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The genetic interacting landscape of 63 candidate genes in Major Depressive Disorder: an explorative study

Overview of attention for article published in BioData Mining, September 2014
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Title
The genetic interacting landscape of 63 candidate genes in Major Depressive Disorder: an explorative study
Published in
BioData Mining, September 2014
DOI 10.1186/1756-0381-7-19
Pubmed ID
Authors

Magnus Lekman, Ola Hössjer, Peter Andrews, Henrik Källberg, Daniel Uvehag, Dennis Charney, Husseini Manji, John A Rush, Francis J McMahon, Jason H Moore, Ingrid Kockum

Abstract

Genetic contributions to major depressive disorder (MDD) are thought to result from multiple genes interacting with each other. Different procedures have been proposed to detect such interactions. Which approach is best for explaining the risk of developing disease is unclear. This study sought to elucidate the genetic interaction landscape in candidate genes for MDD by conducting a SNP-SNP interaction analysis using an exhaustive search through 3,704 SNP-markers in 1,732 cases and 1,783 controls provided from the GAIN MDD study. We used three different methods to detect interactions, two logistic regressions models (multiplicative and additive) and one data mining and machine learning (MDR) approach.

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
Germany 1 2%
Unknown 50 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 25%
Student > Bachelor 7 13%
Researcher 6 12%
Professor > Associate Professor 4 8%
Student > Postgraduate 3 6%
Other 8 15%
Unknown 11 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 15%
Agricultural and Biological Sciences 8 15%
Psychology 5 10%
Neuroscience 5 10%
Computer Science 4 8%
Other 8 15%
Unknown 14 27%
Attention Score in Context

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 15 September 2014.
All research outputs
#18,378,085
of 22,763,032 outputs
Outputs from BioData Mining
#259
of 307 outputs
Outputs of similar age
#170,229
of 238,632 outputs
Outputs of similar age from BioData Mining
#6
of 6 outputs
Altmetric has tracked 22,763,032 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 307 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one is in the 6th percentile – i.e., 6% of its peers scored the same or lower than it.
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We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.