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Bioinformatics: The Machine Learning Approach (Adaptive Computation and Machine Learning Series) Hardcover – 8 May 1998

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Hardcover, 8 May 1998
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Product details

  • Hardcover: 370 pages
  • Publisher: MIT Press (8 May 1998)
  • Language: English
  • ISBN-10: 026202442X
  • ISBN-13: 978-0262024426
  • Product Dimensions: 3.2 x 18.4 x 24.1 cm
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (4 customer reviews)
  • Amazon Bestsellers Rank: 2,266,289 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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This is a very good book, written with a high level of erudition and insight. -- Gustavo A. Stolovitzky Physics Today --This text refers to an alternate Hardcover edition.

About the Author

Pierre Baldi is Professor of Information and Computer Science and of Biological Chemistry (College of Medicine) and Director of the Institute for Genomics and Bioinformatics at the University of California, Irvine. Soren Brunak is Professor and Director of the Center for Biological Sequence Analysis at the Technical University of Denmark. --This text refers to an alternate Hardcover edition.

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2 of 3 people found the following review helpful By A Customer on 28 July 1999
Format: Hardcover
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.
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By chris on 5 Dec 2013
Format: Hardcover Verified Purchase
Very detailed and pleased to have it in my library. Was going to write a book on this subject until I read this one.
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Format: Hardcover
There was this time when there was a belief that computers were a model for everything and so we went from the idea of analogy of computation and information to believing that in some way this captures reality. This book follows in that path. There is a lot about computational methods for finding patterns that have evolved from biological inspiration such as neural networks and genetic algorithms. The truth is that 4 billion years of "suck it and see" where you have layers of evolving components built on top of each other mean that our attempts at following nature are rather feeble. The other belief is that we can compute to understand anything.

Sadly with biology the easy bit is the maths and the computing, even the model building is not too hard. The bad bit is that your models are always wrong and that reality always has a hundred little tricks to make you think you get an answer when you don't. So for a start they talk about the evolution of the codon usage and what it might mean. Now Andreas Wagner has made a much better job of sorting this out and the ideas presented here have been super-ceded. Then they look at annotation of genomes and propagation of errors. So after 10 years of committees and ontologies we are actually still in the same position except now we have even more data to sort out.

If you want to know about Markov Models then read Durbin et al. where they make a clearer explanation and the other methods such as neural networks made some impact in structural bioinformatics but were in effect throwing a kitchen sink at the problem. If you want to use the latest models for pattern finding in an situation - bioinformatics or not them look at Friedmann, Hastie and Tibshirani and look at the other books on data-mining by Witten. They are much more practically focussed.
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3 of 5 people found the following review helpful By A Customer on 30 July 1999
Format: Hardcover
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.
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 16 reviews
21 of 21 people found the following review helpful
Could have been a great one. 13 Dec 2003
By wiredweird - Published on Amazon.com
Format: Hardcover
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.
35 of 39 people found the following review helpful
A very bad book. A colection of references w/o explanations 19 Sep 2001
By Mark - Published on Amazon.com
Format: Hardcover
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.
10 of 11 people found the following review helpful
Terrible 16 Mar 2006
By David H. Johnson - Published on Amazon.com
Format: Hardcover
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.
6 of 7 people found the following review helpful
the worst book I have ever read 6 Nov 2005
By supercutepig - Published on Amazon.com
Format: Hardcover
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.
18 of 26 people found the following review helpful
thumbs down 13 July 1999
By A Customer - Published on Amazon.com
Format: Hardcover
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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