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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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Mentioned by

twitter
2 tweeters

Citations

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32 Dimensions

Readers on

mendeley
30 Mendeley
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1 CiteULike
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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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

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 6 20%
Japan 1 3%
United States 1 3%
France 1 3%
Unknown 21 70%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 33%
Researcher 7 23%
Student > Master 2 7%
Student > Doctoral Student 2 7%
Student > Bachelor 2 7%
Other 7 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 37%
Agricultural and Biological Sciences 8 27%
Computer Science 4 13%
Medicine and Dentistry 3 10%
Engineering 1 3%
Other 0 0%
Unknown 3 10%

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
#7,805,048
of 12,440,694 outputs
Outputs from BMC Bioinformatics
#3,182
of 4,617 outputs
Outputs of similar age
#77,478
of 144,148 outputs
Outputs of similar age from BMC Bioinformatics
#12
of 18 outputs
Altmetric has tracked 12,440,694 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,617 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 22nd percentile – i.e., 22% 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 144,148 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 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.