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Bioinformatics: The Machine Learning Approach (Adaptive Computation and Machine Learning Series) Hardcover – 10 Aug 2001

4.5 out of 5 stars 4 customer reviews

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Product details

  • Hardcover: 400 pages
  • Publisher: MIT Press; 2nd Revised edition edition (10 Aug. 2001)
  • Language: English
  • ISBN-10: 026202506X
  • ISBN-13: 978-0262025065
  • Product Dimensions: 17.8 x 3.2 x 22.9 cm
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (4 customer reviews)
  • Amazon Bestsellers Rank: 628,344 in Books (See Top 100 in Books)
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Review

-- Gustavo A. Stolovitzky, Physics Today

" This is a very good book, written with a high level of erudition and insight." -- Gustavo A. Stolovitzky, Physics Today

& quot; This is a very good book, written with a high level of erudition and insight.& quot; -- Gustavo A. Stolovitzky, Physics Today

"This is a very good book, written with a high level of erudition and insight."-- Gustavo A. Stolovitzky, "Physics Today"

"This is a very good book, written with a high level of erudition and insight." Gustavo A. Stolovitzky Physics Today

This is a very good book, written with a high level of erudition and insight.--Gustavo A. Stolovitzky "Physics Today "

This is a very good book, written with a high level of erudition and insight.

--Gustavo A. Stolovitzky "Physics Today "

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.

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Top Customer Reviews

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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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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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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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) (May include reviews from Early Reviewer Rewards Program)

Amazon.com: 3.7 out of 5 stars 16 reviews
21 of 21 people found the following review helpful
3.0 out of 5 stars 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.
7 of 14 people found the following review helpful
5.0 out of 5 stars Great book 30 July 1999
By A Customer - Published on Amazon.com
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.
10 of 17 people found the following review helpful
5.0 out of 5 stars A must-have 28 July 1999
By A Customer - Published on Amazon.com
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.
17 of 25 people found the following review helpful
5.0 out of 5 stars A first-rate treatment of computational bioinformatics 15 Sept. 1999
By A Customer - Published on Amazon.com
Format: Hardcover
"Bioinformatics", by Baldi and Brunak, is a very well-written treatment of current stochastic algorithmics of genomics and proteomics. It is profitable reading for both the computer scientist learning relevant biology and the computational biologist learning relevant computer science. It probably favours the biologist slightly in this regard, as witnessed by my own enthusiasm for this work. Of particular value are the chapters on hidden markov processes and stochastic grammars. The treatment builds smoothly from early chapters on Bayesian fundamentals in chapter 2, to markov chain monte carlo processes in chapter 3, followed by theory and applications of neural networks, three chapters on hidden markov processes (a fascinating and vital field in modern genomics) and lastly an introductory chapter to the equally important area of stochastic grammars. Other appreciated features include: an up-to-date 452-reference bibliography; a comprehensive survey of web-based resources re both genomic databases and available search engines for DNA, RNA and protein sequence-patterns; in the appendices, there are concise definitional reviews re the coupling of information theory with entropy and aspects of HMM's.Lastly, the price is right, as is most often the case with books from MIT Press.
The above authors have succeeded well in illuminating a large piece of a very large (and growing) object: the landscape of modern informational biology. They of course cannot cover it all. Another recent book (1997) that complements this book's particular focus is that of Setubal and Meidanis ("Introduction to Computational Molecular Biology"). These authors offer a greater emphasis on string and graph theoretic approaches to sequencing algorithms and deal more directly with various heuristic approaches to fragment assembly and hybridization mapping.
39 of 43 people found the following review helpful
1.0 out of 5 stars A very bad book. A colection of references w/o explanations 19 Sept. 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.
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