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Bioinformatics The Machine Learning Approach

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ISBN-10: 026202442X

ISBN-13: 9780262024426

Edition: 1998

Authors: Pierre Baldi, S�ren Brunak

List price: $52.95
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Description:

In this book, aimed at researchers and students, Baldi and Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data.
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Book details

List price: $52.95
Copyright year: 1998
Publisher: MIT Press
Publication date: 2/13/1998
Binding: Hardcover
Pages: 371
Size: 18.80" wide x 23.90" long x 1.20" tall
Weight: 1.892
Language: English

Series Foreword
Preface
Introduction
Biological Data in Digital Symbol Sequences
Genomes--Diversity, Size, and Structure
Proteins and Proteomes
On the Information Content of Biological Sequences
Prediction of Molecular Function and Structure
Machine Learning Foundations: The Probabilistic Framework
Introduction: Bayesian Modeling
The Cox-Jaynes Axioms
Bayesian Inference and Induction
Model Structures: Graphical Models and Other Tricks
Summary
Probabilistic Modeling and Inference: Examples
The Simplest Sequence Models
Statistical Mechanics
Machine Learning Algorithms
Introduction
Dynamic Programming
Gradient Descent
EM/GEM Algorithms
Markov Chain Monte Carlo Methods
Simulated Annealing
Evolutionary and Genetic Algorithms
Learning Algorithms: Miscellaneous Aspects
Neural Networks: The Theory
Introduction
Universal Approximation Properties
Priors and Likelihoods
Learning Algorithms: Backpropagation
Neural Networks: Applications
Sequence Encoding and Output Interpretation
Prediction of Protein Secondary Structure
Prediction of Signal Peptides and Their Cleavage Sites
Applications for DNA and RNA Nucleotide Sequences
Hidden Markov Models: The Theory
Introduction
Prior Information and Initialization
Likelihood and Basic Algorithms
Learning Algorithms
Applications of HMMs: General Aspects
Hidden Markov Models: Applications
Protein Applications
DNA and RNA Applications
Conclusion: Advantages and Limitations of HMMs
Hybrid Systems: Hidden Markov Models and Neural Networks
Introduction to Hybrid Models
The Single-Model Case
The Multiple-Model Case
Simulation Results
Summary
Probabilistic Models of Evolution: Phylogenetic Trees
Introduction to Probabilistic Models of Evolution
Substitution Probabilities and Evolutionary Rates
Rates of Evolution
Data Likelihood
Optimal Trees and Learning
Parsimony
Extensions
Stochastic Grammars and Linguistics
Introduction to Formal Grammars
Formal Grammars and the Chomsky Hierarchy
Applications of Grammars to Biological Sequences
Prior Information and Initialization
Likelihood
Learning Algorithms
Applications of SCFGs
Experiments
Future Directions
Internet Resources and Public Databases
A Rapidly Changing Set of Resources
Databases over Databases and Tools
Databases over Databases
Databases
Sequence Similarity Searches
Alignment
Selected Prediction Servers
Molecular Biology Software Links
Ph.D. Courses over the Internet
HMM/NN Simulator
Statistics
Decision Theory and Loss Functions
Quadratic Loss Functions
The Bias/Variance Trade-off
Combining Estimators
Error Bars
Sufficient Statistics
Exponential Family
Gaussian Process Models
Variational Methods
Information Theory, Entropy, and Relative Entropy
Entropy
Relative Entropy
Mutual Information
Jensen's Inequality
Maximum Entropy
Minimum Relative Entropy
Probabilistic Graphical Models
Notation and Preliminaries
The Undirected Case: Markov Random Fields
The Directed Case: Bayesian Networks
HMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures
Scaling
Periodic Architectures
State Functions: Bendability
Dirichlet Mixtures
List of Main Symbols and Abbreviations
References
Index