Gene expression rule discovery with a multi-objective neural-genetic hybrid

Recent advances in microarray technology allow an unprecedented view of the biochemical mechanisms contained within a cell. Deriving useful information from the data is still proving to be a difficult task. In this paper a novel method based on a multi-objective genetic algorithm that discovers relevant sets of genes and uses a neural network to create rules using the evolved genes is described. This hybrid method is shown to work on four well-established gene expression datasets taken from the literature. The results indicate that the approach can return biologically intelligible as well as plausible results. The proposed method requires no pre-filtering or preselection of genes.