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AntEpiSeeker specifications


Unique identifier OMICS_10088
Name AntEpiSeeker
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data A comma-delimited file (by default, "data0.txt") which contains the case-control genotype data as the input for the program.
Operating system Unix/Linux, Windows
Programming languages C++
Computer skills Advanced
Version 1.0
Stability Stable
Maintained Yes


No version available


  • person_outline Romdhane Rekaya

Publication for AntEpiSeeker

AntEpiSeeker citations


Heterogeneity Analysis and Diagnosis of Complex Diseases Based on Deep Learning Method

Sci Rep
PMCID: 5906634
PMID: 29670206
DOI: 10.1038/s41598-018-24588-5

[…] ting to save running time. With using the exhaustive strategy, all of epistatic combinations have been tested, so that the power of association studies is relatively higher. Heuristic methods such as AntEpiSeeker and MACOED use prior knowledge or information retrieved by swarm intelligence to narrow down the combination space. The main limitation of heuristic methods is randomness. It means that t […]


Niche harmony search algorithm for detecting complex disease associated high order SNP combinations

Sci Rep
PMCID: 5599559
PMID: 28912584
DOI: 10.1038/s41598-017-11064-9

[…] In the CS, by dividing SNP sites into M groups according to correlation among SNPs, only k-way (k < = M) SNP combinations are selected out of the M groups. Ant colony optimization (ACO) is adopted in AntEpiSeeker and MACOED, where the former employs chi-square test(χ 2) score to evaluate association between SNP combinations and phenotype, while the latter adopts Bayesian based K2-score and logisti […]


HiSeeker: Detecting High Order SNP Interactions Based on Pairwise SNP Combinations

PMCID: 5485517
PMID: 28561745
DOI: 10.3390/genes8060153

[…] To assess the performance of HiSeeker, we perform extensive simulation experiments using six different disease models and compare its power with four representative approaches: AntEpiSeeker [], EDCF [], DCHE [] and TAMW []. We adopt the same measure of power proposed by Wan et al. [] as follows:(10)Power=SND where S is the number of datasets in which true interaction loci ar […]


FHSA SED: Two Locus Model Detection for Genome Wide Association Study with Harmony Search Algorithm

PLoS One
PMCID: 4807955
PMID: 27014873
DOI: 10.1371/journal.pone.0150669

[…] ion datasets with different type of disease models and compared its performance with two excellent intelligent optimization algorithms (MACOED, CSE). The MACOED and CSE algorithm have advantages over AntEpiSeeker, BEAM and BOOST on the detection of multi-locus disease models in terms of power, sensitivity (true positive rate: TPR) or specificity (SPC) (true negative rate: TNR). The Matlab source c […]


A survey about methods dedicated to epistasis detection

Front Genet
PMCID: 4564769
PMID: 26442103
DOI: 10.3389/fgene.2015.00285

[…] ne concentration along this path. Other ants will more likely follow that path showing increased pheromone concentrations, thereby creating a positive feedback to find the best path to food. In 2010, AntEpiSeeker algorithm (Wang et al., ) was derived from the generic ant colony optimization (Dorigo and Gambardella, ) (ACO) algorithm. AntEpiSeeker performs the search of multiple groups of SNPs asso […]


An Improved Opposition Based Learning Particle Swarm Optimization for the Detection of SNP SNP Interactions

Biomed Res Int
PMCID: 4509494
PMID: 26236727
DOI: 10.1155/2015/524821

[…] To demonstrate the validity of IOBLPSO, its detection power is evaluated by comparison with several typical SNP-SNP interaction detection methods, that is, BOOST [], AntEpiSeeker [], SNPRuler [], and TEAM []. These machine learning methods are recently proposed, claimed to facilitate large scale data sets, and their packages are online freely available []. Besides […]


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AntEpiSeeker institution(s)
Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA; Institute of Bioinformatics, University of Georgia, Athens, GA, USA; Department of Statistics, University of Georgia, Athens, GA, USA

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