Gene expression rule discovery and multi-objective ROC analysis using a neural-genetic hybrid. (1st January 2013)

• Record Type: Journal Article
• Authors: Keedwell, Ed; Narayanan, Ajit
• Is Part Of: International journal of data mining and bioinformatics. Volume 7:Number 4(2013)
• Page Start: 376
• Page End: 396
• Publication Date: 2013-01-01
• Subjects: MOO -- multi-objective optimisation -- gene expression classification -- hybrid methods -- GAs -- genetic algorithms -- neural networks -- microarrays -- ROC analysis; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

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.