Bioinformatics : the machine learning approach

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Bioinformatics : the machine learning approach / Pierre Baldi, Søren Brunak

資料種別:

図書

出版情報:

Cambridge, Mass. : The MIT Press, 1998

形態:

xviii, 351 p., [8] p. of plats ; 24 cm

シリーズ名:

Adaptive computation and machine learning <BA35126370>
Bradford book <BA00245453>

著者名:

ISBN:

9780262024426 [026202442X] (: hc)

書誌ID:

BA35126100

子書誌情報

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他の版・巻

書誌詳細

注記:

Includes bibliographical references(p. 319-346) and index

主題:

Machine learning; Markov processes; Molecular biology--Computer simulation; Molecular biology--Mathematical models; Neural networks (Computer science)

言語:

英語

目次情報:

Series Foreword

Preface

Introduction / 1：

Biological Data in Digital Symbol Sequences / 1.1：

Genomes--Diversity, Size, and Structure / 1.2：

Proteins and Proteomes / 1.3：

On the Information Content of Biological Sequences / 1.4：

Prediction of Molecular Function and Structure / 1.5：

Machine Learning Foundations: The Probabilistic Framework / 2：

Introduction: Bayesian Modeling / 2.1：

The Cox-Jaynes Axioms / 2.2：

Bayesian Inference and Induction / 2.3：

Model Structures: Graphical Models and Other Tricks / 2.4：

Summary / 2.5：

Probabilistic Modeling and Inference: Examples / 3：

The Simplest Sequence Models / 3.1：

Statistical Mechanics / 3.2：

Machine Learning Algorithms / 4：

Dynamic Programming / 4.1：

Gradient Descent / 4.3：

EM/GEM Algorithms / 4.4：

Markov Chain Monte Carlo Methods / 4.5：

Simulated Annealing / 4.6：

Evolutionary and Genetic Algorithms / 4.7：

Learning Algorithms: Miscellaneous Aspects / 4.8：

Neural Networks: The Theory / 5：

Universal Approximation Properties / 5.1：

Priors and Likelihoods / 5.3：

Learning Algorithms: Backpropagation / 5.4：

Neural Networks: Applications / 6：

Sequence Encoding and Output Interpretation / 6.1：

Prediction of Protein Secondary Structure / 6.2：

Prediction of Signal Peptides and Their Cleavage Sites / 6.3：

Applications for DNA and RNA Nucleotide Sequences / 6.4：

Hidden Markov Models: The Theory / 7：

Prior Information and Initialization / 7.1：

Likelihood and Basic Algorithms / 7.3：

Learning Algorithms / 7.4：

Applications of HMMs: General Aspects / 7.5：

Hidden Markov Models: Applications / 8：

Protein Applications / 8.1：

DNA and RNA Applications / 8.2：

Conclusion: Advantages and Limitations of HMMs / 8.3：

Hybrid Systems: Hidden Markov Models and Neural Networks / 9：

Introduction to Hybrid Models / 9.1：

The Single-Model Case / 9.2：

The Multiple-Model Case / 9.3：

Simulation Results / 9.4：

Probabilistic Models of Evolution: Phylogenetic Trees / 9.5：

Introduction to Probabilistic Models of Evolution / 10.1：

Substitution Probabilities and Evolutionary Rates / 10.2：

Rates of Evolution / 10.3：

Data Likelihood / 10.4：

Optimal Trees and Learning / 10.5：

Parsimony / 10.6：

Extensions / 10.7：

Stochastic Grammars and Linguistics / 11：

Introduction to Formal Grammars / 11.1：

Formal Grammars and the Chomsky Hierarchy / 11.2：

Applications of Grammars to Biological Sequences / 11.3：

Likelihood / 11.4：

Applications of SCFGs / 11.6：

Experiments / 11.8：

Future Directions / 11.9：

Internet Resources and Public Databases / 12：

A Rapidly Changing Set of Resources / 12.1：

Databases over Databases and Tools / 12.2：

Databases over Databases / 12.3：

Databases / 12.4：

Sequence Similarity Searches / 12.5：

Alignment / 12.6：

Selected Prediction Servers / 12.7：

Molecular Biology Software Links / 12.8：

Ph.D. Courses over the Internet / 12.9：

HMM/NN Simulator / 12.10：

Statistics / A：

Decision Theory and Loss Functions / A.1：

Quadratic Loss Functions / A.2：

The Bias/Variance Trade-off / A.3：

Combining Estimators / A.4：

Error Bars / A.5：

Sufficient Statistics / A.6：

Exponential Family / A.7：

Gaussian Process Models / A.8：

Variational Methods / A.9：

Information Theory, Entropy, and Relative Entropy / B：

Entropy / B.1：

Relative Entropy / B.2：

Mutual Information / B.3：

Jensen's Inequality / B.4：

Maximum Entropy / B.5：

Minimum Relative Entropy / B.6：

Probabilistic Graphical Models / C：

Notation and Preliminaries / C.1：

The Undirected Case: Markov Random Fields / C.2：

The Directed Case: Bayesian Networks / C.3：

HMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures / D：

Scaling / D.1：

Periodic Architectures / D.2：

State Functions: Bendability / D.3：

Dirichlet Mixtures / D.4：

List of Main Symbols and Abbreviations / E：

References

Index

Series Foreword

Preface

Introduction / 1：

Biological Data in Digital Symbol Sequences / 1.1：

Genomes--Diversity, Size, and Structure / 1.2：

Proteins and Proteomes / 1.3：

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