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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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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.

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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 readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 23%
Researcher 2 15%
Student > Bachelor 1 8%
Unknown 7 54%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 23%
Computer Science 2 15%
Nursing and Health Professions 1 8%
Agricultural and Biological Sciences 1 8%
Unknown 6 46%
Attention Score in Context

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
#14,717,488
of 23,577,761 outputs
Outputs from Algorithms for Molecular Biology
#109
of 251 outputs
Outputs of similar age
#204,663
of 355,335 outputs
Outputs of similar age from Algorithms for Molecular Biology
#4
of 8 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 251 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 49th percentile – i.e., 49% of its peers scored the same or lower than it.
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 355,335 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
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 4 of them.