The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics. This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics.
Rather than getting bogged down in proofs and algorithms, probabilistic methods used for biological information and Bayesian networks are explained in an accessible way using applications and case studies. The many useful applications of Bayesian networks that have been developed in the past 10 years are discussed. Forming a review of all the significant work in the field that will arguably become the most prevalent method in biological data analysis.
Key Features
- Unique coverage of probabilistic reasoning methods applied to bioinformatics data--those methods that are likely to become the standard analysis tools for bioinformatics.
- Shares insights about when and why probabilistic methods can and cannot be used effectively;
- Complete review of Bayesian networks and probabilistic methods with a practical approach.
Preface
About the Author
I: Background
Chapter 1: Probabilistic Informatics
1.1 What Is Informatics?
1.2 Bioinformatics
1.3 Probabilistic Informatics
1.4 Outline of This Book
Chapter 2: Probability Basics
2.1 Probability Basics
2.2 Random Variables
2.3 The Meaning of Probability
2.4 Random Variables in Applications
Chapter 3: Statistics Basics
3.1 Basic Concepts
3.2 Markov Chain Monte Carlo
3.3 The Normal Distribution
Chapter 4: Genetics Basics
4.1 Organisms and Cells
4.2 Genes
4.3 Mutations
II: Bayesian Networks
Chapter 5: Foundations of Bayesian Networks
5.1 What Is a Bayesian Network?
5.2 Properties of Bayesian Networks
5.3 Causal Networks as Bayesian Networks
5.4 Inference in Bayesian Networks
5.5 Networks with Continuous Variables
5.6 How Do We Obtain the Probabilities?
Chapter 6: Further Properties of Bayesian Networks
6.1 Entailed Conditional Independencies
6.2 Faithfulness
6.3 Markov Equivalence
6.4 Markov Blankets and Boundaries
Chapter 7: Learning Bayesian Network Parameters
7.1 Learning a Single Parameter
7.2 Learning Parameters in a Bayesian Network
Chapter 8: Learning Bayesian Network Structure
8.1 Model Selection
8.2 Score-Based Structure Learning
8.3 Constraint-Based Structure Learning
8.4 Causal Learning
8.5 Model Averaging
8.6 Approximate Structure Learning
8.7 Software Packages for Learning
III: Bioinformatics Applications
Chapter 9: Nonmolecular Evolutionary Genetics
9.1 No Mutations, Selection, or Genetic Drift
9.2 Natural Selection
9.3 Genetic Drift
9.4 Natural Selection and Genetic Drift
9.5 Rate of Substitution
Chapter 10: Molecular Evolutionary Genetics
10.1 Models of Nucleotide Substitution
10.2 Evolutionary Distance
10.3 Sequence Alignment
Chapter 11: Molecular Phylogenetics
11.1 Phylogenetic Trees
11.2 Distance Matrix Learning Methods
11.3 Maximum Likelihood Method
11.4 Distance Matrix Methods Using ML
Chapter 12: Analyzing Gene Expression Data
12.1 DNA Microarrays
12.2 A Bootstrap Approach
12.3 Model Averaging Approaches
12.4 Module Network Approach
Chapter 13: Genetic Linkage Analysis
13.1 Introduction to Genetic Linkage Analysis
13.2 Genetic Linkage Analysis in Humans
13.3 A Bayesian Network Model
Bibliography
Index
Bayesian Data Analysis, second edition, Gelman et al, Chapman and Hall, 2003, 700 pp, $59.95, 158488388X.
An Introduction to Bioinformatics Algorithms, Jones/Pevzner, MIT Press 2004, $55, 0262101068.
Neaoplitan and Jiang, Probabilistic Methods for Financial and Marketing Informatics 9780123704771
