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.
Rezensionen / Stimmen
"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
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Technology
Zielgruppe
Für Beruf und Forschung
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.
Produkt-Hinweis
Maße
Höhe: 243 mm
Breite: 197 mm
Dicke: 29 mm
Gewicht
ISBN-13
978-0-12-370476-4 (9780123704764)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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).
Autor*in
Professor and Chair of Computer Science, Northeastern Illinois University, Chicago, USA
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
