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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:his-590 |
| Date | January 2001 |
| Creators | Lundell, Simon |
| Publisher | Högskolan i Skövde, Institutionen för datavetenskap, Skövde : Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/postscript, application/pdf |
| Rights | info:eu-repo/semantics/openAccess, info:eu-repo/semantics/openAccess |
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