epiACO - a method for identifying epistasis based on ant Colony optimization algorithm
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Title
epiACO - a method for identifying epistasis based on ant Colony optimization algorithm
Authors
Keywords
Epistatic interactions, Ant colony optimization, Bayesian network, Mutual information
Journal
BioData Mining
Volume 10, Issue 1, Pages -
Publisher
Springer Nature
Online
2017-07-06
DOI
10.1186/s13040-017-0143-7
References
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Related references
Note: Only part of the references are listed.- MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies
- (2014) Peng-Jie Jing et al. BIOINFORMATICS
- Gene, pathway and network frameworks to identify epistatic interactions of single nucleotide polymorphisms derived from GWAS data
- (2013) Yu Liu et al. BMC Systems Biology
- EpiMiner: A three-stage co-information based method for detecting and visualizing epistatic interactions
- (2013) Junliang Shang et al. DIGITAL SIGNAL PROCESSING
- EpiSIM: simulation of multiple epistasis, linkage disequilibrium patterns and haplotype blocks for genome-wide interaction analysis
- (2013) Junliang Shang et al. Genes & Genomics
- Genetic studies of complex human diseases: Characterizing SNP-disease associations using Bayesian networks
- (2012) Bing Han et al. BMC Systems Biology
- Incorporating heuristic information into ant colony optimization for epistasis detection
- (2012) Junliang Shang et al. Genes & Genomics
- An Effective Feature Selection Method via Mutual Information Estimation
- (2012) Jian-Bo Yang et al. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
- Learning genetic epistasis using Bayesian network scoring criteria
- (2011) Xia Jiang et al. BMC BIOINFORMATICS
- Performance analysis of novel methods for detecting epistasis
- (2011) Junliang Shang et al. BMC BIOINFORMATICS
- BOOST: A Fast Approach to Detecting Gene-Gene Interactions in Genome-wide Case-Control Studies
- (2010) Xiang Wan et al. AMERICAN JOURNAL OF HUMAN GENETICS
- TEAM: efficient two-locus epistasis tests in human genome-wide association study
- (2010) X. Zhang et al. BIOINFORMATICS
- Bioinformatics challenges for genome-wide association studies
- (2010) J. H. Moore et al. BIOINFORMATICS
- Identifying genetic interactions in genome-wide data using Bayesian networks
- (2010) Xia Jiang et al. GENETIC EPIDEMIOLOGY
- Ant colony optimisation to identify genetic variant association with type 2 diabetes
- (2010) Jacqueline Christmas et al. INFORMATION SCIENCES
- Dynamic causal modeling with genetic algorithms
- (2010) M. Pyka et al. JOURNAL OF NEUROSCIENCE METHODS
- Editorial survey: swarm intelligence for data mining
- (2010) David Martens et al. MACHINE LEARNING
- Predictive rule inference for epistatic interaction detection in genome-wide association studies
- (2009) Xiang Wan et al. BIOINFORMATICS
- A random forest approach to the detection of epistatic interactions in case-control studies
- (2009) Rui Jiang et al. BMC BIOINFORMATICS
- Failure to Replicate a Genetic Association May Provide Important Clues About Genetic Architecture
- (2009) Casey S. Greene et al. PLoS One
- Detecting SNP-expression associations: A comparison of mutual information and median test with standard statistical approaches
- (2009) S. Szymczak et al. STATISTICS IN MEDICINE
- Epistatic Module Detection for Case-Control Studies: A Bayesian Model with a Gibbs Sampling Strategy
- (2009) Wanwan Tang et al. PLoS Genetics
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