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Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping

Overview of attention for article published in BMC Genetics, February 2013
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Article details
Title
Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping
Published in
BMC Genetics, February 2013
DOI 10.1186/1471-2156-14-5
Pubmed ID
Authors
Abstract

Complex binary traits are influenced by many factors including the main effects of many quantitative trait loci (QTLs), the epistatic effects involving more than one QTLs, environmental effects and the effects of gene-environment interactions. Although a number of QTL mapping methods for binary traits have been developed, there still lacks an efficient and powerful method that can handle both main and epistatic effects of a relatively large number of possible QTLs.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 2 5%
Turkey 1 3%
Japan 1 3%
Spain 1 3%
Unknown 34 87%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 11 28%
Researcher 6 15%
Student > Doctoral Student 4 10%
Student > Bachelor 4 10%
Professor > Associate Professor 4 10%
Other 3 8%
Unknown 7 18%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 9 23%
Mathematics 7 18%
Computer Science 4 10%
Biochemistry, Genetics and Molecular Biology 3 8%
Social Sciences 2 5%
Other 7 18%
Unknown 7 18%
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 February 2013.
All research outputs
#19,157,392
of 27,781,301 outputs
Outputs from BMC Genetics
#222
of 484 outputs
Outputs of similar age
#219,121
of 319,455 outputs
Outputs of similar age from BMC Genetics
#6
of 7 outputs
Altmetric has tracked 27,781,301 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 484 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 319,455 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one.