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Foundations of Biomedical Knowledge Representation pp 159–178Cite as

Chain Graphs and Gene Networks

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9521))

Abstract

Chain graphs are graphs with possibly directed and undirected edges, and no semidirected cycle. They have been extensively studied as a formalism to represent probabilistic independence models, because they can model symmetric and asymmetric relationships between random variables. This allows chain graphs to represent a wider range of systems than Bayesian networks. This in turn allows for a more correct representation of systems that may contain both causal and non-causal relationships between its variables, like for example biological systems. In this chapter we give an overview of how to use chain graphs and what research exists on them today. We also give examples on how chain graphs can be used to model advanced systems, that are not well understood, such as gene networks.

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Acknowledgements

This work is funded by the Center for Industrial Information Technology (CENIIT) and a so-called career contract at Linköping University, and by the Swedish Research Council (ref. 2010-4808).

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Authors and Affiliations

  1. Department of Computer and Information Science, Linköping University, Linköping, Sweden

    Dag Sonntag & Jose M. Peña

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  1. Dag Sonntag
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  2. Jose M. Peña
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Correspondence to Dag Sonntag .

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Editors and Affiliations

  1. Inst. for Computing and Inf. Sciences, Radboud University, Nijmegen, The Netherlands

    Arjen Hommersom

  2. Inst. for Computing and Inf. Sciences, Radboud University, Nijmegen, Gelderland, The Netherlands

    Peter J.F. Lucas

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Sonntag, D., Peña, J.M. (2015). Chain Graphs and Gene Networks. In: Hommersom, A., Lucas, P. (eds) Foundations of Biomedical Knowledge Representation. Lecture Notes in Computer Science(), vol 9521. Springer, Cham. https://doi.org/10.1007/978-3-319-28007-3_10

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  • DOI: https://doi.org/10.1007/978-3-319-28007-3_10

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  • Online ISBN: 978-3-319-28007-3

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