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Detection for gene-gene co-association via kernel canonical correlation analysis

Overview of attention for article published in BMC Genetics, October 2012
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Citations

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Title
Detection for gene-gene co-association via kernel canonical correlation analysis
Published in
BMC Genetics, October 2012
DOI 10.1186/1471-2156-13-83
Pubmed ID
Authors

Zhongshang Yuan, Qingsong Gao, Yungang He, Xiaoshuai Zhang, Fangyu Li, Jinghua Zhao, Fuzhong Xue

Abstract

Currently, most methods for detecting gene-gene interaction (GGI) in genomewide association studies (GWASs) are limited in their use of single nucleotide polymorphism (SNP) as the unit of association. One way to address this drawback is to consider higher level units such as genes or regions in the analysis. Earlier we proposed a statistic based on canonical correlations (CCU) as a gene-based method for detecting gene-gene co-association. However, it can only capture linear relationship and not nonlinear correlation between genes. We therefore proposed a counterpart (KCCU) based on kernel canonical correlation analysis (KCCA).

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 26%
Professor > Associate Professor 4 17%
Researcher 3 13%
Student > Master 2 9%
Student > Doctoral Student 1 4%
Other 1 4%
Unknown 6 26%
Readers by discipline Count As %
Computer Science 6 26%
Biochemistry, Genetics and Molecular Biology 3 13%
Agricultural and Biological Sciences 3 13%
Engineering 2 9%
Medicine and Dentistry 1 4%
Other 1 4%
Unknown 7 30%

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 21 October 2012.
All research outputs
#10,994,585
of 12,372,276 outputs
Outputs from BMC Genetics
#667
of 828 outputs
Outputs of similar age
#110,270
of 129,856 outputs
Outputs of similar age from BMC Genetics
#12
of 12 outputs
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