Book on Bioinformatics

[pfbaldi@netid.com]

The following book is now available from MIT Press:

Bioinformatics: the Machine Learning Approach
Pierre Baldi and Soren Brunak

February 1998 
ISBN 0-262-02442-X 
360 pp., 62 illus., 10 color
$40.00 (cloth)
MIT Press
(800) 625-8569
(617) 253-5249
(617) 258-6894 (FAX)

Additional information can be found at:

http://mitpress.mit.edu/book-home.tcl?isbn=026202442X

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Table of Contents 

Series Foreword 

Preface 

1   Introduction 
1.1 Biological Data in Digital Symbol Sequences 
1.2 Genomes--Diversity, Size, and Structure 
1.3 Proteins and Proteomes 
1.4 On the Information Content of Biological Sequences 
1.5 Prediction of Molecular Function and Structure 

2   Machine Learning Foundations: The Probabilistic Framework 
2.1 Introduction: Bayesian Modeling 
2.2 The Cox-Jaynes Axioms 
2.3 Bayesian Inference and Induction 
2.4 Model Structures: Graphical Models and Other Tricks 
2.5 Summary 

3   Probabilistic Modeling and Inference: Examples 
3.1 The Simplest Sequence Models 
3.2 Statistical Mechanics 

4   Machine Learning Algorithms 
4.1 Introduction 
4.2 Dynamic Programming 
4.3 Gradient Descent 
4.4 EM/GEM Algorithms 
4.5 Markov Chain Monte Carlo Methods 
4.6 Simulated Annealing 
4.7 Evolutionary and Genetic Algorithms 
4.8 Learning Algorithms: Miscellaneous Aspects 

5   Neural Networks: The Theory 
5.1 Introduction 
5.2 Universal Approximation Properties 
5.3 Priors and Likelihoods 
5.4 Learning Algorithms: Backpropagation 

6   Neural Networks: Applications 
6.1 Sequence Encoding and Output Interpretation 
6.2 Prediction of Protein Secondary Structure 
6.3 Prediction of Signal Peptides and Their Cleavage Sites 
6.4 Applications for DNA and RNA Nucleotide Sequences 

7   Hidden Markov Models: The Theory 
7.1 Introduction 
7.2 Prior Information and Initialization 
7.3 Likelihood and Basic Algorithms 
7.4 Learning Algorithms 
7.5 Applications of HMMs: General Aspects 

8   Hidden Markov Models: Applications 
8.1 Protein Applications 
8.2 DNA and RNA Applications 
8.3 Conclusion: Advantages and Limitations of HMMs 

9   Hybrid Systems: Hidden Markov Models and Neural Networks 
9.1 Introduction to Hybrid Models 
9.2 The Single-Model Case 
9.3 The Multiple-Model Case 
9.4 Simulation Results 
9.5 Summary 

10   Probabilistic Models of Evolution: Phylogenetic Trees 
10.1 Introduction to Probabilistic Models of Evolution 
10.2 Substitution Probabilities and Evolutionary Rates 
10.3 Rates of Evolution 
10.4 Data Likelihood 
10.5 Optimal Trees and Learning 
10.6 Parsimony 
10.7 Extensions 

11   Stochastic Grammars and Linguistics 
11.1 Introduction to Formal Grammars 
11.2 Formal Grammars and the Chomsky Hierarchy 
11.3 Applications of Grammars to Biological Sequences 
11.4 Prior Information and Initialization 
11.5 Likelihood 
11.6 Learning Algorithms 
11.7 Applications of SCFGs 
11.8 Experiments 
11.9 Future Directions 

12   Internet Resources and Public Databases 
12.1 A Rapidly Changing Set of Resources 
12.2 Databases over Databases and Tools 
12.3 Databases over Databases 
12.4 Databases 
12.5 Sequence Similarity Searches 
12.6 Alignment 
12.7 Selected Prediction Servers 
12.8 Molecular Biology Software Links 
12.9 Ph.D. Courses over the Internet 
12.10 HMM/NN Simulator 

A   Statistics 
A.1 Decision Theory and Loss Functions 
A.2 Quadratic Loss Functions 
A.3 The Bias/Variance Trade-off 
A.4 Combining Estimators 
A.5 Error Bars 
A.6 Sufficient Statistics 
A.7 Exponential Family 
A.8 Gaussian Process Models 
A.9 Variational Methods 

B   Information Theory, Entropy, and Relative Entropy 
B.1 Entropy 
B.2 Relative Entropy 
B.3 Mutual Information 
B.4 Jensen's Inequality 
B.5 Maximum Entropy 
B.6 Minimum Relative Entropy 

C   Probabilistic Graphical Models 
C.1 Notation and Preliminaries 
C.2 The Undirected Case: Markov Random Fields 
C.3 The Directed Case: Bayesian Networks 

D   HMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures 
D.1 Scaling 
D.2 Periodic Architectures 
D.3 State Functions: Bendability 
D.4 Dirichlet Mixtures 

E   List of Main Symbols and Abbreviations 
References 

Index 

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