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Modelling Gene Expression during Ontogenetic Differentiation
Högskolan i Skövde, Institutionen för datavetenskap.
2001 (engelsk)Independent thesis Advanced level (degree of Master (One Year))Oppgave
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.

sted, utgiver, år, opplag, sider
Skövde: Institutionen för datavetenskap , 2001. , s. 66
Emneord [en]
Genetic Networks, Neural Networks, Gene Expression analysis
HSV kategori
Identifikatorer
URN: urn:nbn:se:his:diva-590OAI: oai:DiVA.org:his-590DiVA, id: diva2:2978
Presentation
(engelsk)
Uppsök
Life Earth Science
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Tilgjengelig fra: 2008-01-25 Laget: 2008-01-25 Sist oppdatert: 2009-11-25

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