oru.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Developing and Testing methods for microarray data analysis Using an Artificial life framework
Institute of Medical Biometry and Statistics, Lübeck, Germany.ORCID iD: 0000-0002-7173-5579
Institute for Neuro- and Bioinformatics, Lübeck, Germany.
2003 (English)In: Advances in artificial life: 7th European Conference, ECAL 2003 Dortmund, Germany, September 14-17, 2003 Proceedings / [ed] Banzhaf, W, Christaller, T, Dittrich, P, Kim, JT, Ziegler, J, Berlin, Germany, 2003, Vol. 2801, p. 686-695Conference paper, Published paper (Refereed)
Abstract [en]

Microarray technology has resulted in large sets of gene expression data. Using these data to derive knowledge about the underlying mechanisms that control gene expression dynamics has become an important challenge. Adequate models of the fundamental principles of gene regulation, such as Artificial Life models of regulatory networks, are pivotal for progress in this area. In this contribution, we present a framework for simulating microarray gene expression experiments. Within this framework, artificial regulatory networks with a simple regulon structure are generated. Simulated expression profiles are obtained from these networks under a series of different environmental conditions. The expression profiles show a complex diversity. Consequently, success in using hierarchical clustering to detect groups of genes which form a regulon proves to depend strongly on the method which is used to quantify similarity between expression profiles. When measurements are noisy, even clusters of identically regulated genes are surprisingly difficult to detect. Finally, we suggest cluster support, a method based on overlaying multiple clustering trees, to find out which clusters in a tree are biologically significant.

Place, publisher, year, edition, pages
Berlin, Germany, 2003. Vol. 2801, p. 686-695
Series
Lecture Notes in Computer Science ; 0302-9743
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:oru:diva-40661DOI: 10.1007/978-3-540-39432-7_74ISI: 000187009400074Scopus ID: 2-s2.0-7444258701ISBN: 3-540-20057-6 (print)OAI: oai:DiVA.org:oru-40661DiVA, id: diva2:778295
Conference
7th European Conference on Artifical Life, Dortmund, Germany,14-17 September, 2003.
Available from: 2015-01-09 Created: 2015-01-09 Last updated: 2018-01-30Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Repsilber, Dirk

Search in DiVA

By author/editor
Repsilber, Dirk
Bioinformatics and Systems Biology

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 59 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf