Grammatical Evolution Association Rule Mining to Detect Gene-Gene Interaction

An important goal of human genetics is to identify DNA sequence variations that increase or decrease specific disease susceptibility. Complex interactions among genes and environmental factors are known to play a role in common human disease etiology. Methods for association rule mining ( ARM) are highly successful; especially that they produce rules which are easily interpretable. This has made them widely used in various domains. During the different stages of the knowledge discovery process, several problems are faced. It turns out that, the search characteristics of Evolutionary Algorithms make them suited to solve this kind of problems. In this study, we introduce GEARM, a novel approach for discovering association rules using Grammatical Evolution. We present the approach and evaluate it on simulated data that represents epistasis models. We show that this method improves the performance of gene-gene interaction detection.