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Quantum coupled mutation finder: predicting functionally or structurally important sites in proteins using quantum Jensen-Shannon divergence and CUDA programming

Overview of attention for article published in BMC Bioinformatics, April 2014
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Citations

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Title
Quantum coupled mutation finder: predicting functionally or structurally important sites in proteins using quantum Jensen-Shannon divergence and CUDA programming
Published in
BMC Bioinformatics, April 2014
DOI 10.1186/1471-2105-15-96
Pubmed ID
Authors

Mehmet Gültas, Güncel Düzgün, Sebastian Herzog, Sven Joachim Jäger, Cornelia Meckbach, Edgar Wingender, Stephan Waack

Abstract

The identification of functionally or structurally important non-conserved residue sites in protein MSAs is an important challenge for understanding the structural basis and molecular mechanism of protein functions. Despite the rich literature on compensatory mutations as well as sequence conservation analysis for the detection of those important residues, previous methods often rely on classical information-theoretic measures. However, these measures usually do not take into account dis/similarities of amino acids which are likely to be crucial for those residues. In this study, we present a new method, the Quantum Coupled Mutation Finder (QCMF) that incorporates significant dis/similar amino acid pair signals in the prediction of functionally or structurally important sites.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 7%
Japan 1 4%
Philippines 1 4%
Unknown 24 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 36%
Student > Bachelor 4 14%
Researcher 4 14%
Professor > Associate Professor 3 11%
Student > Doctoral Student 1 4%
Other 4 14%
Unknown 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 57%
Computer Science 4 14%
Biochemistry, Genetics and Molecular Biology 2 7%
Physics and Astronomy 2 7%
Mathematics 1 4%
Other 1 4%
Unknown 2 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 03 April 2014.
All research outputs
#20,226,756
of 22,751,628 outputs
Outputs from BMC Bioinformatics
#6,841
of 7,268 outputs
Outputs of similar age
#192,713
of 225,518 outputs
Outputs of similar age from BMC Bioinformatics
#105
of 112 outputs
Altmetric has tracked 22,751,628 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,268 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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