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Building Markov state models with solvent dynamics

Overview of attention for article published in BMC Bioinformatics, January 2013
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Citations

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Readers on

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60 Mendeley
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1 CiteULike
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Title
Building Markov state models with solvent dynamics
Published in
BMC Bioinformatics, January 2013
DOI 10.1186/1471-2105-14-s2-s8
Pubmed ID
Authors

Chen Gu, Huang-Wei Chang, Lutz Maibaum, Vijay S Pande, Gunnar E Carlsson, Leonidas J Guibas

Abstract

Markov state models have been widely used to study conformational changes of biological macromolecules. These models are built from short timescale simulations and then propagated to extract long timescale dynamics. However, the solvent information in molecular simulations are often ignored in current methods, because of the large number of solvent molecules in a system and the indistinguishability of solvent molecules upon their exchange.

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 7%
Italy 1 2%
Unknown 55 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 38%
Researcher 11 18%
Student > Postgraduate 6 10%
Student > Bachelor 4 7%
Professor 4 7%
Other 11 18%
Unknown 1 2%
Readers by discipline Count As %
Chemistry 19 32%
Agricultural and Biological Sciences 15 25%
Physics and Astronomy 9 15%
Biochemistry, Genetics and Molecular Biology 4 7%
Engineering 3 5%
Other 8 13%
Unknown 2 3%

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 24 February 2015.
All research outputs
#2,545,465
of 4,793,454 outputs
Outputs from BMC Bioinformatics
#1,952
of 2,795 outputs
Outputs of similar age
#79,826
of 143,186 outputs
Outputs of similar age from BMC Bioinformatics
#92
of 121 outputs
Altmetric has tracked 4,793,454 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,795 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 18th percentile – i.e., 18% 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 143,186 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 121 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.