ArticleBMC bioinformatics2016
CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- ACOCMPMI: An Ant Colony Optimization Algorithm Based on Composite Multiscale Part Mutual Information for Detecting Epistatic Interactions.Human mutation · 2025Article
- A multi-threaded approach to genotype pattern mining for detecting digenic disease genes.Frontiers in genetics · 2023Article
- Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models.PloS one · 2022Article
- FDHE-IW: A Fast Approach for Detecting High-Order Epistasis in Genome-Wide Case-Control Studies.Genes · 2018Article
- Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene-gene interactions.Scientific reports · 2017Article
- Niche harmony search algorithm for detecting complex disease associated high-order SNP combinations.Scientific reports · 2017Article
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6 authors.
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Abstract
backgroundDetecting and visualizing nonlinear interaction effects of single nucleotide polymorphisms (SNPs) or epistatic interactions are important topics in bioinformatics since they play an important role in unraveling the mystery of "missing heritability". However, related studies are almost limited to pairwise epistatic interactions due to their methodological and computational challenges.
resultsWe develop CINOEDV (Co-Information based N-Order Epistasis Detector and Visualizer) for the detection and visualization of epistatic interactions of their orders from 1 to n (n ≥ 2). CINOEDV is composed of two stages, namely, detecting stage and visualizing stage. In detecting stage, co-information based measures are employed to quantify association effects of n-order SNP combinations to the phenotype, and two types of search strategies are introduced to identify n-order epistatic interactions: an exhaustive search and a particle swarm optimization based search. In visualizing stage, all detected n-order epistatic interactions are used to construct a hypergraph, where a real vertex represents the main effect of a SNP and a virtual vertex denotes the interaction effect of an n-order epistatic interaction. By deeply analyzing the constructed hypergraph, some hidden clues for better understanding the underlying genetic architecture of complex diseases could be revealed.
conclusionsExperiments of CINOEDV and its comparison with existing state-of-the-art methods are performed on both simulation data sets and a real data set of age-related macular degeneration. Results demonstrate that CINOEDV is promising in detecting and visualizing n-order epistatic interactions. CINOEDV is implemented in R and is freely available from R CRAN: http://cran.r-project.org and https://sourceforge.net/projects/cinoedv/files/ .
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