Probabilistic Methods for Bioinformatics book cover

Probabilistic Methods for Bioinformatics

with an Introduction to Bayesian Networks

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.

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.

Hardbound, 424 Pages

Published: April 2009

Imprint: Morgan Kaufmann

ISBN: 978-0-12-370476-4


  • ¿Probabilistic Methods for Financial and Marketing Informatics makes important and novel contributions to understanding investment strategies and should be welcomed by practitioners and researchers alike¿¿ --Tony Volpon, Strategist/Chief Economist, CM Capital Markets ¿Richard Neapolitan's new book with Xia Jiang on Bayes nets and its applications to finance and marketing is a must-read for professionals in the finance and marketing communities. The relatively new technique of Bayes nets has great potential for managing uncertainty in all domains. But applications of this technique in finance and marketing have not been widely reported to date. This new book fills this gaping void at a level most professionals can easily understand. Congratulations to Neapolitan and Jiang for this excellent addition to the literature on Bayes nets!¿ --Prakash P. Shenoy, University of Kansas


  • 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


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