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Computational Intelligence in Bioinformatics pp 75–111Cite as

Belief Networks for Bioinformatics

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 94))

Summary

Recent publications illustrate successful applications of belief networks1 (BNs) and related probabilistic networks in the domain of bioinformatics. Examples are the modeling of gene regulation networks [6,14,26], the discovering of metabolic [40,83] and signalling pathways [94], sequence analysis [9, 10], protein structure [16, 28, 76], and linkage analysis [55]. Belief networks are applied broadly in health care and medicine for diagnosis and as a data mining tool [57, 60, 61]. New developments in learning belief networks from heterogeneous data sources [40, 56, 67, 80, 82, 96] show that belief networks are becoming an important tool for dealing with high-throughput data at a large scale, not only at the genetic and biochemical level, but also at the level of systems biology.

In this chapter we introduce belief networks and describe their current use within bioinformatics. The goal of the chapter is to help the reader to understand and apply belief networks in the domain of bioinformatics. To achieve this, we (1) make the reader acquainted with the basic mathematical background of belief networks, (2) introduce algorithms to learn and to query belief networks, (3) describe the current state-of-the-art by discussing several real-world applications in bioinformatics, and (4) discuss (free and commercially) available software tools.

The chapter is organized as follows. We start (in Section 3.1) with introducing the concept of belief networks. Then (in Section 3.2) we present some basic algorithms to infer on belief networks and to learn belief networks from data. Section 3.3 is dedicated to a (non-exhaustive) range of extensions to and variants of the standard belief-network concept. We continue (in Section 3.4) by discussing some techniques and guidelines to construct belief networks from domain knowledge. Section 3.5 reviews some recent applications of belief networks in the domain of bioinformatics. In Section 3.6 we discuss a range of tools that are available for constructing, querying, and learning belief networks. Finally, (in Section 3.7) we provide a brief guide to the literature on belief networks.

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Authors and Affiliations

  1. Bio- and medical Informatics Competence Center (BioMICC), MICC, Universiteit Maastricht, PO Box 616, 6200, MD Maastricht, The Netherlands

    Jeroen H. H. L. M. Donkers & Karl Tuyls

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  1. Jeroen H. H. L. M. Donkers
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  2. Karl Tuyls
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Editors and Affiliations

  1. Department of Neurology Buffalo Neuroimaging Analysis Center The Jacobs Neurological Institute University at Buffalo, The State University of New York, 100 High Street, Buffalo, NY, 14203, USA

    Arpad Kelemen

  2. Centre for Quantifiable Quality of Service in Communication Systems (Q2S), Centre of Excellence Norwegian University of Science and Technology, O.S. Bragstads plass 2E, N-7491, Trondheim, Norway

    Ajith Abraham

  3. School of Information Science and Engineering, Jinan University, Jiwei Road 106, Jinan, 250022, P.R. China

    Yuehui Chen

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Donkers, J.H.H.L.M., Tuyls, K. (2008). Belief Networks for Bioinformatics. In: Kelemen, A., Abraham, A., Chen, Y. (eds) Computational Intelligence in Bioinformatics. Studies in Computational Intelligence, vol 94. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76803-6_3

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