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Bioinformatics: The Machine Learning Approach

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Pierre Baldi and Soren 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, ...
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Overview

Pierre Baldi and Soren 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.
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Editorial Reviews

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)

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.

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Product Details

Meet the Author

Pierre Baldi Professor Department Information and Computer Science (ICS), University of California at Irvine ( UCI) Director Institute for Genomics and Bioinformatics (IGB) - UCI

Søren Brunak is Director, Center for Biological Sequence Analysis, 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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