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2013 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Bayesian Networks in R

with Applications in Systems Biology

verfasst von: Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre

Verlag: Springer New York

Buchreihe : Use R!

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Bayesian Networks in R with Applications in Systems Biology is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is also gradually increased across the chapters with exercises and solutions for enhanced understanding for hands-on experimentation of the theory and concepts. The application focuses on systems biology with emphasis on modeling pathways and signaling mechanisms from high-throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regard. Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. It is also expected that the prevalence of publicly available high-throughput biological data sets may encourage the audience to explore investigating novel paradigms using the approaches presented in the book.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
Bayesian networks and their applications to real-world problems lie at the intersection of several fields such as probability and graph theory. In this chapter a brief introduction to the terminology and the basic properties of graphs, with particular attention to directed graphs, is provided. As with other Use R!-series books, a brief introduction to the R environment and basic R programming is also provided. Some background in probability theory and programming is assumed. However, the necessary references are included under the respective sections for a more complete treatment.
Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre
Chapter 2. Bayesian Networks in the Absence of Temporal Information
Abstract
Data recorded across multiple variables of interest for a given phenomenon often do not contain any explicit temporal information. In the absence of such information, the data essentially represent a static snapshot of the underlying phenomenon at a particular moment in time. For this reason, they are sometimes referred to as static data.
Static Bayesian networks, commonly known simply as Bayesian networks, provide an intuitive and comprehensive framework to model the dependencies between the variables in static data. In this chapter, we will introduce the essential definitions and properties of static Bayesian networks. Subsequently, we will discuss existing Bayesian network learning algorithms and illustrate their applications with real-world examples and different R packages.
Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre
Chapter 3. Bayesian Networks in the Presence of Temporal Information
Abstract
Real-world entities comprising a complex system evolve as a function of time and respond to external perturbations. Dynamic Bayesian networks extend the fundamental ideas behind static Bayesian networks to model associations arising from the temporal dynamics between the entities of interest. This has to be contrasted with static Bayesian networks, which model the network structure from multiple independent realizations of the entities of a snapshot of the process. More importantly, incorporating the temporal signatures is useful in capturing possible feedback loops that are implicitly disregarded in the case of static Bayesian networks. Since feedback loops are ubiquitous in biological pathways, dynamic Bayesian network modeling is expected to result in better representations of such pathways.
In this chapter, we will introduce basic definitions and models for modeling associations from multivariate linear time series using dynamic Bayesian networks. Applications include modeling gene networks from expression data. Two broad classes of multivariate time series are considered: those whose statistical properties are invariant as a function of time and those whose properties do show change of time.
Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre
Chapter 4. Bayesian Network Inference Algorithms
Abstract
Chapters 2 and 3 discussed the importance of learning the structure and the parameters of Bayesian networks from observational and interventional data sets. Bayesian inference on the other hand is often a follow-up to Bayesian network learning and deals with inferring the state of a set of variables given the state of others as evidence. Such an approach eliminates the need for additional experiments and is therefore extremely helpful. In this chapter, we will introduce inferential techniques for static and dynamic Bayesian networks and their applications to gene expression profiles.
Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre
Chapter 5. Parallel Computing for Bayesian Networks
Abstract
Most problems in Bayesian network theory have a computational complexity that, in the worst case, scales exponentially with the number of variables. It is polynomial even for sparse networks. Even though newer algorithms are designed to improve scalability, it is unfeasible to analyze data containing more than a few hundreds of variables. Parallel computing provides a way to address this problem by making better use of modern hardware.
In this chapter we will provide a brief overview of the history and the fundamental concepts of parallel computing, and we will examine their applications to Bayesian network learning and inference using the bnlearn package.
Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre
Backmatter
Metadaten
Titel
Bayesian Networks in R
verfasst von
Radhakrishnan Nagarajan
Marco Scutari
Sophie Lèbre
Copyright-Jahr
2013
Verlag
Springer New York
Electronic ISBN
978-1-4614-6446-4
Print ISBN
978-1-4614-6445-7
DOI
https://doi.org/10.1007/978-1-4614-6446-4