading up to Baum-Welch, it presents all the extras - convergence, etc.
This additional depth of coverage may go beyond many readers' needs. It is very helpful, though, for people who need more than the usual algorithms. By giving the background in such detail, a persistent reader can follow to a certain point, then create modifications with a clear idea of where the new algorithm actually comes from.
Regarding the current practice of HMM usage, I found it a bit thin. Widely-known tools based on HMMs are mentioned only occasionally and in passing, and HMM-based alignment is discussed only briefly. Well, this book isn't for the tool user. Perhaps more important, I found scant mention of scoring with respect to some background probability model ("null" model, as it's called here).
My one real complaint, and this is truly minor, is the quality of illustration. The line-drawings look like Word pictures - not necessarily a bad thing, if done well. These aren't particularly professional-looking, though, and oddly stretched or squashed in many cases. Still, they're readable enough and make all the needed points.
A lesser point, and not the author's fault, is the editorial implication that this book introduces probabilitic models in general. It does not. This is strictly about HMMs, not Bayesian nets, bootstrap techniques, or any of the dozens of other probabilistic models used in bioinformatics. That is not a flaw of the book, just a flaw in how it's represented.
If you are dedicated to becoming an expert in HMM construction and application, you must have this book. It's a bit much, though, for people who just want the results that HMMs give.
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The field of computational biology has expanded greatly in the last decade, mainly due to the increasing role of bioinformatics in the genome sequencing projects. This book outlines a particular set of algorithms called hidden Markov models, that are used frequently in genetic sequence search routines. The book is primarily for mathematicians who want to move into bioinformatics, but it could be read by a biologist who has a strong mathematical background. The book is detailed at some places, sparse in others, and reads like a literature survey at times, but many references are given, and there are very interesting exercises at the end of each chapter section. In fact it is really imperative that the reader work some of these exercises, as the author proves some of the results in the main body of the text via the exercises.
Some of the highlights of the book include: 1. An overview of the probability theory to be used in the book. The material is fairly standard, including a review of continuous and discrete random variables, from the measure-theoretic point of view, i.e the author introduces them via a probability space which is set with its sigma field, and a probability measure on this field. The weight matrix or "profile" as it is sometimes called, is defined, this having many applications in bioinformatics. Bayesian learning is also discussed, and the author introduces what he calls the "missing information principle", and is fundamental to the probabilistic modeling of biological sequences. Applications of probability theory to DNA analysis are discussed, including shotgun assembly and the distribution of fragment lengths from restriction digests.
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"Hidden Markov Models of Bioinformatics" is an excellent exploration of the subject matter: appropriate coverage, well written, and engaging. Hidden Markov Models are a rather broad class of probabilistic models useful for sequential processes. Their use in the modeling and abstraction of motifs in, for example, gene and protein families is a specialization that bears a thorough description, and this book does so very well. This is a book for understanding the theory and core ideas underlying profile HMMs, and if the term Expectation Maximization doesn't sound familiar or interesting to you, this is probably not the book you're looking for. Personally I found it clearer in some ways than the standard reference by Durbin, Eddy, Krogh, and Mitchison, but actually the two complement each other very nicely. If you are interested in constructing an HMM for your favorite protein family you probably want to look at the HMMER or SAM documentation instead; if you want to understand where HMMs come from or how you might architect one, there's probably no better book than this one.
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