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X Demographics
Mendeley readers
Attention Score in Context
| 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. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 77 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% |
| Netherlands | 1 | 1% |
| Ghana | 1 | 1% |
| Switzerland | 1 | 1% |
| Brazil | 1 | 1% |
| Taiwan | 1 | 1% |
| Canada | 1 | 1% |
| Unknown | 66 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 26 | 34% |
| Researcher | 16 | 21% |
| Student > Master | 10 | 13% |
| Professor > Associate Professor | 6 | 8% |
| Other | 4 | 5% |
| Other | 10 | 13% |
| Unknown | 5 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 24 | 31% |
| Computer Science | 11 | 14% |
| Biochemistry, Genetics and Molecular Biology | 7 | 9% |
| Medicine and Dentistry | 7 | 9% |
| Engineering | 7 | 9% |
| Other | 12 | 16% |
| Unknown | 9 | 12% |
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
#15,243,549
of 22,665,794 outputs
Outputs from BMC Bioinformatics
#5,361
of 7,247 outputs
Outputs of similar age
#104,478
of 163,915 outputs
Outputs of similar age from BMC Bioinformatics
#74
of 107 outputs
Altmetric has tracked 22,665,794 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,247 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% 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 163,915 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 107 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.