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An effective sequence-alignment-free superpositioning of pairwise or multiple structures with missing data

Overview of attention for article published in Algorithms for Molecular Biology, June 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)

Mentioned by

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3 tweeters

Citations

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2 Dimensions

Readers on

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8 Mendeley
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Title
An effective sequence-alignment-free superpositioning of pairwise or multiple structures with missing data
Published in
Algorithms for Molecular Biology, June 2016
DOI 10.1186/s13015-016-0079-3
Pubmed ID
Authors

Jianbo Lu, Guoliang Xu, Shihua Zhang, Benzhuo Lu

Abstract

Superpositioning is an important problem in structural biology. Determining an optimal superposition requires a one-to-one correspondence between the atoms of two proteins structures. However, in practice, some atoms are missing from their original structures. Current superposition implementations address the missing data crudely by ignoring such atoms from their structures. In this paper, we propose an effective method for superpositioning pairwise and multiple structures without sequence alignment. It is a two-stage procedure including data reduction and data registration. Numerical experiments demonstrated that our method is effective and efficient. The code package of protein structure superposition method for addressing the cases with missing data is implemented by MATLAB, and it is freely available from: http://sourceforge.net/projects/pssm123/files/?source=navbar.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 38%
Researcher 2 25%
Unknown 3 38%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 25%
Nursing and Health Professions 1 13%
Computer Science 1 13%
Agricultural and Biological Sciences 1 13%
Unknown 3 38%

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 07 August 2016.
All research outputs
#6,456,352
of 11,298,646 outputs
Outputs from Algorithms for Molecular Biology
#77
of 177 outputs
Outputs of similar age
#123,887
of 270,476 outputs
Outputs of similar age from Algorithms for Molecular Biology
#6
of 8 outputs
Altmetric has tracked 11,298,646 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 177 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 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 270,476 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 51% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.