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Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology

Overview of attention for article published in BMC Bioinformatics, May 2012
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Mentioned by

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1 tweeter

Citations

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17 Dimensions

Readers on

mendeley
70 Mendeley
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3 CiteULike
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Title
Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology
Published in
BMC Bioinformatics, May 2012
DOI 10.1186/1471-2105-13-94
Pubmed ID
Authors

Steven M Hill, Richard M Neve, Nora Bayani, Wen-Lin Kuo, Safiyyah Ziyad, Paul T Spellman, Joe W Gray, Sach Mukherjee

Abstract

An important question in the analysis of biochemical data is that of identifying subsets of molecular variables that may jointly influence a biological response. Statistical variable selection methods have been widely used for this purpose. In many settings, it may be important to incorporate ancillary biological information concerning the variables of interest. Pathway and network maps are one example of a source of such information. However, although ancillary information is increasingly available, it is not always clear how it should be used nor how it should be weighted in relation to primary data.

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

Geographical breakdown

Country Count As %
United States 3 4%
United Kingdom 2 3%
Brazil 1 1%
Ghana 1 1%
Netherlands 1 1%
Canada 1 1%
Taiwan 1 1%
Switzerland 1 1%
Unknown 59 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 36%
Researcher 16 23%
Student > Master 9 13%
Professor > Associate Professor 5 7%
Professor 3 4%
Other 10 14%
Unknown 2 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 33%
Computer Science 10 14%
Engineering 7 10%
Biochemistry, Genetics and Molecular Biology 7 10%
Medicine and Dentistry 6 9%
Other 11 16%
Unknown 6 9%

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 May 2012.
All research outputs
#7,762,551
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#3,175
of 4,576 outputs
Outputs of similar age
#66,382
of 118,921 outputs
Outputs of similar age from BMC Bioinformatics
#33
of 47 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 118,921 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 47 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.