Bioinformatics: The Machine Learning Approach / Edition 2

Bioinformatics: The Machine Learning Approach / Edition 2

by Pierre Baldi, S?ren Brunak
     
 

ISBN-10: 026202506X

ISBN-13: 9780262025065

Pub. Date: 08/01/2001

Publisher: MIT Press

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,

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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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Product Details

ISBN-13:
9780262025065
Publisher:
MIT Press
Publication date:
08/01/2001
Series:
Adaptive Computation and Machine Learning series
Edition description:
second edition
Pages:
476
Sales rank:
1,233,887
Product dimensions:
7.00(w) x 9.00(h) x 1.25(d)
Age Range:
18 Years

Table of Contents

Series Foreword
Preface
1Introduction1
2Machine Learning Foundations: The Probabilistic Framework39
3Probabilistic Modeling and Inference: Examples59
4Machine Learning Algorithms73
5Neural Networks: The Theory91
6Neural Networks: Applications105
7Hidden Markov Models: The Theory143
8Hidden Markov Models: Applications167
9Hybrid Systems: Hidden Markov Models and Neural Networks201
10Probabilistic Models of Evolution: Phylogenetic Trees217
11Stochastic Grammars and Linguistics229
12Internet Resources and Public Databases251
A: Statistics271
BInformation Theory, Entropy, and Relative Entropy281
CProbabilistic Graphical Models289
DHMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures299
E: List of Main Symbols and Abbreviations311
References319
Index347

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