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THESEUS: maximum likelihood superpositioning and analysis of macromolecular structures

Overview of attention for article published in Bioinformatics, June 2006
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3 Wikipedia pages

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108 Mendeley
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9 CiteULike
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Article details
Title
THESEUS: maximum likelihood superpositioning and analysis of macromolecular structures
Published in
Bioinformatics, June 2006
DOI 10.1093/bioinformatics/btl332
Pubmed ID
Authors
Abstract

THESEUS is a command line program for performing maximum likelihood (ML) superpositions and analysis of macromolecular structures. While conventional superpositioning methods use ordinary least-squares (LS) as the optimization criterion, ML superpositions provide substantially improved accuracy by down-weighting variable structural regions and by correcting for correlations among atoms. ML superpositioning is robust and insensitive to the specific atoms included in the analysis, and thus it does not require subjective pruning of selected variable atomic coordinates. Output includes both likelihood-based and frequentist statistics for accurate evaluation of the adequacy of a superposition and for reliable analysis of structural similarities and differences. THESEUS performs principal components analysis for analyzing the complex correlations found among atoms within a structural ensemble.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 108 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
United States 5 5%
United Kingdom 4 4%
Poland 1 <1%
Korea, Republic of 1 <1%
France 1 <1%
Spain 1 <1%
Germany 1 <1%
Unknown 94 87%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 37 34%
Student > Ph. D. Student 24 22%
Professor 8 7%
Professor > Associate Professor 8 7%
Student > Bachelor 6 6%
Other 14 13%
Unknown 11 10%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 39 36%
Biochemistry, Genetics and Molecular Biology 18 17%
Chemistry 17 16%
Computer Science 7 6%
Arts and Humanities 2 2%
Other 10 9%
Unknown 15 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 09 March 2023.
All research outputs
#8,533,995
of 25,371,288 outputs
Outputs from Bioinformatics
#6,956
of 12,808 outputs
Outputs of similar age
#30,042
of 88,182 outputs
Outputs of similar age from Bioinformatics
#29
of 79 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,808 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 34th percentile – i.e., 34% 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 88,182 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 79 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.