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Mendeley readers
Attention Score in Context
| Title |
Building a protein name dictionary from full text: a machine learning term extraction approach
|
|---|---|
| Published in |
BMC Bioinformatics, April 2005
|
| DOI | 10.1186/1471-2105-6-88 |
| Pubmed ID | |
| Authors |
Lei Shi, Fabien Campagne |
| Abstract |
The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature. |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 5% |
| Portugal | 1 | 2% |
| Brazil | 1 | 2% |
| France | 1 | 2% |
| Russia | 1 | 2% |
| United Kingdom | 1 | 2% |
| Unknown | 48 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 20% |
| Student > Ph. D. Student | 9 | 16% |
| Student > Doctoral Student | 5 | 9% |
| Professor > Associate Professor | 5 | 9% |
| Student > Master | 5 | 9% |
| Other | 11 | 20% |
| Unknown | 10 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 18 | 32% |
| Agricultural and Biological Sciences | 10 | 18% |
| Linguistics | 3 | 5% |
| Medicine and Dentistry | 3 | 5% |
| Arts and Humanities | 2 | 4% |
| Other | 9 | 16% |
| Unknown | 11 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 17 October 2013.
All research outputs
#4,696,781
of 22,789,566 outputs
Outputs from BMC Bioinformatics
#1,810
of 7,280 outputs
Outputs of similar age
#10,503
of 59,622 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 6 outputs
Altmetric has tracked 22,789,566 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,280 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 gotten more attention than average, scoring higher than 73% 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 59,622 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.