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.
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References in zbMATH (referenced in 2 articles )
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- Guan, Boxin; Zhao, Yuhai; Yin, Ying; Li, Yuan: A differential evolution based feature combination selection algorithm for high-dimensional data (2021)
- Yuan, Lin; Yuan, Chang-An; Huang, De-Shuang: FAACOSE: a fast adaptive ant colony optimization algorithm for detecting SNP epistasis (2017)