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TIN-X: target importance and novelty explorer

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

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

twitter
2 X users
wikipedia
1 Wikipedia page

Readers on

mendeley
37 Mendeley
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Article details
Title
TIN-X: target importance and novelty explorer
Published in
Bioinformatics, April 2017
DOI 10.1093/bioinformatics/btx200
Pubmed ID
Authors
Abstract

The increasing amount of peer-reviewed manuscripts requires the development of specific mining tools to facilitate the visual exploration of evidence linking diseases and proteins. We developed TIN-X, the Target Importance and Novelty eXplorer, to visualize the association between proteins and diseases, based on text mining data processed from scientific literature. In the current implementation, TIN-X supports exploration of data for G-protein coupled receptors, kinases, ion channels, and nuclear receptors. TIN-X supports browsing and navigating across proteins and diseases based on ontology classes, and displays a scatter plot with two proposed new bibliometric statistics: Importance and Novelty. http://www.newdrugtargets.org. [email protected]. Further information including the source code is available from the project repository at http://bitbucket.org/dccannon/tin-x/overview.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 10 27%
Student > Ph. D. Student 5 14%
Student > Master 3 8%
Librarian 2 5%
Other 2 5%
Other 4 11%
Unknown 11 30%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 7 19%
Biochemistry, Genetics and Molecular Biology 5 14%
Medicine and Dentistry 5 14%
Chemistry 4 11%
Computer Science 2 5%
Other 2 5%
Unknown 12 32%
Attention Score in Context

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 30 April 2026.
All research outputs
#9,522,396
of 32,550,730 outputs
Outputs from Bioinformatics
#6,639
of 14,125 outputs
Outputs of similar age
#113,590
of 344,939 outputs
Outputs of similar age from Bioinformatics
#67
of 147 outputs
Altmetric has tracked 32,550,730 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 14,125 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.1. This one has gotten more attention than average, scoring higher than 52% 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 344,939 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 66% of its contemporaries.
We're also able to compare this research output to 147 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 53% of its contemporaries.