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Integrating Prior Knowledge into the Fitness Function of an Evolutionary Algorithm for Deriving Gene Regulatory Networks
University of Skövde, School of Humanities and Informatics.
2006 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The topic of gene regulation is a major research area in the bioinformatics community. In this thesis prior knowledge from Gene Ontology in the form of templates is integrated into the fitness function of an evolutionary algorithm to predict gene regulatory networks. The resulting multi-objective fitness functions are then tested with MAPK network data taken from KEGG to evaluate their respective performances. The results are presented and analyzed. However, a clear tendency cannot be observed. The results are nevertheless promising and can provide motivation for further research in that direction. Therefore different ideas and approaches are suggested for future work.

Place, publisher, year, edition, pages
Skövde: Institutionen för kommunikation och information , 2006. , 87 p.
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:his:diva-31OAI: oai:DiVA.org:his-31DiVA: diva2:2672
Subject / course
Bioinformatics
Educational program
Bioinformatics - Master’s Programme
Presentation
(English)
Uppsok
Physics, Chemistry, Mathematics
Supervisors
Examiners
Available from: 2006-11-06 Created: 2006-11-06 Last updated: 2014-01-08

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • 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
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  • asciidoc
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