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X Demographics
Mendeley readers
Attention Score in Context
Title |
Mapping biological entities using the longest approximately common prefix method
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Published in |
BMC Bioinformatics, June 2014
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DOI | 10.1186/1471-2105-15-187 |
Pubmed ID | |
Authors |
Alex Rudniy, Min Song, James Geller |
Abstract |
The significant growth in the volume of electronic biomedical data in recent decades has pointed to the need for approximate string matching algorithms that can expedite tasks such as named entity recognition, duplicate detection, terminology integration, and spelling correction. The task of source integration in the Unified Medical Language System (UMLS) requires considerable expert effort despite the presence of various computational tools. This problem warrants the search for a new method for approximate string matching and its UMLS-based evaluation. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Norway | 1 | 33% |
Unknown | 2 | 67% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 67% |
Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 1 | 4% |
United States | 1 | 4% |
Korea, Republic of | 1 | 4% |
Unknown | 22 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 6 | 24% |
Researcher | 3 | 12% |
Student > Master | 3 | 12% |
Student > Doctoral Student | 2 | 8% |
Professor | 2 | 8% |
Other | 4 | 16% |
Unknown | 5 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 9 | 36% |
Agricultural and Biological Sciences | 3 | 12% |
Medicine and Dentistry | 3 | 12% |
Social Sciences | 2 | 8% |
Psychology | 1 | 4% |
Other | 1 | 4% |
Unknown | 6 | 24% |
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 14 June 2014.
All research outputs
#17,722,094
of 22,757,090 outputs
Outputs from BMC Bioinformatics
#5,926
of 7,272 outputs
Outputs of similar age
#155,538
of 228,190 outputs
Outputs of similar age from BMC Bioinformatics
#106
of 154 outputs
Altmetric has tracked 22,757,090 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,272 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 13th percentile – i.e., 13% 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 228,190 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 154 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.