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PCDq: human protein complex database with quality index which summarizes different levels of evidences of protein complexes predicted from H-Invitational protein-protein interactions integrative…

Overview of attention for article published in BMC Systems Biology, December 2012
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
61 Dimensions

Readers on

mendeley
38 Mendeley
citeulike
2 CiteULike
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Title
PCDq: human protein complex database with quality index which summarizes different levels of evidences of protein complexes predicted from H-Invitational protein-protein interactions integrative dataset
Published in
BMC Systems Biology, December 2012
DOI 10.1186/1752-0509-6-s2-s7
Pubmed ID
Authors

Shingo Kikugawa, Kensaku Nishikata, Katsuhiko Murakami, Yoshiharu Sato, Mami Suzuki, Md Altaf-Ul-Amin, Shigehiko Kanaya, Tadashi Imanishi

Abstract

Proteins interact with other proteins or biomolecules in complexes to perform cellular functions. Existing protein-protein interaction (PPI) databases and protein complex databases for human proteins are not organized to provide protein complex information or facilitate the discovery of novel subunits. Data integration of PPIs focused specifically on protein complexes, subunits, and their functions. Predicted candidate complexes or subunits are also important for experimental biologists.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 3%
Korea, Republic of 1 3%
India 1 3%
Germany 1 3%
Unknown 34 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 29%
Student > Ph. D. Student 6 16%
Professor 5 13%
Student > Master 5 13%
Other 2 5%
Other 5 13%
Unknown 4 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 45%
Biochemistry, Genetics and Molecular Biology 9 24%
Computer Science 3 8%
Arts and Humanities 2 5%
Medicine and Dentistry 2 5%
Other 1 3%
Unknown 4 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 24 January 2013.
All research outputs
#1,750,826
of 3,685,110 outputs
Outputs from BMC Systems Biology
#196
of 597 outputs
Outputs of similar age
#116,647
of 278,044 outputs
Outputs of similar age from BMC Systems Biology
#12
of 46 outputs
Altmetric has tracked 3,685,110 research outputs across all sources so far. This one has received more attention than most of these and is in the 50th percentile.
So far Altmetric has tracked 597 research outputs from this source. They receive a mean Attention Score of 2.7. This one has gotten more attention than average, scoring higher than 63% 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 278,044 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 55% of its contemporaries.
We're also able to compare this research output to 46 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 69% of its contemporaries.