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Open Biomedical Ontology-based Medline exploration

Overview of attention for article published in BMC Bioinformatics, May 2009
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
49 Mendeley
citeulike
7 CiteULike
connotea
1 Connotea
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Title
Open Biomedical Ontology-based Medline exploration
Published in
BMC Bioinformatics, May 2009
DOI 10.1186/1471-2105-10-s5-s6
Pubmed ID
Authors

Weijian Xuan, Manhong Dai, Barbara Mirel, Jean Song, Brian Athey, Stanley J Watson, Fan Meng

Abstract

Effective Medline database exploration is critical for the understanding of high throughput experimental results and the development of novel hypotheses about the mechanisms underlying the targeted biological processes. While existing solutions enhance Medline exploration through different approaches such as document clustering, network presentations of underlying conceptual relationships and the mapping of search results to MeSH and Gene Ontology trees, we believe the use of multiple ontologies from the Open Biomedical Ontology can greatly help researchers to explore literature from different perspectives as well as to quickly locate the most relevant Medline records for further investigation.

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 12%
United Kingdom 4 8%
Canada 1 2%
Switzerland 1 2%
New Zealand 1 2%
Spain 1 2%
Portugal 1 2%
Unknown 34 69%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 39%
Student > Ph. D. Student 9 18%
Student > Master 6 12%
Other 5 10%
Student > Bachelor 3 6%
Other 6 12%
Unknown 1 2%
Readers by discipline Count As %
Computer Science 16 33%
Agricultural and Biological Sciences 12 24%
Medicine and Dentistry 8 16%
Arts and Humanities 2 4%
Social Sciences 2 4%
Other 5 10%
Unknown 4 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 02 November 2010.
All research outputs
#816,068
of 3,631,524 outputs
Outputs from BMC Bioinformatics
#787
of 2,289 outputs
Outputs of similar age
#25,272
of 96,550 outputs
Outputs of similar age from BMC Bioinformatics
#40
of 127 outputs
Altmetric has tracked 3,631,524 research outputs across all sources so far. This one has received more attention than most of these and is in the 63rd percentile.
So far Altmetric has tracked 2,289 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 58% 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 96,550 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 72% of its contemporaries.
We're also able to compare this research output to 127 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.