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# Probabilistic Methods for Bioinformatics

## with an Introduction to Bayesian Networks

- 1st Edition - April 3, 2009
- Author: Richard E. Neapolitan
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 1 6 5 4 6 - 4
- Hardback ISBN:9 7 8 - 0 - 1 2 - 3 7 0 4 7 6 - 4
- eBook ISBN:9 7 8 - 0 - 0 8 - 0 9 1 9 3 6 - 2

The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with sp… Read more

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Request a sales quoteThe 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.

- 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

- No. of pages: 424
- Language: English
- Edition: 1
- Published: April 3, 2009
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9780323165464
- Hardback ISBN: 9780123704764
- eBook ISBN: 9780080919362

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