| Title |
Semantically linking molecular entities in literature through entity relationships
|
|---|---|
| Published in |
BMC Bioinformatics, June 2012
|
| DOI | 10.1186/1471-2105-13-s11-s6 |
| Pubmed ID | |
| Authors |
Sofie Van Landeghem, Jari Björne, Thomas Abeel, Bernard De Baets, Tapio Salakoski, Yves Van de Peer |
| Abstract |
Text mining tools have gained popularity to process the vast amount of available research articles in the biomedical literature. It is crucial that such tools extract information with a sufficient level of detail to be applicable in real life scenarios. Studies of mining non-causal molecular relations attribute to this goal by formally identifying the relations between genes, promoters, complexes and various other molecular entities found in text. More importantly, these studies help to enhance integration of text mining results with database facts. |
X Demographics
The data shown below were collected from the profiles of 2 X users 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 | 50% |
| United Kingdom | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 5% |
| Mexico | 1 | 3% |
| Germany | 1 | 3% |
| France | 1 | 3% |
| Unknown | 35 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 28% |
| Researcher | 7 | 18% |
| Student > Doctoral Student | 4 | 10% |
| Other | 4 | 10% |
| Student > Master | 4 | 10% |
| Other | 6 | 15% |
| Unknown | 4 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 21 | 53% |
| Agricultural and Biological Sciences | 9 | 23% |
| Biochemistry, Genetics and Molecular Biology | 2 | 5% |
| Medicine and Dentistry | 2 | 5% |
| Decision Sciences | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 4 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 28 August 2012.
All research outputs
#14,147,730
of 22,671,366 outputs
Outputs from BMC Bioinformatics
#4,713
of 7,247 outputs
Outputs of similar age
#96,465
of 164,435 outputs
Outputs of similar age from BMC Bioinformatics
#60
of 98 outputs
Altmetric has tracked 22,671,366 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% 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 30th percentile – i.e., 30% 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 164,435 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 98 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.