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Title: Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning) by Pierre Baldi, Søren Brunak ISBN: 0-262-02506-X Publisher: MIT Press Pub. Date: 01 August, 2001 Format: Hardcover Volumes: 1 List Price(USD): $60.00

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Average Customer Rating: 4.13 (15 reviews)

Rating: 3
Summary: Could have been a great one.
Comment: This book is decidedly a mix: some very good information, combined with some very puzzling omissions and uneven editing.

First, the good. The description of stochastic context free grammars is the best I've seen. I don't know any other reference that even hint at how to use generative grammars to evaluate likelihoods. Once they caught my interest, though, the authors did not carry through with training and evaluation algorithms I could really use. I suspect that parts of the information are there, but I'll have to go back over their opaque notation again to work out just what they've given and just what's been left out.

This same pattern - an interesting introduction with missing or mysterious development - recurs throughout the book. The discussion on clustering and phylogeny goes the same way: a number of techniques are mentioned but not developed. The authors mention a tree drawing problem, not just building the tree's topology, but ordering the branches for the most informative rendering. Again, a critical topic and one that most authors miss - in the end, these authors miss it, too, by mentioning but not filling in the idea.

Their discussion of neural nets suffers badly from the authors' partial presentation. Evaluation of network output for a given input is relatively straightforward, and they present it in some detail. Training the net is the real problem, though, and is given less than a page.

Baldi and Brunak give more of the fundamentals than most authors. For example, they explain the maximum entropy principle well enough that I'll use it in lots of other areas. They give some coverage to topics of intermediate complexity, such as the forward and backward algorithms for HMM training. Finally, they fizzle out at the higher levels of complexity - the Baum-Welch algorithm could have followed from the forward and backward methods, but is left as a reference to another book.

There is some good here, especially in the fundamentals behind important techniques. The discussions I wanted - the more avanced topics, in forms I can use - are often weak, missing, or impenetrable. Just a bit more work, clearly within the authors' capability, would have made this a landmark reference.

Rating: 5
Summary: Great book if you have the necessary background
Comment: Their bayesian presentation of machine learning algorithms can be hard to follow at times, but the authors cover a large amount of very current practical and theoretical material. One of the the book's unique features is it's broad scope. The authors discuss neural networks, hidden markov models, clustering, gaussian processes and support vector machines. The bibliography contains some of the most useful references for those wishing to implement bioinformatics algorithms. The fast pace may leave some wanting more complete explanations.
You should disregard the claim that this book could be used by those unfamiliar with either molecular biology or computer science. To really make the most of this book, you should be comfortable with the material in Pattern Classification (Duda, Hart and Stork), Biological Sequence Analysis (Durbin, Eddy, Krogh, and Mitchison), and Molecular Biology of the Cell (Alberts et al). That said, this is the best bioinformatics book on the market.

Rating: 5
Summary: An excellent book.
Comment: Very well written, clear, and self-contained. The authors provide a masterly treatment of machine learning methods (neural networks, hidden markov models, etc.) and their applications to fundamental problems in sequence analyis and biology. The book goes all the way from first principles to advanced research topics and should be valuable for both students and researchers. Second edition has many new topics, including DNA microarrays. Requires some concentration but mathematical details are summarized in the appendices. I strongly recommend it for anyone with an interest in bioinformatics and/or machine learning.

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