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Quantifying and filtering knowledge generated by literature based discovery

Overview of attention for article published in BMC Bioinformatics, May 2017
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

twitter
5 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
22 Mendeley
citeulike
2 CiteULike
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Title
Quantifying and filtering knowledge generated by literature based discovery
Published in
BMC Bioinformatics, May 2017
DOI 10.1186/s12859-017-1641-9
Pubmed ID
Authors

Judita Preiss, Mark Stevenson

Abstract

Literature based discovery (LBD) automatically infers missed connections between concepts in literature. It is often assumed that LBD generates more information than can be reasonably examined. We present a detailed analysis of the quantity of hidden knowledge produced by an LBD system and the effect of various filtering approaches upon this. The investigation of filtering combined with single or multi-step linking term chains is carried out on all articles in PubMed. The evaluation is carried out using both replication of existing discoveries, which provides justification for multi-step linking chain knowledge in specific cases, and using timeslicing, which gives a large scale measure of performance. While the quantity of hidden knowledge generated by LBD can be vast, we demonstrate that (a) intelligent filtering can greatly reduce the number of hidden knowledge pairs generated, (b) for a specific term, the number of single step connections can be manageable, and

Twitter Demographics

The data shown below were collected from the profiles of 5 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 23%
Student > Ph. D. Student 4 18%
Professor 3 14%
Student > Bachelor 2 9%
Student > Doctoral Student 2 9%
Other 3 14%
Unknown 3 14%
Readers by discipline Count As %
Computer Science 8 36%
Biochemistry, Genetics and Molecular Biology 4 18%
Medicine and Dentistry 4 18%
Agricultural and Biological Sciences 2 9%
Unspecified 1 5%
Other 0 0%
Unknown 3 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 06 April 2022.
All research outputs
#5,698,171
of 22,163,477 outputs
Outputs from BMC Bioinformatics
#2,141
of 7,122 outputs
Outputs of similar age
#85,536
of 290,967 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 11 outputs
Altmetric has tracked 22,163,477 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 7,122 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 69% 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 290,967 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 70% of its contemporaries.
We're also able to compare this research output to 11 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 90% of its contemporaries.