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Simulations of simple artificial genetic networks reveal features in the use of Relevance Networks
University of Skövde, School of Humanities and Informatics.
University of Skövde, School of Humanities and Informatics.
2005 (English)In: In Silico Biology, ISSN 1386-6338, Vol. 5, no 3, 239-249 p.Article in journal (Refereed) Published
Abstract [en]

Recent research on large scale microarray analysis has explored the use of Relevance Networks to find networks of genes that are associated to each other in gene expression data. In this work, we compare Relevance Networks with other types of clustering methods to test some of the stated advantages of this method. The dataset we used consists of artificial time series of Boolean gene expression values, with the aim of mimicking microarray data, generated from simple artificial genetic networks. By using this dataset, we could not confirm that Relevance Networks based on mutual information perform better than Relevance Networks based on Pearson correlation, partitional clustering or hierarchical clustering, since the results from all methods were very similar. However, all three methods successfully revealed the subsets of co-expressed genes, which is a valuable step in identifying co-regulation.

Place, publisher, year, edition, pages
IOS Press, 2005. Vol. 5, no 3, 239-249 p.
Identifiers
URN: urn:nbn:se:his:diva-1696ISI: 2-s2.0-23744489846OAI: oai:DiVA.org:his-1696DiVA: diva2:31972
Available from: 2007-08-13 Created: 2007-08-13 Last updated: 2013-04-09Bibliographically approved

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http://www.bioinfo.de/isb/2004050023/http://iospress.metapress.com/content/2paux69ha13pfd3w/?genre=article&issn=1386-6338&volume=5&issue=3&spage=239

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Lindlöf, AngelicaLubovac, Zelmina
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Citation style
  • apa
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