ArticleBioData mining2017
epiACO - a method for identifying epistasis based on ant Colony optimization algorithm.
Article in BioData mining, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- iRUNNER: A Baseline Mutation Burden Regression for Identifying Gene Interaction Between Rare Variants for Diseases.Genomics, proteomics & bioinformatics · 2025Article
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- ACOCMPMI: An Ant Colony Optimization Algorithm Based on Composite Multiscale Part Mutual Information for Detecting Epistatic Interactions.Human mutation · 2025Article
- A Novel Detection Method for High-Order SNP Epistatic Interactions Based on Explicit-Encoding-Based Multitasking Harmony Search.Interdisciplinary sciences, computational life sciences · 2024Article
- SimHOEPI: A resampling simulator for generating single nucleotide polymorphism data with a high-order epistasis model.Quantitative biology (Beijing, China) · 2024Article
- SEEI: spherical evolution with feedback mechanism for identifying epistatic interactions.BMC genomics · 2024Article
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- Article
- Parent-offspring genotyped trios unravelling genomic regions with gametic and genotypic epistatic transmission bias on the cattle genome.Frontiers in genetics · 2023Article
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- Multi-Objective Artificial Bee Colony Algorithm Based on Scale-Free Network for Epistasis Detection.Genes · 2022Article
- A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases.Computational and mathematical methods in medicine · 2022Article
- Maximal Information Coefficient-Based Testing to Identify Epistasis in Case-Control Association Studies.Computational and mathematical methods in medicine · 2022Article
- Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models.PloS one · 2022Article
- Article
- Dynamic Programming for Resource Allocation in Multi-Allelic Trait Introgression.Frontiers in plant science · 2021Article
- Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network.BMC bioinformatics · 2019Article
- Self-Adjusting Ant Colony Optimization Based on Information Entropy for Detecting Epistatic Interactions.Genes · 2019Article
- FDHE-IW: A Fast Approach for Detecting High-Order Epistasis in Genome-Wide Case-Control Studies.Genes · 2018Article
- Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying epistasis or epistatic interactions, which refer to nonlinear interaction effects of single nucleotide polymorphisms (SNPs), is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Though many works have been done for identifying epistatic interactions, due to their methodological and computational challenges, the algorithmic development is still ongoing.
resultsIn this study, a method epiACO is proposed to identify epistatic interactions, which based on ant colony optimization algorithm. Highlights of epiACO are the introduced fitness function
conclusionsExperiments of epiACO and its comparison with other recent methods epiMODE, TEAM, BOOST, SNPRuler, AntEpiSeeker, AntMiner, MACOED, and IACO are performed on both simulation data sets and a real data set of age-related macular degeneration. Results show that epiACO is promising in identifying epistasis and might be an alternative to existing methods.
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