Ant Colony Optimization with Self-Evolving Parameter for Detecting Epistatic Interactions

Boxin Guan, Yuzhai Zhao, Yuan Li, Ying Yin

Abstract: The epistatic interactions of single nucleotide poly-morphisms (SNPs) are fundamentally important for understanding the genetic causes of complex diseases. Due to the intensive computational burden and the diversity of disease models, existing methods suffer from low detection power, high computational cost, and preferences for some types of disease models. To tackle these drawbacks, an ant colony optimization with self-evolving parameter (SEPACO) is proposed in the paper. In the proposed algorithm, the self-evolving parameter control (SEPC) strategy is used to select the best parameters of the algorithm during the running process. In this way, SEPACO can set different optimal parameters for different disease models, which leads to the enhancement of the detection ability of the algorithm. Furthermore, the probability distribution function and the pheromone evaporation formula are improved to adapt SEPACO to the detection of epistatic interactions. SEPACO is compared with other recent algorithms on a variety of simulated datasets and a real biological dataset. The experimental results show that our algorithm, compared to the other test algorithms, can improve the average detection power from no more than 32% up to 68%. Moreover, our algorithm uses less running time.

External IDs:dblp:conf/bibm/GuanZLY19