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Bioinformatics: The Machine Learning Approach / Edition 2

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Overview

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.

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Editorial Reviews

From the Publisher
"This is a very good book, written with a high level of erudition and insight."Gustavo A. Stolovitzky Physics Today
Physics Today - Gustavo A. Stolovitzky
This is a very good book, written with a high level of erudition and insight.
Terry Gaasterland
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.
—( Nature Biotechnology, March 1999)

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Product Details

Meet 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.

Søren Brunak is Professor and Director of the Center for Biological Sequence Analysis at the Technical University of Denmark.

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Table of Contents

Series Foreword
Preface
1 Introduction 1
2 Machine Learning Foundations: The Probabilistic Framework 39
3 Probabilistic Modeling and Inference: Examples 59
4 Machine Learning Algorithms 73
5 Neural Networks: The Theory 91
6 Neural Networks: Applications 105
7 Hidden Markov Models: The Theory 143
8 Hidden Markov Models: Applications 167
9 Hybrid Systems: Hidden Markov Models and Neural Networks 201
10 Probabilistic Models of Evolution: Phylogenetic Trees 217
11 Stochastic Grammars and Linguistics 229
12 Internet Resources and Public Databases 251
A: Statistics 271
B Information Theory, Entropy, and Relative Entropy 281
C Probabilistic Graphical Models 289
D HMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures 299
E: List of Main Symbols and Abbreviations 311
References 319
Index 347
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