Modelling Gene Expression during Ontogenetic Differentiation

by Lundell, Simon

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.