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Exact score distribution computation for ontological similarity searches

Overview of attention for article published in BMC Bioinformatics, November 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
1 tweeter

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
41 Mendeley
citeulike
4 CiteULike

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Title
Exact score distribution computation for ontological similarity searches
Published in
BMC Bioinformatics, November 2011
DOI 10.1186/1471-2105-12-441
Pubmed ID
22078312
Authors

Marcel H Schulz, Sebastian Köhler, Sebastian Bauer, Peter N Robinson

Abstract

Semantic similarity searches in ontologies are an important component of many bioinformatic algorithms, e.g., finding functionally related proteins with the Gene Ontology or phenotypically similar diseases with the Human Phenotype Ontology (HPO). We have recently shown that the performance of semantic similarity searches can be improved by ranking results according to the probability of obtaining a given score at random rather than by the scores themselves. However, to date, there are no algorithms for computing the exact distribution of semantic similarity scores, which is necessary for computing the exact P-value of a given score.

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Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 7%
United States 2 5%
India 1 2%
Canada 1 2%
United Kingdom 1 2%
Unknown 33 80%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 39%
Student > Ph. D. Student 9 22%
Other 4 10%
Student > Master 4 10%
Student > Doctoral Student 2 5%
Other 4 10%
Unknown 2 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 37%
Computer Science 9 22%
Biochemistry, Genetics and Molecular Biology 6 15%
Medicine and Dentistry 3 7%
Psychology 1 2%
Other 3 7%
Unknown 4 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 29 July 2014.
All research outputs
#247,264
of 4,506,407 outputs
Outputs from BMC Bioinformatics
#142
of 2,646 outputs
Outputs of similar age
#3,609
of 73,869 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 96 outputs
Altmetric has tracked 4,506,407 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,646 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 94% 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 73,869 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% 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 done particularly well, scoring higher than 94% of its contemporaries.

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