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Published on: August 16, 2017
Ant colony optimisation of decision tree and contingency table models for the discovery of gene-gene interactions
Emmanuel Sapin1, Ed Keedwell2, Tim Frayling3
1College of Engineering, Mathematics and Physical Sciences, University of Exeter, UK. esapin@gmu.edu.
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.
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