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Series Foreword
Preface
1. Introduction
Biological data in digital symbol sequences
Genomes--diversity, size, & structure
Proteins & proteomes
On the information content of biological sequences
Prediction of molecular function & structure
2. Machine-Learning Foundations: The Probabilistic
Framework
Introduction: Bayesian modeling
The Cox Jaynes axioms
Bayesian inference & induction
Model structures: graphical models & other tricks
Summary
3. Probabilistic Modeling & Inference: Examples
The simplest sequence models
Statistical mechanics
4. Machine Learning Algorithms
Introduction
Dynamic programming
Gradient descent
EM/GEM algorithms
Markov-chain Monte-Carlo methods
Simulated annealing
Evolutionary & genetic algorithms
Learning algorithms: miscellaneous aspects
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5.
Neural Networks: The Theory
Introduction
Universal approximation properties
Priors & likelihoods
Learning algorithms: backpropagation
6. Neural Networks: Applications
Sequence encoding & output interpretation
Sequence correlations & neural networks
Prediction of protein secondary structure
Prediction of signal peptides & their cleavage sites
Applications for DNA & RNA nucleotide sequences
Prediction performance evaluation
Different performance measures
7. Hidden Markov Models: The Theory
Introduction
Prior information & initialization
Likelihood & basic algorithms
Learning algorithms
Applications of HMMs: general aspects
8. Hidden Markov Models: Applications
Protein applications
DNA & RNA applications
Advantages & limitations of HMMs
9. Probabilistic Graphical Models in Bioinformatics
The zoo of graphical models in bioinformatics Markov models & DNA
symmetries
Markov models & gene finders
Hybrid models & neural network parameterization of graphical models
The single-model case
Bidirectional recurrent neural networks for protein secondary structure
prediction
9.
10. Probabilistic Models of Evolution: Phylogenetic Trees
Introduction to probabilistic models of evolution
Substitution probabilities & evolutionary rates
Rates of evolution
Data likelihood
Optimal trees & learning
Parsimony
Extensions
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11. Stochastic Grammars & Linguistics Introduction to
formal grammars
Formal grammars & the Chomsky hierarchy
Applications of grammars to biological sequences
Prior information & initialization
Likelihood
Learning algorithms
Applications of SCFGs
Experiments
Future directions
12. Microarrays & Gene Expression Introduction to microarray
data
Probabilistic modeling of array data
Clustering
Gene Regulation
13. Internet Resources & Public Databases
A rapidly changing set of resources Databases over databases and tools
Databases over databases in molecular biology
Sequence & structure databases
Sequence similarity searches
Alignment
Selected prediction servers
Molecular biology software links
Ph.D. courses over the Internet
Bioinformatics societies
HMM/NN simulator
A. Statistics
Decision theory & loss functions
Quadratic loss functions
The bias/variance trade-off
Combining estimators
Error bars
Sufficient statistics
Exponential family
Additional useful distributions
Variational methods
B. Information Theory, Entropy, & Relative
Entropy
Entropy
Relative Entropy
Mutual Information
Jensen's Inequality
Maximum Entropy
Minimum Relative Entropy
C. Probabilistic Graphical Models
Notation & preliminaries
The undirected case: Markov random fields
The directed case: Bayesian networks
D. HMM Technicalities, Scaling, Periodic Architectures,
State Functions, and Dirichlet Mixtures
Scaling
Periodic architectures
State functions: bendability
Dirichlet mixtures
E. Gaussian Processes, Kernel Methods, and Support
Vector Machines
Gaussian process models
Kernel methods & support vector machines
Theorems for Gaussian processes & SVMs
F. Symbols and Abbreviations
References
Index |
