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CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions

Overview of attention for article published in BMC Bioinformatics, May 2016
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Title
CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions
Published in
BMC Bioinformatics, May 2016
DOI 10.1186/s12859-016-1076-8
Pubmed ID
Authors

Junliang Shang, Yingxia Sun, Jin-Xing Liu, Junfeng Xia, Junying Zhang, Chun-Hou Zheng

Abstract

Detecting 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. We 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. Experiments 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/ .

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 47%
Student > Master 3 18%
Student > Doctoral Student 1 6%
Researcher 1 6%
Professor > Associate Professor 1 6%
Other 1 6%
Unknown 2 12%
Readers by discipline Count As %
Computer Science 5 29%
Agricultural and Biological Sciences 4 24%
Engineering 2 12%
Biochemistry, Genetics and Molecular Biology 1 6%
Neuroscience 1 6%
Other 1 6%
Unknown 3 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 May 2016.
All research outputs
#4,030,873
of 7,757,128 outputs
Outputs from BMC Bioinformatics
#2,383
of 3,457 outputs
Outputs of similar age
#146,417
of 269,559 outputs
Outputs of similar age from BMC Bioinformatics
#74
of 101 outputs
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