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.
Chapter 1: Probabilistic Informatics
Chapter 2: Probability Basics
Chapter 3: Statistics Basics
Chapter 4: Genetics Basics
II: Bayesian Networks
Chapter 5: Foundations of Bayesian Networks
Chapter 6: Further Properties of Bayesian Networks
Chapter 7: Learning Bayesian Network Parameters
Chapter 8: Learning Bayesian Network Structure
III: Bioinformatics Applications
Chapter 9: Nonmolecular Evolutionary Genetics
Chapter 10: Molecular Evolutionary Genetics
Chapter 11: Molecular Phylogenetics
Chapter 12: Analyzing Gene Expression Data
Chapter 13: Genetic Linkage Analysis
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).