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
Predicting gene expression using artificial neural networks
University of Skövde, Department of Computer Science.
2002 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesis
Abstract [en]

Today one of the greatest aims within the area of bioinformatics is to gain a complete understanding of the functionality of genes and the systems behind gene regulation. Regulatory relationships among genes seem to be of a complex nature since transcriptional control is the result of complex networks interpreting a variety of inputs. It is therefore essential to develop analytical tools detecting complex genetic relationships.

This project examines the possibility of the data mining technique artificial neural network (ANN) detecting regulatory relationships between genes. As an initial step for finding regulatory relationships with the help of ANN the goal of this project is to train an ANN to predict the expression of an individual gene. The genes predicted are the nuclear receptor PPAR-g and the insulin receptor. Predictions of the two target genes respectively were made using different datasets of gene expression data as input for the ANN. The results of the predictions of PPAR-g indicate that it is not possible to predict the expression of PPAR-g under the circumstances for this experiment. The results of the predictions of the insulin receptor indicate that it is not possible to discard using ANN for predicting the gene expression of an individual gene.

Place, publisher, year, edition, pages
Skövde: Institutionen för datavetenskap , 2002. , 76 p.
Keyword [en]
Artificial neural networks gene expression
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:his:diva-707OAI: oai:DiVA.org:his-707DiVA: diva2:3107
Presentation
(English)
Uppsok
Physics, Chemistry, Mathematics
Supervisors
Available from: 2008-02-04 Created: 2008-02-04 Last updated: 2009-11-18

Open Access in DiVA

fulltext(779 kB)524 downloads
File information
File name FULLTEXT01.psFile size 779 kBChecksum SHA-1
6b1100cc6449768685f8d81a586cac30b52f8e54fce603847511f3d3e4a6b0e4f142870e
Type fulltextMimetype application/postscript
fulltext(250 kB)109 downloads
File information
File name FULLTEXT02.pdfFile size 250 kBChecksum SHA-512
3e1c376ea629a93cd8542d158b3d424c79ff15db9e1fb6ac1eb9d0c5b25143fc18144378131080700648427036a79cf6bfa4e0a22cb5ee1ff7a2bcde0ddf62d1
Type fulltextMimetype application/pdf

By organisation
Department of Computer Science
Bioinformatics (Computational Biology)

Search outside of DiVA

GoogleGoogle Scholar
Total: 633 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: 258 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