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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Text-mining assisted regulatory annotation
|
|---|---|
| Published in |
Genome Biology, February 2008
|
| DOI | 10.1186/gb-2008-9-2-r31 |
| Pubmed ID | |
| Authors |
Stein Aerts, Maximilian Haeussler, Steven van Vooren, Obi L Griffith, Paco Hulpiau, Steven JM Jones, Stephen B Montgomery, Casey M Bergman, The Open Regulatory Annotation Consortium |
| Abstract |
Decoding transcriptional regulatory networks and the genomic cis-regulatory logic implemented in their control nodes is a fundamental challenge in genome biology. High-throughput computational and experimental analyses of regulatory networks and sequences rely heavily on positive control data from prior small-scale experiments, but the vast majority of previously discovered regulatory data remains locked in the biomedical literature. |
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 % |
|---|---|---|
| Unknown | 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 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 5% |
| United Kingdom | 4 | 5% |
| France | 2 | 3% |
| Germany | 2 | 3% |
| Brazil | 2 | 3% |
| Spain | 2 | 3% |
| Portugal | 1 | 1% |
| Australia | 1 | 1% |
| Mexico | 1 | 1% |
| Other | 0 | 0% |
| Unknown | 59 | 76% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 24 | 31% |
| Student > Ph. D. Student | 23 | 29% |
| Student > Master | 8 | 10% |
| Professor | 7 | 9% |
| Professor > Associate Professor | 4 | 5% |
| Other | 5 | 6% |
| Unknown | 7 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 35 | 45% |
| Computer Science | 17 | 22% |
| Biochemistry, Genetics and Molecular Biology | 8 | 10% |
| Engineering | 3 | 4% |
| Linguistics | 2 | 3% |
| Other | 7 | 9% |
| Unknown | 6 | 8% |
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 26 September 2019.
All research outputs
#20,655,488
of 25,371,288 outputs
Outputs from Genome Biology
#4,269
of 4,467 outputs
Outputs of similar age
#164,612
of 174,895 outputs
Outputs of similar age from Genome Biology
#31
of 32 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 1st percentile – i.e., 1% 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 174,895 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 2nd percentile – i.e., 2% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.