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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.
- 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.
About the Author
Chapter 1: Probabilistic Informatics
1.1 What Is Informatics?
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
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
6.3 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
- 3rd April 2009
- Morgan Kaufmann
- Hardcover ISBN:
- eBook ISBN:
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).
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