MACOED: A multi-objective ant colony optimization algorithm for SNP epistasis detection in Genome Wide Association Study
MACOED is a multi-objective ant colony optimization algorithm for detecting the genetic interactions. In the MACOED, we combine both the standard logistical regression and the Bayesian network methods, which are from the opposing schools of statistics. The combination of these two evaluation objectives is proved to be complementary to each other resulting in a performance of higher power and lower false positives. To solve the space and time complexity for large dimension problems, a memory-based multi-objective ant colony optimization algorithm is designed in MACOED, which is able to retentive the non-dominated solutions found in the past iterations.
Fig.1. The flow chart of MACOED.
Source code and executable file
C++ version: MACOED_C++.zip
Matlab version: MACOED_Matlab.zip
Exhaustive multi-objective method:
C++ version: Exhau_mop_C++.zip
Matlab version: Exhau_mop_Matlab.zip
DME model: DME model datasets.zip
DNME model: DNME model datasets.zip
Fig.3: Single-objective v.s. Multi-objective on DME models
Fig.4: Single-objective v.s. Multi-objective on DNME models
Fig.5: Performance comparison
Pengjie Jing: email@example.com
Hongbin Shen: firstname.lastname@example.org
Peng-Jie Jing, Hong-Bin Shen, MACOED: A multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies, Bioinformatics, 2014 (in press).