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Hidden Markov Models for Bioinformatics (Computational Biology) Paperback – May 1, 2002
Top Customer Reviews
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.
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.
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.Read more ›