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Modelling Gene Expression during Ontogenetic Differentiation
University of Skövde, Department of Computer Science.
2001 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesis
Abstract [en]

Various types of recurrent neural networks have been used as models for the regulatory relationships between genes. The neural network is trained on the data from micro-array techniques, each gene corresponds to a neuron in the network. The data from the micro-array technologies has numerous genes, but usually involves few samples, this makes the network heavily under-determined. In this work we will propose a method that can cope with the poorness of the data. We will use a Hopfield-type neural network to model the ontogenetic differentiation of female honeybees. A method that identifies the genes that determine the castes is proposed.

Place, publisher, year, edition, pages
Skövde: Institutionen för datavetenskap , 2001. , 66 p.
Keyword [en]
Genetic Networks, Neural Networks, Gene Expression analysis
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:his:diva-590OAI: oai:DiVA.org:his-590DiVA: diva2:2978
Presentation
(English)
Uppsok
Life Earth Science
Supervisors
Available from: 2008-01-25 Created: 2008-01-25 Last updated: 2009-11-25

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fulltext(1354 kB)126 downloads
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CiteExportLink to record
Permanent link

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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
  • text
  • asciidoc
  • rtf