There is an urgent need for tools to unravel the complex interactions and functionalities of genes. As such, there has been much interest in reverse-engineering genetic regulatory networks from time series gene expression data. We use an artificial neural network to model the dynamics of complicated gene networks and to learn their parameters. The positive and negative regulations of genes are defined by a weight matrix, and different genes are allowed to have different decaying time constants. We demonstrate the effectiveness of the method by recreating the SOS DNA Repair network of Escherichia coli bacterium, previously discovered through experimental data.
X. Hu et al., "A General Recurrent Neural Network Approach to Model Genetic Regulatory Networks," Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS (2005, Shanghai, China), pp. 4735-4738, Institute of Electrical and Electronics Engineers (IEEE), Jan 2005.
The definitive version is available at http://dx.doi.org/10.1109/IEMBS.2005.1615529
27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 (2005: Sep. 1-4, Shanghai, China)
Electrical and Computer Engineering
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