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Mouse obesity network reconstruction with a variational Bayes algorithm to employ aggressive false positive control

Overview of attention for article published in BMC Bioinformatics, April 2012
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1 tweeter

Citations

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23 Dimensions

Readers on

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55 Mendeley
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2 CiteULike
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Title
Mouse obesity network reconstruction with a variational Bayes algorithm to employ aggressive false positive control
Published in
BMC Bioinformatics, April 2012
DOI 10.1186/1471-2105-13-53
Pubmed ID
Authors

Benjamin A Logsdon, Gabriel E Hoffman, Jason G Mezey

Abstract

We propose a novel variational Bayes network reconstruction algorithm to extract the most relevant disease factors from high-throughput genomic data-sets. Our algorithm is the only scalable method for regularized network recovery that employs Bayesian model averaging and that can internally estimate an appropriate level of sparsity to ensure few false positives enter the model without the need for cross-validation or a model selection criterion. We use our algorithm to characterize the effect of genetic markers and liver gene expression traits on mouse obesity related phenotypes, including weight, cholesterol, glucose, and free fatty acid levels, in an experiment previously used for discovery and validation of network connections: an F2 intercross between the C57BL/6 J and C3H/HeJ mouse strains, where apolipoprotein E is null on the background.

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 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Brazil 1 2%
United Kingdom 1 2%
Sweden 1 2%
Taiwan 1 2%
Poland 1 2%
Unknown 48 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 33%
Researcher 11 20%
Professor > Associate Professor 7 13%
Student > Master 5 9%
Student > Bachelor 2 4%
Other 4 7%
Unknown 8 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 45%
Computer Science 11 20%
Biochemistry, Genetics and Molecular Biology 3 5%
Engineering 2 4%
Medicine and Dentistry 2 4%
Other 1 2%
Unknown 11 20%

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 14 April 2012.
All research outputs
#7,762,552
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#3,175
of 4,576 outputs
Outputs of similar age
#64,389
of 117,379 outputs
Outputs of similar age from BMC Bioinformatics
#29
of 48 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 21st percentile – i.e., 21% 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 117,379 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.