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Mendeley readers
Attention Score in Context
| Title |
GAGE: generally applicable gene set enrichment for pathway analysis
|
|---|---|
| Published in |
BMC Bioinformatics, May 2009
|
| DOI | 10.1186/1471-2105-10-161 |
| Pubmed ID | |
| Authors |
Weijun Luo, Michael S Friedman, Kerby Shedden, Kurt D Hankenson, Peter J Woolf |
| Abstract |
Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs. |
Mendeley readers
The data shown below were compiled from readership statistics for 1,041 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 17 | 2% |
| United Kingdom | 14 | 1% |
| Germany | 6 | <1% |
| Sweden | 3 | <1% |
| Netherlands | 2 | <1% |
| Portugal | 2 | <1% |
| Korea, Republic of | 2 | <1% |
| Italy | 2 | <1% |
| Russia | 2 | <1% |
| Other | 13 | 1% |
| Unknown | 978 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 268 | 26% |
| Researcher | 232 | 22% |
| Student > Master | 116 | 11% |
| Student > Doctoral Student | 65 | 6% |
| Student > Bachelor | 61 | 6% |
| Other | 145 | 14% |
| Unknown | 154 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 334 | 32% |
| Biochemistry, Genetics and Molecular Biology | 215 | 21% |
| Medicine and Dentistry | 81 | 8% |
| Immunology and Microbiology | 51 | 5% |
| Computer Science | 44 | 4% |
| Other | 129 | 12% |
| Unknown | 187 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 20 July 2023.
All research outputs
#2,247,538
of 25,837,817 outputs
Outputs from BMC Bioinformatics
#512
of 7,763 outputs
Outputs of similar age
#7,535
of 125,276 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 39 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,763 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done particularly well, scoring higher than 93% 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 125,276 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.