title: Ant colony optimisation of decision tree and contingency table models for the discovery of gene-gene interactions.
abstract: In this study, ant colony optimisation (ACO) algorithm is used to derive near-optimal interactions between a number of single nucleotide polymorphisms (SNPs). This approach is used to discover small numbers of SNPs that are combined into a decision tree or contingency table model. The ACO algorithm is shown to be very robust as it is proven to be able to find results that are discriminatory from a statistical perspective with logical interactions, decision tree and contingency table models for various numbers of SNPs considered in the interaction. A large number of the SNPs discovered here have been already identified in large genome-wide association studies to be related to type II diabetes in the literature, lending additional confidence to the results.
authors.affiliation: College of Engineering, Mathematics and Physical Sciences, University of Exeter, UK. esapin@gmu.edu.
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Sapin
authors.affiliation: College of Engineering, Mathematics and Physical Sciences, University of Exeter, UK.
authors.forename: Ed
authors.initials: E
authors.lastname: Keedwell
authors.affiliation: Exeter Medical School, University of Exeter, UK.
authors.forename: Tim
authors.initials: T
authors.lastname: Frayling
mesh_terms: D000465:Algorithms; D000818:Animals; D001000:Ants; D003661:Decision Support Techniques; D004843:Epistasis, Genetic; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide