his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
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
  • text
  • asciidoc
  • rtf
Deriving Genetic Networks from Gene Expression Data and Prior Knowledge
University of Skövde, Department of Computer Science.
2001 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesis
Abstract [en]

In this work three different approaches for deriving genetic association networks were tested. The three approaches were Pearson correlation, an algorithm based on the Boolean network approach and prior knowledge. Pearson correlation and the algorithm based on the Boolean network approach derived associations from gene expression data. In the third approach, prior knowledge from a known genetic network of a related organism was used to derive associations for the target organism, by using homolog matching and mapping the known genetic network to the related organism. The results indicate that the Pearson correlation approach gave the best results, but the prior knowledge approach seems to be the one most worth pursuing

Place, publisher, year, edition, pages
Skövde: Institutionen för datavetenskap , 2001. , 86 p.
Keyword [en]
Genetic networks, Homology, Gene expression data, Correlation measurement, Boolean network
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:his:diva-589OAI: oai:DiVA.org:his-589DiVA: diva2:2976
Presentation
(English)
Uppsok
Life Earth Science
Supervisors
Examiners
Available from: 2008-01-25 Created: 2008-01-25 Last updated: 2009-11-18

Open Access in DiVA

fulltext(5047 kB)159 downloads
File information
File name FULLTEXT01.psFile size 5047 kBChecksum MD5
093e59613c126f104e73ed06885a2f2d41faf660dfd52c95abe5e30db897e3364daab2b4
Type fulltextMimetype application/postscript
fulltext(865 kB)173 downloads
File information
File name FULLTEXT02.pdfFile size 865 kBChecksum SHA-512
0b9878eb5240d6166fe3d9410ff32890411a30463f74440f2dfe5c4f65149ee7e20679a91002116bf123042f7fce49fde6ada47cd62cc6d21095c61415736963
Type fulltextMimetype application/pdf

By organisation
Department of Computer Science
Bioinformatics and Systems Biology

Search outside of DiVA

GoogleGoogle Scholar
Total: 332 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 150 hits
CiteExportLink to record
Permanent link

Direct link
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
  • text
  • asciidoc
  • rtf