Probabilistic Methods for Bioinformatics

With an Introduction to Bayesian Networks
Erscheinungsjahr 2009
ISBN: 978-0-12-370476-4
Verlag: Elsevier Science

Buch, Englisch, 424 Seiten, Format (B × H): 197 mm x 243 mm, Gewicht: 897 g

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.

Zielgruppe

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.

Autoren/Hrsg.

Neapolitan, Richard E

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

I: Background

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

Bibliography

Index

Über Autor(innen)

Neapolitan, Richard E
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).