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A semantic web framework to integrate cancer omics data with biological knowledge

Overview of attention for article published in BMC Bioinformatics, January 2012
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
92 Mendeley
citeulike
2 CiteULike
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Title
A semantic web framework to integrate cancer omics data with biological knowledge
Published in
BMC Bioinformatics, January 2012
DOI 10.1186/1471-2105-13-s1-s10
Pubmed ID
Authors

Matthew E Holford, James P McCusker, Kei-Hoi Cheung, Michael Krauthammer

Abstract

The RDF triple provides a simple linguistic means of describing limitless types of information. Triples can be flexibly combined into a unified data source we call a semantic model. Semantic models open new possibilities for the integration of variegated biological data. We use Semantic Web technology to explicate high throughput clinical data in the context of fundamental biological knowledge. We have extended Corvus, a data warehouse which provides a uniform interface to various forms of Omics data, by providing a SPARQL endpoint. With the querying and reasoning tools made possible by the Semantic Web, we were able to explore quantitative semantic models retrieved from Corvus in the light of systematic biological knowledge.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
India 1 1%
Brazil 1 1%
Austria 1 1%
Canada 1 1%
Japan 1 1%
Germany 1 1%
Luxembourg 1 1%
Unknown 81 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 23%
Student > Master 15 16%
Student > Ph. D. Student 15 16%
Other 10 11%
Student > Postgraduate 5 5%
Other 17 18%
Unknown 9 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 33%
Computer Science 20 22%
Medicine and Dentistry 9 10%
Biochemistry, Genetics and Molecular Biology 7 8%
Social Sciences 3 3%
Other 11 12%
Unknown 12 13%

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 08 February 2016.
All research outputs
#6,432,497
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#2,256
of 4,576 outputs
Outputs of similar age
#80,728
of 231,275 outputs
Outputs of similar age from BMC Bioinformatics
#46
of 96 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 48th percentile – i.e., 48% 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 231,275 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 64% of its contemporaries.
We're also able to compare this research output to 96 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 50% of its contemporaries.