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 |