Ant Colony Optimization with SelfEvolving Parameter for Detecting Epistatic Interactions The epistatic interactions of single nucleotide polymorphisms (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 selfevolving parameter (SEPACO) is proposed in the paper. In the proposed algorithm, the selfevolving 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.

BnBeeEpi: An Approach of Epistasis Mining Based on Artificial Bee Colony Algorithm Optimizing Bayesian Network Mining epistatic gene locus which influence complex disease has great research significance. Bayesian network (BN) has been widely used in many researches of epistasis mining. However, Bayesian network methods have disadvantages of being easily trapped into local optimum, low learning efficiency and not being able to handle largescale network. In this work, we propose an epistasis mining approach based on artificial bee colony algorithm optimizing Bayesian network (BnBeeEpi). We apply artificial bee colony algorithm into the heuristic search strategy of Bayesian network, and then use two kinds of BN scoring functions (BIC and MIT) to calculate the network fitness value to avoid overfitting and reduce false positive rate. Moreover, we introduce decomposable BIC scoring to solve the largescale network learning problem. Finally, we compare BnBeeEpi with current popular epistasis mining algorithms by using both simulated and real datasets. Experiment results show that ombFast has very short running time with its accuracy is as good as other methods, and BnBeeEpi has better F1score and lower false positive rate compared to others. Availability and implementation: codes and visualization platform are available at: http:106.14.132.202.