Title: Gene expression rule discovery and multi-objective ROC analysis using a neural-genetic hybrid

Authors: Ed Keedwell; Ajit Narayanan

Addresses: College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, North Park Road, Exeter EX4 4QF, UK ' School of Computing and Mathematical Sciences, Auckland University of Technology, AUT Tower, 2-14 Wakefield St, Auckland 1142, New Zealand

Abstract: Microarray data allows an unprecedented view of the biochemical mechanisms contained within a cell although 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 is investigated that evolves a near-optimal trade-off between Artificial Neural Network (ANN) classifier accuracy (sensitivity and specificity) and size (number of genes). This hybrid method is shown to work on four well-established gene expression data sets taken from the literature. The results provide evidence for the rule discovery ability of the hybrid method and indicate that the approach can return biologically intelligible as well as plausible results and requires no pre-filtering or pre-selection of genes.

Keywords: MOO; multi-objective optimisation; gene expression classification; hybrid methods; GAs; genetic algorithms; artificial neural networks; ANNs; microarrays; ROC analysis; bioinformatics.

DOI: 10.1504/IJDMB.2013.054225

International Journal of Data Mining and Bioinformatics, 2013 Vol.7 No.4, pp.376 - 396

Received: 28 Jun 2011
Accepted: 30 Jun 2011
Published online: 20 Oct 2014 *

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