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X Demographics
Mendeley readers
Attention Score in Context
| Title |
ErmineJ: Tool for functional analysis of gene expression data sets
|
|---|---|
| Published in |
BMC Bioinformatics, November 2005
|
| DOI | 10.1186/1471-2105-6-269 |
| Pubmed ID | |
| Authors |
Homin K Lee, William Braynen, Kiran Keshav, Paul Pavlidis |
| Abstract |
It is common for the results of a microarray study to be analyzed in the context of biologically-motivated groups of genes such as pathways or Gene Ontology categories. The most common method for such analysis uses the hypergeometric distribution (or a related technique) to look for "over-representation" of groups among genes selected as being differentially expressed or otherwise of interest based on a gene-by-gene analysis. However, this method suffers from some limitations, and biologist-friendly tools that implement alternatives have not been reported. |
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 % |
|---|---|---|
| Brazil | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 166 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 9 | 5% |
| France | 4 | 2% |
| Canada | 3 | 2% |
| Brazil | 2 | 1% |
| South Africa | 1 | <1% |
| Finland | 1 | <1% |
| Netherlands | 1 | <1% |
| Germany | 1 | <1% |
| United Kingdom | 1 | <1% |
| Other | 6 | 4% |
| Unknown | 137 | 83% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 50 | 30% |
| Student > Ph. D. Student | 36 | 22% |
| Student > Master | 18 | 11% |
| Professor > Associate Professor | 14 | 8% |
| Professor | 10 | 6% |
| Other | 29 | 17% |
| Unknown | 9 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 95 | 57% |
| Biochemistry, Genetics and Molecular Biology | 28 | 17% |
| Computer Science | 16 | 10% |
| Medicine and Dentistry | 7 | 4% |
| Engineering | 5 | 3% |
| Other | 5 | 3% |
| Unknown | 10 | 6% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 23 March 2016.
All research outputs
#3,943,346
of 22,743,667 outputs
Outputs from BMC Bioinformatics
#1,511
of 7,268 outputs
Outputs of similar age
#9,366
of 60,640 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 15 outputs
Altmetric has tracked 22,743,667 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,268 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 79% of its peers.
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 60,640 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.