Probabilistic Methods for Bioinformatics - 1st Edition - ISBN: 9780123704764, 9780080919362

Probabilistic Methods for Bioinformatics

1st Edition

with an Introduction to Bayesian Networks

Authors: Richard E. Neapolitan
eBook ISBN: 9780080919362
Hardcover ISBN: 9780123704764
Imprint: Morgan Kaufmann
Published Date: 3rd April 2009
Page Count: 424
Tax/VAT will be calculated at check-out
File Compatibility per Device

PDF, EPUB, VSB (Vital Source):
PC, Apple Mac, iPhone, iPad, Android mobile devices.

Amazon Kindle eReader.

Institutional Access

Secure Checkout

Personal information is secured with SSL technology.

Free Shipping

Free global shipping
No minimum order.


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.


This book is for all R&D professionals and students who are involved with industrial informatics, that is, applying the methodologies of computer science and engineering to biological information. This includes Computer Science and other professionals in the data management and data mining field whose interests are bioinformatics in general, and who want to apply AI and probabilistic methods to their problems--in order to better make predictions about the data. For instance, suppose you have long homologous DNA sequences from the human, the chimpanzee, the gorilla, the orangutan, and the rhesus monkey. One can use the methologies from informatics to obtain new information about which species is most closely related to the human.

Table of Contents


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




No. of pages:
© Morgan Kaufmann 2009
Morgan Kaufmann
eBook ISBN:
Hardcover ISBN:

About the Author

Richard E. Neapolitan

Richard E. Neapolitan is professor and Chair of Computer Science at Northeastern Illinois University. He has previously written four books including the seminal 1990 Bayesian network text Probabilistic Reasoning in Expert Systems. More recently, he wrote the 2004 text Learning Bayesian Networks, the textbook Foundations of Algorithms, which has been translated to three languages and is one of the most widely-used algorithms texts world-wide, and the 2007 text Probabilistic Methods for Financial and Marketing Informatics (Morgan Kaufmann Publishers).

Affiliations and Expertise

Northeastern Illinois University, Chicago, USA


"This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics…probabilistic methods used for biological information and Bayesian networks are explained in an accessible way using applications and case studies."--Zentralblatt MATH 1284-1