An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Søren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.

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.

16 of 16 found the following review helpful:


Could have been a great one.  Dec 13, 2003 By wiredweird "wiredweird"
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.

8 of 9 found the following review helpful:


Terrible  Mar 16, 2006 By David H. Johnson
I'm a graduate student, reading a lot of bioinformatics materials. This is by far the worst text I've read on the subject. Poorly explained, poorly edited. Poor.

4 of 5 found the following review helpful:


the worst book I have ever read  Nov 06, 2005 By supercutepig
Just a collection of formulae, in an unclear way. Once we tried to use it in our seminar of bioinformatics, but after a few chapters we had to give it up for its bad writing. I could not find any reason to buy it or read it.

16 of 23 found the following review helpful:


thumbs down  Jul 13, 1999
This book is abysmally written. It appears to be filled with technically accurate information, but not organized in a form amenable to learning. It is probably not even appropriate for an expert: an expert could probably verify its correctness, but it's not organized appropriately to be used as reference.

I have a PhD in computer science and I'm used to working hard to master technical material without assistance. However, I found it extremely difficult to fight off the feeling that these authors' goal was to parade what they know without actually sharing it.

There's no need to pay extra money for color photographs of the authors' car license plates and a half-breed cat. Buy Durbin, Eddy, Krogh, Mitchison's well-written book instead.

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