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A domain-centric solution to functional genomics via dcGO Predictor

Overview of attention for article published in BMC Bioinformatics, February 2013
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Title
A domain-centric solution to functional genomics via dcGO Predictor
Published in
BMC Bioinformatics, February 2013
DOI 10.1186/1471-2105-14-s3-s9
Pubmed ID
Authors

Hai Fang, Julian Gough

Abstract

Computational/manual annotations of protein functions are one of the first routes to making sense of a newly sequenced genome. Protein domain predictions form an essential part of this annotation process. This is due to the natural modularity of proteins with domains as structural, evolutionary and functional units. Sometimes two, three, or more adjacent domains (called supra-domains) are the operational unit responsible for a function, e.g. via a binding site at the interface. These supra-domains have contributed to functional diversification in higher organisms. Traditionally functional ontologies have been applied to individual proteins, rather than families of related domains and supra-domains. We expect, however, to some extent functional signals can be carried by protein domains and supra-domains, and consequently used in function prediction and functional genomics.

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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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 6 16%
Japan 1 3%
France 1 3%
Unknown 29 78%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 27%
Researcher 8 22%
Professor 3 8%
Student > Master 3 8%
Student > Bachelor 2 5%
Other 8 22%
Unknown 3 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 32%
Agricultural and Biological Sciences 9 24%
Computer Science 6 16%
Medicine and Dentistry 3 8%
Engineering 2 5%
Other 0 0%
Unknown 5 14%
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 25 April 2013.
All research outputs
#15,270,698
of 22,708,120 outputs
Outputs from BMC Bioinformatics
#5,361
of 7,256 outputs
Outputs of similar age
#121,279
of 192,985 outputs
Outputs of similar age from BMC Bioinformatics
#119
of 159 outputs
Altmetric has tracked 22,708,120 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 7,256 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 192,985 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 159 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.