Open AccessAnt colony optimisation of decision tree and contingency table models for the discovery of gene–gene interactions
Author(s) -
Sapin Emmanuel,
Keedwell Ed,
Frayling Tim
Publication year - 2015
Publication title -
iet systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.367
H-Index - 50
eISSN - 1751-8857
pISSN - 1751-8849
DOI - 10.1049/iet-syb.2015.0017
Subject(s) - contingency table , computer science , computational biology , decision tree , table (database) , tree (set theory) , single nucleotide polymorphism , data mining , artificial intelligence , biology , gene , machine learning , mathematics , genetics , genotype , mathematical analysis
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.