Ant Colony Optimization
A proof of convergence for Ant algorithms
Information Sciences—Informatics and Computer Science: An International Journal
Ant Colony Optimization for Genome-Wide Genetic Analysis
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Information Sciences: an International Journal
New heuristics for two bounded-degree spanning tree problems
Information Sciences: an International Journal
Information Sciences: an International Journal
A chaotic digital secure communication based on a modified gravitational search algorithm filter
Information Sciences: an International Journal
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
Towards applying associative classifier for genetic variants
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Around 1.8 million people in the UK have type 2 diabetes, representing about 90% of all diabetes cases in the UK. Genome wide association studies have recently implicated several new genes that are likely to be associated with this disease. However, common genetic variants so far identified only explain a small proportion of the heritability of type 2 diabetes. The interaction of two or more gene variants, may explain a further element of this heritability but full interaction analyses are currently highly computationally burdensome or infeasible. For this reason this study investigates an ant colony optimisation (ACO) approach for its ability to identify common gene variants associated with type 2 diabetes, including putative epistatic interactions. This study uses a dataset comprising 15,309 common (5% minor allele frequency) SNPs from chromosome 16, genotyped in 1924 type 2 diabetes cases and 2938 controls. This chromosome contains two previously determined associations, one of which is replicated in additional samples. Although no epistatic interactions have been previously reported on this dataset, we demonstrate that ACO can be used to discover single SNP and plausible epistatic associations from this dataset and is shown to be both accurate and computationally tractable on large, real datasets of SNPs with no expert knowledge included in the algorithm.