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A benchmark server using high resolution protein structure data, and benchmark results for membrane helix predictions

Overview of attention for article published in BMC Bioinformatics, March 2013
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3 X users

Citations

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9 Dimensions

Readers on

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35 Mendeley
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1 CiteULike
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Title
A benchmark server using high resolution protein structure data, and benchmark results for membrane helix predictions
Published in
BMC Bioinformatics, March 2013
DOI 10.1186/1471-2105-14-111
Pubmed ID
Authors

Emma M Rath, Dominique Tessier, Alexander A Campbell, Hong Ching Lee, Tim Werner, Noeris K Salam, Lawrence K Lee, W Bret Church

Abstract

Helical membrane proteins are vital for the interaction of cells with their environment. Predicting the location of membrane helices in protein amino acid sequences provides substantial understanding of their structure and function and identifies membrane proteins in sequenced genomes. Currently there is no comprehensive benchmark tool for evaluating prediction methods, and there is no publication comparing all available prediction tools. Current benchmark literature is outdated, as recently determined membrane protein structures are not included. Current literature is also limited to global assessments, as specialised benchmarks for predicting specific classes of membrane proteins were not previously carried out.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 6%
Spain 1 3%
India 1 3%
Canada 1 3%
Unknown 30 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 31%
Researcher 6 17%
Student > Bachelor 3 9%
Student > Doctoral Student 3 9%
Student > Master 3 9%
Other 5 14%
Unknown 4 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 43%
Biochemistry, Genetics and Molecular Biology 6 17%
Computer Science 5 14%
Chemistry 3 9%
Physics and Astronomy 2 6%
Other 0 0%
Unknown 4 11%
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 27 March 2013.
All research outputs
#12,873,109
of 22,703,044 outputs
Outputs from BMC Bioinformatics
#3,782
of 7,254 outputs
Outputs of similar age
#101,263
of 197,838 outputs
Outputs of similar age from BMC Bioinformatics
#79
of 145 outputs
Altmetric has tracked 22,703,044 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 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 45th percentile – i.e., 45% 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 197,838 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 145 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.