Cover

Title

Copyright

Contents

Preface

1 Biological Foundations

1.1 Genetics

1.1.1 Nucleic Acid Structure

1.1.2 Genes

1.1.3 RNA

1.1.4 Transcription

1.1.5 Proteins

1.1.6 Translation

1.1.7 Transcriptional Regulation

1.2 Genomics

1.2.1 Microarray Technology

1.3 Proteomics

Bibliography

2 Deterministic Models of Gene Networks

2.1 Graph Models

2.2 Boolean Networks

2.2.1 Cell Differentiation and Cellular Functional States

2.2.2 Network Properties and Dynamics

2.2.3 Network Inference

2.3 Generalizations of Boolean Networks

2.3.1 Asynchrony

2.3.2 Multivalued Networks

2.4 Differential Equation Models

2.4.1 A Differential Equation Model Incorporating Transcription and Translation

2.4.2 Discretization of the Continuous Differential Equation Model

Bibliography

3 Stochastic Models of Gene Networks

3.1 Bayesian Networks

3.2 Probabilistic Boolean Networks

3.2.1 Definitions

3.2.2 Inference

3.2.3 Dynamics of PBNs

3.2.4 Steady-State Analysis of Instantaneously Random PBNs

3.2.5 Relationships of PBNs to Bayesian Networks

3.2.6 Growing Subnetworks from Seed Genes

3.3 Intervention

3.3.1 Gene Intervention

3.3.2 Structural Intervention

3.3.3 External Control

Bibliography

4 Classification

4.1 Bayes Classifier

4.2 Classification Rules

4.2.1 Consistent Classifier Design

4.2.2 Examples of Classification Rules

4.3 Constrained Classifiers

4.3.1 Shatter Coefficient

4.3.2 VC Dimension

4.4 Linear Classification

4.4.1 Rosenblatt Perceptron

4.4.2 Linear and Quadratic Discriminant Analysis

4.4.3 Linear Discriminants Based on Least-Squares Error

4.4.4 Support Vector Machines

4.4.5 Representation of Design Error for Linear Discriminant Analysis

4.4.6 Distribution of the QDA Sample-Based Discriminant

4.5 Neural Networks Classifiers

4.6 Classification Trees

4.6.1 Classification and Regression Trees

4.6.2 Strongly Consistent Rules for Data-Dependent Partitioning

4.7 Error Estimation

4.7.1 Resubstitution

4.7.2 Cross-validation

4.7.3 Bootstrap

4.7.4 Bolstering

4.7.5 Error Estimator Performance

4.7.6 Feature Set Ranking

4.8 Error Correction

4.9 Robust Classifiers

4.9.1 Optimal Robust Classifiers

4.9.2 Performance Comparison for Robust Classifiers

Bibliography

5 Regularization

5.1 Data Regularization

5.1.1 Regularized Discriminant Analysis

5.1.2 Noise Injection

5.2 Complexity Regularization

5.2.1 Regularization of the Error

5.2.2 Structural Risk Minimization

5.2.3 Empirical Complexity

5.3 Feature Selection

5.3.1 Peaking Phenomenon

5.3.2 Feature Selection Algorithms

5.3.3 Impact of Error Estimation on Feature Selection

5.3.4 Redundancy

5.3.5 Parallel Incremental Feature Selection

5.3.6 Bayesian Variable Selection

5.4 Feature Extraction

Bibliography

6 Clustering

6.1 Examples of Clustering Algorithms

6.1.1 Euclidean Distance Clustering

6.1.2 Self-Organizing Maps

6.1.3 Hierarchical Clustering

6.1.4 Model-Based Cluster Operators

6.2 Cluster Operators

6.2.1 Algorithm Structure

6.2.2 Label Operators

6.2.3 Bayes Clusterer

6.2.4 Distributional Testing of Cluster Operators

6.3 Cluster Validation

6.3.1 External Validation

6.3.2 Internal Validation

6.3.3 Instability Index

6.3.4 Bayes Factor

6.4 Learning Cluster Operators

6.4.1 Empirical-Error Cluster Operator

6.4.2 Nearest-Neighbor Clustering Rule

Bibliography

Index