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MICAN : a protein structure alignment algorithm that can handle Multiple-chains, Inverse alignments, Cα only models, Alternative alignments, and Non-sequential alignments

Overview of attention for article published in BMC Bioinformatics, January 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

Mentioned by

twitter
7 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
56 Dimensions

Readers on

mendeley
57 Mendeley
citeulike
2 CiteULike
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Title
MICAN : a protein structure alignment algorithm that can handle Multiple-chains, Inverse alignments, Cα only models, Alternative alignments, and Non-sequential alignments
Published in
BMC Bioinformatics, January 2013
DOI 10.1186/1471-2105-14-24
Pubmed ID
Authors

Shintaro Minami, Kengo Sawada, George Chikenji

Abstract

Protein pairs that have the same secondary structure packing arrangement but have different topologies have attracted much attention in terms of both evolution and physical chemistry of protein structures. Further investigation of such protein relationships would give us a hint as to how proteins can change their fold in the course of evolution, as well as a insight into physico-chemical properties of secondary structure packing. For this purpose, highly accurate sequence order independent structure comparison methods are needed.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 4%
Switzerland 1 2%
Brazil 1 2%
Czechia 1 2%
United Kingdom 1 2%
Egypt 1 2%
Spain 1 2%
Japan 1 2%
United States 1 2%
Other 0 0%
Unknown 47 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 23%
Student > Ph. D. Student 12 21%
Student > Master 8 14%
Student > Bachelor 5 9%
Other 4 7%
Other 7 12%
Unknown 8 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 28%
Computer Science 15 26%
Agricultural and Biological Sciences 12 21%
Chemistry 5 9%
Mathematics 1 2%
Other 1 2%
Unknown 7 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 12 May 2022.
All research outputs
#5,064,886
of 24,037,100 outputs
Outputs from BMC Bioinformatics
#1,877
of 7,494 outputs
Outputs of similar age
#53,910
of 292,112 outputs
Outputs of similar age from BMC Bioinformatics
#36
of 137 outputs
Altmetric has tracked 24,037,100 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,494 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 73% 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 292,112 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 137 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 74% of its contemporaries.