Bioinformatics (Adaptive Computation and Machine Learning) [Hardcover]

Pierre Baldi (Author), SÃ,ren Brunak (Author)

Book Description

ISBN-10: 026202442X | ISBN-13: 978-0262024426 | Publication Date: February 13, 1998

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 more than ever. Biotechnology, pharmacology, and medicine will be particularly affected by the new results and the increased understanding of life at the molecular level. Bioinformatics is the development and application of computer methods for analysis, interpretation, and prediction, as well as for the design of experiments. It has emerged as a strategic frontier between biology and computer science.

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—and this is exactly the situation in molecular biology. As with its predecessor, statistical model fitting, the goal in machine learning is to extract useful information from a body of data by building good probabilistic models. The particular twist behind machine learning, however, is 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 at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology.

Editorial Reviews

Review

"With this work, Baldi and Brunak have provided a sound foundation for the process of classifying and interconnecting the hierarchy of parts encoded by genomic sequence data and their variability. Not only is the book appropriate for students new to this intersection between computation and biology, it will also prove useful for long-time workers on `classic' problems in computational molecular biology. The book has a continuity from beginning to end that helps a reader to develop an understanding of machine learning techniques and how to apply them to molecular biology... this book is one of four indispensable books for the bioinformatician's library." -- Terry Gaasterland, Nature Biotechnology, March 1999

From the Publisher

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 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. --This text refers to an alternate Hardcover edition.

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Product Details

• Hardcover: 360 pages
• Publisher: The MIT Press (February 13, 1998)
• Language: English
• ISBN-10: 026202442X
• ISBN-13: 978-0262024426
• Product Dimensions: 9.1 x 7.3 x 1.3 inches
• Shipping Weight: 2.2 pounds
• Average Customer Review: 3.7 out of 5 stars  See all reviews (16 customer reviews)
• Amazon Best Sellers Rank: #2,827,821 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

33 of 37 people found the following review helpful:

1.0 out of 5 stars A very bad book. A colection of references w/o explanations, September 19, 2001

By 

Mark "Mark" (Florida,MO USA) - See all my reviews

This review is from: Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning) (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.

16 of 16 people found the following review helpful:

3.0 out of 5 stars Could have been a great one., December 13, 2003

By 

wiredweird "wiredweird" (Earth, or somewhere nearby) - See all my reviews
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This review is from: Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning) (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.

8 of 8 people found the following review helpful:

1.0 out of 5 stars Terrible, March 15, 2006

By 

David H. Johnson (USA) - See all my reviews
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This review is from: Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning) (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.