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Graph representation of high-dimensional alpha-helical membrane protein data

Overview of attention for article published in BioData Mining, December 2013
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1 Facebook page

Citations

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2 Dimensions

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5 Mendeley
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Title
Graph representation of high-dimensional alpha-helical membrane protein data
Published in
BioData Mining, December 2013
DOI 10.1186/1756-0381-6-21
Pubmed ID
Authors

Steffen Grunert, Dirk Labudde

Abstract

In genomics and proteomics, membrane protein analysis have shown that such analyses are very important to support the understanding of complex biological processes. In Genome-wide investigations of membrane proteins a large number of short, distinct sequence motifs has been revealed. Such motifs found so far support the understanding of the folded membrane protein in the membrane environment. They provide important information about functional or stabilizing properties. Recently several integrative approaches have been proposed to extract meaningful information out of the membrane environment. However, many information based approaches deliver results having deficits of visualisation outputs. Outgoing from high-throughput protein data analysis, these outputs play an important role in the evaluation of high-dimensional protein data, to establish a biological relationship and ultimately to provide useful information for research.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 60%
Researcher 1 20%
Student > Postgraduate 1 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 40%
Computer Science 2 40%
Biochemistry, Genetics and Molecular Biology 1 20%
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 04 December 2013.
All research outputs
#20,211,690
of 22,733,113 outputs
Outputs from BioData Mining
#288
of 307 outputs
Outputs of similar age
#267,642
of 307,218 outputs
Outputs of similar age from BioData Mining
#7
of 7 outputs
Altmetric has tracked 22,733,113 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 307 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one is in the 1st percentile – i.e., 1% 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 307,218 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one.