Chain Graphs and Gene Networks
2015 (English)In: Foundations of Biomedical Knowledge Representation: Methods and Applications / [ed] Arjen Hommersom and Peter J.F. Lucas, Springer, 2015, 159-178 p.Chapter in book (Refereed)
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.
Place, publisher, year, edition, pages
Springer, 2015. 159-178 p.
, Lecture Notes in Artificial Intelligence, ISSN 0302-9743 ; 9521
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-105813DOI: 10.1007/978-3-319-28007-3_10ISBN: 978-3-319-28006-6 (Print)ISBN: 978-3-319-28007-3 (eBook)OAI: oai:DiVA.org:liu-105813DiVA: diva2:710802
The previous status of this article was Manuscript.2014-04-082014-04-082016-03-29Bibliographically approved