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AbsIDconvert: An absolute approach for converting genetic identifiers at different granularities

Overview of attention for article published in BMC Bioinformatics, September 2012
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2 X users

Citations

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30 Mendeley
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Title
AbsIDconvert: An absolute approach for converting genetic identifiers at different granularities
Published in
BMC Bioinformatics, September 2012
DOI 10.1186/1471-2105-13-229
Pubmed ID
Authors

Fahim Mohammad, Robert M Flight, Benjamin J Harrison, Jeffrey C Petruska, Eric C Rouchka

Abstract

High-throughput molecular biology techniques yield vast amounts of data, often by detecting small portions of ribonucleotides corresponding to specific identifiers. Existing bioinformatic methodologies categorize and compare these elements using inferred descriptive annotation given this sequence information irrespective of the fact that it may not be representative of the identifier as a whole.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Luxembourg 1 3%
Unknown 28 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 20%
Student > Bachelor 5 17%
Student > Ph. D. Student 5 17%
Student > Master 5 17%
Other 4 13%
Other 3 10%
Unknown 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 33%
Computer Science 6 20%
Biochemistry, Genetics and Molecular Biology 4 13%
Medicine and Dentistry 2 7%
Engineering 2 7%
Other 3 10%
Unknown 3 10%
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 06 March 2021.
All research outputs
#15,251,053
of 22,678,224 outputs
Outputs from BMC Bioinformatics
#5,361
of 7,249 outputs
Outputs of similar age
#106,258
of 168,582 outputs
Outputs of similar age from BMC Bioinformatics
#58
of 91 outputs
Altmetric has tracked 22,678,224 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,249 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 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 168,582 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 91 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.