Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning)

Rating:
Summary: Excellent text both for biologists and computer scientists.
Review: I found the book very readable, and full of information combining the machine learning approach (neural nets and Hidden Markov models) with biological problems. The wealth of specific biological information bridges the background gap for computer scientists and mathematicians, and the organization of topics is excellent.

In the mathematics and computer science community, Baldi is an internationally recognized expert in the fields of neural nets and Hidden Markov models and their applications (for instance, he holds a patent for neural net recognition of fingerprints). Concerning HMM's Baldi and co-workers have found statistical models for protein families, sequence signals for nucleosome centers, etc. Moreover, Baldi, together with Chauvin, has developed a gradient descent parameter update method for HMM's which has no zero probability absorptions, and allows on-line updates, useful features not supported by the standard EM method.

From these and other applications, I found the text very useful.

Rating:
Summary: Great book if you have the necessary background
Review: I just bought this book and am COMPLETEly disappointed with it.
Here is why. The book is badly written, hard to read and follow. Although it is said that this is a book is for " many readers", it is really for those who have already known all the algorithms. It is simply impossible to learn the algorithms from this book. The chapter on neural network is a few pages. It provieds a few equations for backpropagation. That is it! It is pretty much true for every thing else. Equations, hard to understand sentences, abbreviations with no explnantions, tons of citations everywhere. A book should strive to explain, and not to cite what other papers and go look there all the time. I suspect the few good reviews here are from the authors themselves.

I have a good programming background. I also read some papers on neural network and hidden markov models, This book is a lot worse than anything I have read in explaining the stuff. Very disappointed. Save your money and get something else.

Rating:
Summary: Great book
Review: The book of P.Baldi and S.Brunak presents a clear and exhaustive review of the main topics concerning Machine Learning techniques, as well as a broad discussion on the most significant problems that have faced Bioinformatics in recent years together with many hints on the future directions for the ML approach in BI. In the book the description of ML tools (Probabilistic Models, ANNs, HMMs, Hybrid Systems, etc.) unified under the Bayesian framework, is always clear and rigorous. Most of the theoretical materials that are unnecessary for an immediate comprehension -but that some readers may require for a deeper foundation of the ML approach- are presented in the rich appendices, a fair choice to keep the text clear. In any case the specific techniques are described in enough detail, so that any smart reader should be able to implement the models presented without further information. The biological aspects are described at a similar level of detail. As a result the book is very useful both for CS researchers interested in Computational Biology and for Biologists who want to acquire a deeper knowledge of the ML algorithmic tools used for biological data processing. It is obvious that ML plays a broad role in Bioinformatics and that sometimes some of its different aspects seem to be so weakly related that it seems a hard task to systematically review the state of the art of this approach. Anyway, the book of P.Baldi and S.Brunak performs the task successfully and actually represents both the first comprehensive book on ML in Bioinformatics and an incredibly rich pointer to all the resources (books, papers, servers and biological databases on the web) concerning this very promising discipline.

Rating:
Summary: Great book if you have the necessary background
Review: 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:
Summary: A must-have
Review: This book is an excellent source of information for beginning the study of machine learning algorithms applied to biology. Reading the book you get a clear feeling that bioinformatics is not just one of the many application fields of computer science and artificial intelligence, it is perhaps the most challenging set of problems for intelligent algorithms not primarily focused on replicating human intelligence. There is an amazing wealth of open problems, some of which apparently very difficult. No doubt that unless you are already an expert you need an accurate map of this complex territory and the book by Baldi and Brunak is an excellent and up-to-date map that may suggest new exciting ideas for research.

As a computer scientist I can say that the book is sometimes difficult to read if you have no previous knowledge of biology. This is because the authors didn't take the simplificative approach of reducing biological problems to abstract mathematics. Rather, they preserved the full biological flavor of the problems. Although this approach costs you more at the beginning, you can eventually get a more accurate and nontrivial picture of the problems.

My conclusion: it is perhaps unlikely that you can learn about bioinformatics using only this book. However, if you want to learn about bioinformatics, this book is a must-have reference.

Rating:
Summary: Could have been a great one.
Review: 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:
Summary: An excellent book.
Review: 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.