The Bayesian network is one of the most important architectures for representing and
reasoning with multivariate probability distributions. When used in conjunction with …

Bayesian methods are valuable, inter alia, whenever there is a need to extract information
from data that are uncertain or subject to any kind of error or noise (including measurement …

Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for
researchers and students in statistics, machine learning, and the biological sciences. The …

Bayesian networks are a combination of probability theory and graph theory. Graph theory
provides a framework to represent complex structures of highly-interacting sets of variables …

Bayesian networks (BNs) provide a neat and compact representation for expressing joint
probability distributions (JPDs) and for inference. They are becoming increasingly important …

A guide to machine learning approaches and their application to the analysis of biological
data. An unprecedented wealth of data is being generated by genome sequencing projects …

The field of high-throughput genetic experimentation is evolving rapidly, with the advent of
new technologies and new venues for data mining. Bayesian methods play a role central to …

This volume discusses the development and application of Bayesian statistical methods for
the analysis of high-throughput bioinformatics data arising from problems in molecular and …

Bayesian networks are emerging into the genomic arena as a general modeling tool able to
unravel the cellular mechanism, to identify genotypes that confer susceptibility to disease …

The interdisciplinary nature of bioinformatics presents a research challenge in integrating
concepts, methods, software and multiplatform data. Although there have been rapid …