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Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning) (Hardcover)
by Pierre Baldi (Author), Søren Brunak (Author) 
3.7 out of 5 stars 
See all reviews (16 customer reviews)
Editorial Reviews
Review
"This is a very good book, written with a high level of erudition and insight." -- Gustavo A. Stolovitzky, "Physics Today"
"This is a very good book, written with a high level of erudition and insight."
— Gustavo A. Stolovitzky, Physics Today 
Product Description
An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible. In this book Pierre Baldi and Søren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology. This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.
Contents
Series Foreword ix
Preface xi
1 Introduction 1
1.1 Biological Data in Digital Symbol Sequences 1
1.2 Genomes—Diversity, Size, and Structure 7
1.3 Proteins and Proteomes 16
1.4 On the Information Content of Biological Sequences 24
1.5 Prediction of Molecular Function and Structure 43
2 Machine-Learning Foundations: The Probabilistic Framework 47
2.1 Introduction: Bayesian Modeling 47
2.2 The Cox Jaynes Axioms 50
2.3 Bayesian Inference and Induction 53
2.4 Model Structures: Graphical Models and Other Tricks 60
2.5 Summary 64
3 Probabilistic Modeling and Inference: Examples 67
3.1 The Simplest Sequence Models 67
3.2 Statistical Mechanics 73
4 Machine Learning Algorithms 81
4.1 Introduction 81
4.2 Dynamic Programming 82
4.3 Gradient Descent 83
4.4 EM/GEM Algorithms 84
4.5 Markov-Chain Monte-Carlo Methods 87
4.6 Simulated Annealing 91
4.7 Evolutionary and Genetic Algorithms 93
4.8 Learning Algorithms: Miscellaneous Aspects 94
v
vi Contents
5 Neural Networks: The Theory 99
5.1 Introduction 99
5.2 Universal Approximation Properties 104
5.3 Priors and Likelihoods 106
5.4 Learning Algorithms: Backpropagation 111
6 Neural Networks: Applications 113
6.1 Sequence Encoding and Output Interpretation 114
6.2 Sequence Correlations and Neural Networks 119
6.3 Prediction of Protein Secondary Structure 120
6.4 Prediction of Signal Peptides and Their Cleavage Sites 133
6.5 Applications for DNA and RNA Nucleotide Sequences 136
6.6 Prediction Performance Evaluation 153
6.7 Different Performance Measures 155
7 Hidden Markov Models: The Theory 165
7.1 Introduction 165
7.2 Prior Information and Initialization 170
7.3 Likelihood and Basic Algorithms 172
7.4 Learning Algorithms 177
7.5 Applications of HMMs: General Aspects 184
8 Hidden Markov Models: Applications 189
8.1 Protein Applications 189
8.2 DNA and RNA Applications 209
8.3 Advantages and Limitations of HMMs 222
9 Probabilistic Graphical Models in Bioinformatics 225
9.1 The Zoo of Graphical Models in Bioinformatics 225
9.2 Markov Models and DNA Symmetries 230
9.3 Markov Models and Gene Finders 234
9.4 Hybrid Models and Neural Network Parameterization of
Graphical Models 239
9.5 The Single-Model Case 241
9.6 Bidirectional Recurrent Neural Networks for Protein Secondary
Structure Prediction 255
10 Probabilistic Models of Evolution: Phylogenetic Trees 265
10.1 Introduction to Probabilistic Models of Evolution 265
10.2 Substitution Probabilities and Evolutionary Rates 267
10.3 Rates of Evolution 269
10.4 Data Likelihood 270
10.5 Optimal Trees and Learning 273
Contents vii
10.6 Parsimony 273
10.7 Extensions 275
11 Stochastic Grammars and Linguistics 277
11.1 Introduction to Formal Grammars 277
11.2 Formal Grammars and the Chomsky Hierarchy 278
11.3 Applications of Grammars to Biological Sequences 284
11.4 Prior Information and Initialization 288
11.5 Likelihood 289
11.6 Learning Algorithms 290
11.7 Applications of SCFGs 292
11.8 Experiments 293
11.9 Future Directions 295
12 Microarrays and Gene Expression 299
12.1 Introduction to Microarray Data 299
12.2 Probabilistic Modeling of Array Data 301
12.3 Clustering 313
12.4 Gene Regulation 320
13 Internet Resources and Public Databases 323
13.1 A Rapidly Changing Set of Resources 323
13.2 Databases over Databases and Tools 324
13.3 Databases over Databases in Molecular Biology 325
13.4 Sequence and Structure Databases 327
13.5 Sequence Similarity Searches 333
13.6 Alignment 335
13.7 Selected Prediction Servers 336
13.8 Molecular Biology Software Links 341
13.9 Ph.D. Courses over the Internet 343
13.10 Bioinformatics Societies 344
13.11 HMM/NN simulator 344
A Statistics 347
A.1 Decision Theory and Loss Functions 347
A.2 Quadratic Loss Functions 348
A.3 The Bias/Variance Trade-off 349
A.4 Combining Estimators 350
A.5 Error Bars 351
A.6 Sufficient Statistics 352
A.7 Exponential Family 352
A.8 Additional Useful Distributions 353
viii Contents
A.9 Variational Methods 354
B Information Theory, Entropy, and Relative Entropy 357
B.1 Entropy 357
B.2 Relative Entropy 359
B.3 Mutual Information 360
B.4 Jensen’s Inequality 361
B.5 Maximum Entropy 361
B.6 Minimum Relative Entropy 362
C Probabilistic Graphical Models 365
C.1 Notation and Preliminaries 365
C.2 The Undirected Case: Markov Random Fields 367
C.3 The Directed Case: Bayesian Networks 369
D HMM Technicalities, Scaling, Periodic Architectures,
State Functions, and Dirichlet Mixtures 375
D.1 Scaling 375
D.2 Periodic Architectures 377
D.3 State Functions: Bendability 380
D.4 Dirichlet Mixtures 382
E Gaussian Processes, Kernel Methods, and Support
Vector Machines 387
E.1 Gaussian Process Models 387
E.2 Kernel Methods and Support Vector Machines 389
E.3 Theorems for Gaussian Processes and SVMs 395
F Symbols and Abbreviations 399
References 409
Index 447 |
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